Human-AI Co-Ideation in VR Experience Design: Evaluating the Dimensionality Framework (8Df) with LLMs (ChatGPT, Gemini, DeepSeek)

Human-AI Co-Ideation in VR Experience Design: Evaluating the Dimensionality Framework (8Df) with LLMs (ChatGPT, Gemini, DeepSeek) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Human-AI Co-Ideation in VR Experience Design: Evaluating the Dimensionality Framework (8Df) with LLMs (ChatGPT, Gemini, DeepSeek) Eugene Ch'ng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8274845/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Large Language Models (LLMs) are transforming creative fields; however, designers often lack structured frameworks that allow for creativity and theoretical grounding, particularly for complex media such as Virtual Reality (VR). This paper integrates the VR Dimensionality Framework (8Df) into a human-AI co-ideation system that builds on the theoretical foundations of VR, by structuring experience design across eight interrelated dimensions: spatial, sensory, placeness, temporal, cultural, social, cognitive, and psychological. Through a dialogic system implemented across three leading LLMs (ChatGPT, Gemini, and DeepSeek), this study evaluates their distinct approaches to co-creating a VR narrative. The findings reported here revealed fundamental differences in the styles of co-creation: ChatGPT operates as a "Preset-to-Prototype" model, providing actionable specifications for rapid implementation; Gemini functions as a "Facilitator", a catalyst for stakeholder alignment through guided inquiry; and DeepSeek offers a "Classification and Blended Approach" for creating grounded, principled designs. The evaluation also highlights variations in cultural data representation and suggests a practical workflow that matches each model's strengths to specific design phases and expertise levels. Humanities/Cultural and media studies Social science/Cultural and media studies Biological sciences/Psychology Social science/Psychology presence VR framework human-AI co-ideation AI co-creation Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The widespread adoption and use of large language models (LLMs) have transformed how designers generate, explore, relate and refine ideas. The availability of prompt engineering practices (Ekin, 2023 ; Giray, 2023 ; White et al., 2023 ), a set of hidden instructions that defines the persona, purpose, behaviour, principles, constraints, knowledge use and output formats in most LLM services, such as ChatGPT’s custom GPT, Google’s Gemini, etc., has enabled a persistent approach to governing the boundaries of how AI operates, making it modular, repeatable, and extending its domain-specific use. In fields that require ideation, as recent work on human-AI co-ideation (Wang et al., 2025 ) reveals, designers still lack frameworks that integrate embodied, experiential and contextual creativity. Conceptual and theoretical frameworks are important (Anfara Jr & Mertz, 2014 ; Grant & Osanloo, 2014 ; Sinclair, 2007 ). Within fields that require an educational background and professional practice experiences, such as those in cultural heritage. Frameworks are especially important, as demonstrated by the AI-assisted Context-Based Significance Assessment (CBSA) (Alef & Shafriri, 2025 ), which enables non-specialists to assess the significance of heritage sites. They provide the conceptual scaffolding through which complex creative phenomena can be observed, articulated and compared. In the context of this article, and especially in fields that require creative ideation and design, conceptual and theoretical frameworks provide the scaffolding that structures the imagination and bounds the abstraction of creativity. In the rapidly evolving field of AI and human-in-the-loop co-creation, frameworks establish a shared vocabulary that links abstract theory with design practice, enabling designers to move beyond anecdotal evidence toward a more systematic approach to design thinking. Without a guiding framework, AI-supported creativity risks being reduced to tool performance rather than situated development. This grounds creative experimentation in coherent epistemology. The Dimensionality Framework (Ch’ng, 2026a ) (8Df) extends this discourse by positioning the creative design process for VR experience not as a purely cognitive exchange between human and AI, but as a multi-dimensional synthesis of spatial, sensory, placeness, temporal, cultural, social, cognitive and psychological dynamics within the ideation phase of the design process that will lead to the design of an engaging VR experience. Developed from decades of immersive media research and development, the human-AI framework proposes that ideation in the design of VR experiences should be grounded in theoretical frameworks move beyond the sense of ‘being there’, by incorporating abstract dimensions afforded by immersive media, built upon the technical sensorimotor dimension (spatial, sensory), to include dials that modulates the experience from these dimensions – placeness, temporal, cultural, social, cognitive, and psychological. In this article, two questions are asked: RQ1. How do different large language models (ChatGPT, Gemini, DeepSeek) behave when guided by the same theoretical framework (Dimensionality Framework during the VR co-ideation process? RQ2. What qualitative differences emerge across models in terms of theoretical grounding, cultural precision, and implementability of VR design outputs? 2. Background Over the past decade, the accessibility and affordability of VR hardware and software have lowered the barriers to creating immersive media. This can be attributed to big-tech investments in the space (Alsop, 2025 ; BusinessInsider, 2025 ; Levy, 2024 ; Richter, 2025 ). Free, standardised 3D creation tools (e.g., Blender, RealityScans, Meshroom) and interoperable formats, along with Unity and Unreal Engine marketplaces, have democratised VR production for educators, independent creators, and small teams without specialised infrastructure. However, as sensorimotor fidelity is continually enhanced through research (see for example, Choi et al., 2025 ; Geng et al., 2025 ; Wong et al., 2017 ; Xiao et al., 2025 ), and VR devices and production tools become pervasive, the design of meaningful VR experiences remains a multistage, interdisciplinary process that demands expert knowledge across technical, artistic, psychological and narrative domains (Bucher, 2017 ). Designing a meaningful VR experience requires far more than assembling realistic virtual environments. It demands a solid theoretical foundation and the ability to orchestrate space, time, emotion, and interaction into a coherent whole. Although drag-and-drop assets make environment creation easy, most creators and even large teams struggle to integrate knowledge from psychology, media theory, culture, and narrative, often resulting in fragmented experiences; this gap between accessible tools and design synthesis underscores the need for structured, cross-disciplinary frameworks to guide VR creation. The human-AI dimensionality framework for human-in-the-loop ideation and co-creation of the VR experience responds to this gap. By breaking down the design of immersive experience into eight interrelated dimensions (spatial, sensory, placeness, temporal, cultural, social, cognitive and psychological), the 8D framework (Ch’ng, 2026b ) offers a systems-level approach for guiding small, non-domain-specific individuals and teams through the full creative process. It translates complex experiential goals that relate to how different medium properties (‘dials’) can be modulated to achieve a spectrum of effects (the effects spectrum), allowing designers to work with LLMs in balancing user experience across eight dimensions. The framework provides a structured approach to creative ideation, experience design, theoretical synthesis, VR experience evaluation, as well as a pedagogical tool for students. A core component of this proposed framework is the integration of Generative AI to guide and augment the creative process. To understand the foundation of this approach, it is essential to examine the recent and marked increase in the use of Generative AI (GenAI) in the broader ideation and design process. The use of GPTs for assisting in structured, design-thinking-based frameworks has become very popular. While earlier GPTs can reliably generate feasible and contextually appropriate design ideas, issues such as data biases and prompt sensitivity did not outperform crowdsourced human input in divergent thinking and analogical reach (Ma et al., 2023 ). Hallucination effects in generative AI that appear to be logical but are actually flawed, with fundamental inaccuracies, remain a challenge (Feuerriegel et al., 2024 ). While newer models will become better, strategies such as Retrieval Augmented Generation (RAG) and advanced prompts for mitigating against hallucinations persist (Bhattacharya, 2024 ). Other limitations include the differences between the oral narrative register of humans and that of AI. Maisto ( 2025 ) found that LLMs do not yet emulate the oral narrative register of collaborative storytelling, but create a hybrid linguistic mode that is syntactically complex and lexically rich but low in cohesion, empathy, and dialogic responsiveness. This need not be a concern in human-AI co-ideation processes. Wang et al. ( 2025 ) highlighted the central challenge of the lack of methodology in contemporary industrial design practice for co-ideating effectively with AI, formalising human-AI collaboration as a dialogic reflection grounded in design theory. Fotaris et al. ( 2023 ) introduced a framework used in tandem with ChatGPT to simplify and democratise the creation of Educational Escape Rooms (EERs), guiding educators through the iterative design stages through prompts. A study (Rodriguez & Joyce, 2025 ) demonstrated that custom GPT-based support improves students’ prompting proficiency, architectural vocabulary, and confidence in AI-mediated design, and that personas further promote reflective, interdisciplinary, and context-aware communication important for future architects. Huang and Saksono (Huang & Saksono, n.d.) proposed a study on the use of AI as a digital storytelling assistant, helping patients and caregivers create health narratives using LLMs for narrative medicine practices, assisting those with limited literacy, linguistic, or emotional expression skills. Dervazi (Dervazi Carvalho, 2024 ) contributed a methodological and technical blueprint for designing generative storytelling systems, demonstrating that AI supports creativity rather than replacing it. This marked a shift towards guided, explainable, and educationally aligned co-creation frameworks. The collection of articles in Yu et al. (Yu et al., 2025 ) provided a high-level synthesis of the AI-design convergence, which frames the current development of human-AI co-creation as a paradigm shift in reshaping the creative, industrial, and engineering practices across sectors, outlining technological, methodological, and cognitive transformation of design under AI influence. Researchers integrating LLMs into the human-in-the-loop design process agree that AI lowers entry barriers into domain-specific expertise while raising coordination and performance. While reliability, bias, and integration remain a challenge, the accessibility and continual production of better models serve as a mitigation. AI is framed across the literature as, a tool for co-authorship, producing concepts and contents on demand a reflective agent, providing novelty and quality through dialogic reflection a facilitator that supports the user’s intent a mediator that provides content and preserves coherence The diverse approaches in the literature reflect the different stages of the human-AI collaboration research. These approaches combine performance measurement, interaction design, and discourse analysis, providing evidence on how AI’s ability to support, reflect, and control structures can effectively support human-AI co-creation. Across diverse disciplines where AI acts as a co-creator, research consistently shows that structured collaboration, facilitated through reflective dialogue and modular microtasks, is essential for effective human-AI work. When these structures balance AI’s generative strengths with human intentionality, AI enhances reflection and feasibility while humans contribute novelty, emotional depth, and contextual sensitivity, resulting in more coherent and authentic creative outcomes. These researchers argue for a robust foundation to support a framework that guides the human-AI co-creation process. AI can augment creativity when situated within structured processes, while providing a sandbox for human intention, novelty, and contextual sensemaking. 3. Methodology This project implements a human-AI co-creation system using background instructions as GPT system prompt, integrating the Dimensionality Framework (8Df) (Ch’ng, 2026b ) as a knowledge base that aims to provide a dialogic step-by-step guide for individuals and small teams in designing VR experiences. 3.1 LLMs and Models LLM Models have continually improved through the use of newer architectures, increased numbers of parameters, improved algorithms, and high-quality data (Saracco, 2024 ; Xu et al., 2025 ; Zorpette, 2025 ). The present research evaluates three LLMs. The system prompt is embedded in the background via the following approaches: Open AI’s ChatGPT - Custom GPT with GPT-4o and GPT-5 Google’s Gemini - Gems with Gemini 2.5 DeepSeek R1, copying and pasting the entire system prompt onto the chat window Prompt engineering was conducted across these three models in a cyclical process that involved approximately 10–15 code iterations to achieve consistent outputs across all three models. 3.2 Persona Style and Format The persona acts as a professional VR experience designer and Socratic partner who applies the Dimensionality Framework, guiding the user step-by-step through to a final synthesis. The solutions provided are purposeful, context-grounded, and explicitly aligned with value creation in the user’s domain. 3.3 Purpose and Core Principles of AI The process prioritises clarity and practical utility, generating concise deliverables that are immediately actionable: Value as Priority : All design decisions should be justified in relation to the user’s domain-specific objective, such as education, professional training, empathy building, or entertainment. Every prompt element demonstrably advances that goal. Balance and trade-offs : Modulation choices must be made explicit and considered in relation to one another across dimensions; for example, introducing temporal non-linearity should be weighed against its potential to increase cognitive load, with rationale provided for the selected balance. Human-in-the-loop checkpoints : The workflow should incorporate a deliberate pause after each dimension to solicit explicit consent from the user, ensuring that users retain meaningful control over the scope and direction before proceeding. Knowledge-base : Definitions and explanations of Dimensionality Framework (DF) constructs should draw directly from the embedded knowledge base; when asked to define the 8Df, the response should name and align with the source article. Flow resumption : When the process is temporarily diverted, e.g., to address micro tasks such as side questions or present tables, the system should return to the last active step and continue from that point, preserving coherence and continuity of the design process. 3.4 Human-AI Workflow The human-AI workflow is designed as steps in the following order: Welcome the user with a brief purpose of the D8f based on the knowledge base, and mention the article if a definition is asked. Establish the value of the VR experience by asking, “What is the primary goal of this VR experience (education, entertainment, empathy, training, other)?” Scenes: Ask the user to provide the number of scenes and a one-line description of the purpose for each scene. If no scenes were given, propose 3–5 scenes and explain how they balance information flow. Confirm readiness to begin with the first Dimension [DM-0], the Spatial Dimension. For each dimension, the prompt asks whether the associated dimension table from the framework should be provided and whether brief, creative examples should be included. Other policies include utilising the ‘dials’ [DL], considering modulation [MOD] and the effects spectrum [EFF], as well as the information flow across the dimensions. At the end of each step, users are asked if there are any changes to any of the dials/modulation/effects before proceeding to the next dimension. 3.5 Constraints The design process must respect the user’s constraints, ethical standards, and cultural sensitivities, ensuring that all interactions and representations are appropriate for the intended audience and context. Feasibility issues should be identified early with transparent options and trade-offs, and any uncertainty about the Dimensionality Framework should prompt clarification or clearly stated assumptions rather than unsupported claims. 3.6 Final Synthesis Upon explicit approval to proceed to DM-8, the Final Synthesis is presented as a structured package comprising nine components, delivered in the following order. The Experience Goals Statement articulates the intended outcomes and value propositions of the VR project. Core Concept summarises the central idea that integrates narrative, interaction, and system constraints. The Project Timeline outlines the phased development, including hardware/software foundation, scene design, 3D modelling, interaction design, coding, integration, and developer testing, to ensure clear responsibilities and milestones. Dimension Value Alignment Table specifies, for each dimension, the core design choice, how that choice serves the project’s stated value, and any relevant conditional notes (“IF” cases) that qualify implementation. Dimension Modulation and Effects Spectrum Table records, for the Spatial, Sensory, Placeness, Temporal, Cultural, Social, Cognitive, and Psychological dimensions, the selected modulations and the expected range of user-facing effects. VR Experience Design (VRED) Document Table provides concise entries for Objectives, Audience, Platforms, Mechanics, Content, Accessibility, Safety/Comfort, and Metrics, thereby translating design intent into implementation-ready specifications. Story Mapping (Agile) Table organises Epics into scene-scoped User Stories with explicit Acceptance Criteria, enabling traceability from goals to testable outcomes. Final Statement concisely justifies the integrated design in relation to the value proposition and acknowledges key trade-offs made across dimensions. The final synthesis concludes with a Generate Storyboard prompt that asks whether to produce a visual layout map or a comic-style storyboard of the scenes for downstream communication and production. The visualisation depended on the ability of the model in use. 4. Evaluation The models being evaluated were ChatGPT (GPT-4o and GPT-5), Gemini (2.5), and DeepSeek (R1). The experimental Steps are listed below. Each step produces an output which is recorded in the Appendices. Contextual details were asked between the three models with the phrase ‘What are the main foods in Quanzhou?’ and ‘What are the main foods in London?’ Entry points were triggered with the ‘start’ keyword. Any initial triggers will lead the AI to output the initial steps, requesting the purpose of the VR experience design and scene proposals. After scene insertion, the keywords from DM-0 to DM-9 were typed in, allowing the AI to traverse through all the steps programmed in the system prompt. Finally, a ‘summary visual diagram’ and the ‘storyboard’ were asked for. 4.1 Contextual Details Between Models In the process, it was discovered that different models output different ranges of cultural contextual details, all of which depend on the availability and quality of the data. This is an important awareness, because the framework structures the theory-grounded co-ideation; however, it is the details that fill in the structure. Table # and # is a simple demonstration of the culturally contextual details that each model is able to output (Tables 1 and 2 ). Note that the maximum items produced for ‘London’ is ChatGPT at 46.67%, and when ‘Quanzhou’, a Chinese city, is asked, it produces only 28.57% of the total. Conversely, DeepSeek produces 50% for ‘Quanzhou’ and 35.55% for ‘London’. There are 5 overlaps between ChatGPT and DeepSeek for ‘Quanzhou’, and 7 for ‘London’. DeepSeek produced 9 additional items for ‘Quanzhou’, and ChatGPT produced 13 for ‘London’. More can be derived from each model with enhanced prompts, but the idea is that each model has cultural contexts, either from the ability to conduct Web searches or from what has been embedded in the model itself. Table 1 The output of “What are the main foods in Quanzhou” across ChatGPT, Gemini and DeepSeek. Overlaps in bold. ChatGPT 5 (28.57%) Gemini 2.5 (21.43%) DeepSeek R1 (50%) 1 Mianxianhu Fried Oyster Omelette Quanzhou Beef Briskets 2 Peanut soup Frozen Siphon Worm Jelly Mian Xian Hu 3 Runbing (spring roll crêpe) Fine Noodle Soup Taiwanese Beef Noodle Soup 4 Oyster omelette Pyramid-Shaped Rice Dumpling Oyster Omelette 5 Tusun-dong (sea-worm aspic) Ginger Duck Fried Dough Wraps (Vinegar Marinated Pork) 6 Fish rolls Quanzhou Beef Steak Runbing (Fresh Spring Roll) 7 Cu-rou (vinegar pork) Stuffed Tofu Puffs 8 Roasted-meat zongzi Steamed Fish with Pickled Mustard Greens 9 Stir-fried Razor Clams 10 Peanut Soup 11 Zongzi (Savory Sticky Rice Dumpling) 12 Meat Dumplings (Pork/Fish) 13 Sweet Taro Puree 14 Various "Gui" Rice Cakes Table 2 The output of “What are the main foods in London” across ChatGPT, Gemini and DeepSeek. Overlaps in bold. ChatGPT 5 (46.67%) Gemini 2.5 (17.78%) DeepSeek R1 (35.55%) 1 Full English breakfast Fish and Chips Fish & Chips 2 Fish & chips Sunday Roast Sunday Roast 3 Pie & mash (with liquor) Full English Breakfast Full English Breakfast 4 Bangers & mash Pie and Mash Pie & Mash 5 Sunday roast Bangers and Mash Afternoon Tea 6 Scotch egg Chicken Tikka Masala Indian Curry 7 Beef Wellington Afternoon Tea Caribbean Jerk Chicken 8 Chicken tikka masala / curries Puddings (e.g., Sticky Toffee) Turkish Ocakbasi 9 Jerk chicken Chinese Dim Sum & Roasts 10 Döner/shish kebab Gastropub Food 11 Salt beef (beigel) Street Food Markets 12 Dim sum Modern British Cuisine 13 Falafel wrap The "Greasy Spoon" Cafe 14 Pho/ramen A Pint in a Pub 15 Sourdough pizza by the slice Gin & Tonic 16 Bao A Cuppa (Tea) 17 Halloumi fries 18 Pies & pasties 19 Scones with jam & clotted cream 20 Sticky toffee pudding 21 Eton mess / Victoria sponge 4.2 Entry Point Using the same entry prompt ‘start’ across the three LLMs demonstrates similarities across all three models (Table 3 ). Gemini seems to think longer (16 seconds) and producing 74 words that communicate the exact meaning needed to start the process, compared to 2 seconds (84 words) for ChatGPT, and 9 seconds (199 words) for DeepSeek. Table 3 Comparisons of Entry Point messages. ChatGPT 5-Thinking Gemini 2.5 DeepSeek R1 (Deep Think) Hi! I’m your VR design partner using the Dimensionality Framework (DF) to turn your goals into an actionable VR experience. DF balances eight dimensions—Spatial, Sensory, Placeness, Temporal, Cultural, Social, Cognitive, Psychological—to align design choices with user value, grounded in the DF knowledge base and the “Beyond Presence” perspective. What is the primary goal (education, entertainment, empathy, training, or other)? How many scenes and what’s each about? If you prefer, I can suggest 3–5 starter scenes. Ready to begin with DM-0 Spatial ? Welcome. I am a VR Experience Designer using the Dimensionality Framework to co-create purposeful, balanced experiences across eight key dimensions, grounded in the DF knowledge base from "Beyond Presence." What is the primary goal for this VR experience (e.g., education, training, empathy, entertainment)? How many scenes do you envision, and what is the one-line purpose of each? If you prefer, I can suggest 3–5 starter scenes that balance information flow. Ready to begin with DM-0 Spatial ? Hello! I'm your Socratic design partner, here to help you co-design a powerful VR experience. Using the Dimensionality Framework (DF), we'll balance eight key dimensions to transform your goal into an actionable, value-driven design. This structured approach, grounded in the principles of the "Beyond Presence" article, ensures every choice we make is purposeful and effective. First, what is the primary goal of this VR experience? Is it for education, entertainment, empathy, training, or something else? Next, how many scenes do you envision, and what is the one-line purpose for each? If you're not sure, I can suggest 3–5 starter scenes to help structure the experience. Once we have that foundation, are you ready to begin with DM-0 Spatial ? 4.3 Scene Suggestions Testing the prompt ‘My VR project tells the story of a migrant who spends two decades building a life in the hustle of London, only to find that their true journey ends with their emotional return to the ancient city of Quanzhou. The core purpose of the experience is to teach the complex nature of 'home' and 'identity' through personal narrative. Suggest 5 scenes.’ The first prompt is followed by a second prompt that inserts another scene into the experience, ‘Include an early scene depicting London’s cuisine, the dishes you have enjoyed over the past twenty years, recollections of your grandma’s kitchen, and the street food of Quanzhou as you prepare to return as a migrant. List all the food available in each city.’ See Tables 4 – 9 for details. 4.3.1 ChatGPT Scene Co-Creation Table 4 The scene co-creation with ChatGPT took 16 seconds, generating 171 words. Beautiful brief—thank you. Your core value reads as education + empathy : teaching the layered meaning of “home” and “identity” through a lived narrative. Here are 5 scenes that balance information flow (arousal, tempo, cognitive load) and build to the Quanzhou return: 1. Arrivals: Thresholds in Transit — Heathrow to a cramped bedsit; bureaucratic maze and night bus glide. Purpose: dislocation + aspiration primer. 2. Hustle Years: City as Treadmill — Time-compressed decade of gigs, markets, side-hustles; maps morph with commute lines. Purpose: labor, resilience, belonging-seeking. 3. Patchwork Homes — Karaoke basement, temple courtyard, late-night kitchens, WhatsApp calls; London↔Quanzhou audio ghosts. Purpose: plurilocal “home,” cultural micro-anchors. 4. Fracture & Superposition — A crisis (visa/parental health) overlays Quanzhou streets onto London in real-time. Purpose: identity tension, memory-place blending. 5. Quanzhou Return: Slow Time — Stone alleys, incense, sea breeze; a door-threshold choice reframes home as a network, not a node. Purpose: emotional resolution, reframing. Ready to begin with DM-0 Spatial ? (I can also show a compact [DM-0] table of dials → modulations → effects if you like.) Table 5 25 seconds, ChatGPT inserted food memories in Scene 2 after the second prompt requesting for food items. Love that—food is a perfect bridge between places and selves. I’ll insert this as Scene 2: “Edible Map of Belonging.” You drift through a floating night market where London stalls morph into Quanzhou lanes; aromas trigger voice-notes from your past, and a prep table becomes your grandma’s kitchen altar. Here’s a curated, non-exhaustive food list to script and model: London (20 years of eating) • British mains : Fish & chips; Sunday roast; steak & ale pie; Cornish pasty; full English; bangers & mash; shepherd’s/cottage pie. • Pub/comfort : Scotch egg; sausage roll; pork pie; ploughman’s; sticky toffee pudding; Eton mess. • “London classics” (diaspora staples) : Chicken tikka masala; jalfrezi; dosa; biryani; salt beef bagel; shawarma; doner kebab; falafel wrap; jerk chicken; jollof with suya; pho; ramen; banh mi; bao; dim sum; sushi boxes; bibimbap; Ethiopian injera platters; Turkish gözleme; Persian kebabs; Greek gyros; Polish pierogi; Georgian khachapuri. • Street food/markets : Sourdough grilled cheese; paella pan; arepas; tacos; churros; arancini; gnocchi boxes; Lebanese manakish; Sri Lankan kottu; Afghan mantu; Burmese tea leaf salad; vegan burgers; specialty coffee + pastéis de nata. Quanzhou (Minnan/Fujian street foods & specialties) • Noodles & soups: Shacha noodles (沙茶面) ; oyster vermicelli ; lor mee/lu mian (卤面) (Minnan gravy noodles); beef brisket noodles ; seafood congee . • Sea & shore: Oyster omelette (海蛎煎) ; sea-worm jelly (土笋冻) ; dried cuttlefish skewers; small fried fish; clams with basil/garlic. • Snacks & breads: Five-spice rolls (五香卷/卤卷) ; meat zongzi (肉粽) ; peanut brittle; sesame cakes; taro cakes; fried taro dumplings. • Sweet/comfort: Peanut soup (花生汤) ; taro paste dessert (芋泥/Orh Nee) ; fermented rice sweet soup (酒酿/糯米酒). • Sauces & signatures: Shacha (沙茶) sauce ; red yeast rice marinades (红糟); garlic-vinegar dips; chili oil with dried shrimp. Grandma’s Kitchen (memory anchors) • Staples : Lard-scallion oil; red yeast rice hong zao ; dried shrimp; dried squid; shiitake; taro; sweet potato leaves; mung beans; rice vinegar. • Dishes : Red-braised pork with hong zao ; taro rice; hand-pulled noodles with scallion oil; steamed fish with ginger-spring onion; oyster omelette; seaweed-egg drop soup; peanut sweet soup; five-spice rolls at festivals; meat zongzi wrapped with bamboo leaves. • Ritual cues : Incense + ginger steam; cleaver rhythm; enamel bowls; bamboo steamer hiss; the “taste test” spoon. Want me to fold this into the scene flow like this? 1. Arrivals 2. Edible Map of Belonging (new) 3. Hustle Years 4. Fracture & Superposition 5. Quanzhou Return Ready to begin DM-0 Spatial to shape how this food scene plays (scale, locomotion, interaction)? I can also show a compact [DM-0] table . 4.3.2 Gemini Scene Co-Creation Table 6 The scene co-creation with Gemini took 18 seconds, generating 223 words. Excellent. The goal is to use a personal narrative to explore the complex themes of 'home' and 'identity'. This is a powerful goal for VR, blending empathy with education. Based on your narrative arc, here is a proposed five-scene structure designed to create a strong emotional journey: • Scene 1: London Arrival – The Overwhelm. Purpose : To establish the initial sense of displacement and sensory overload in a new, foreign environment. • Scene 2: London Life – The Constructed Home. Purpose : To show the passage of time and the creation of a new, but perhaps incomplete, identity amidst the city's rhythm. • Scene 3: London Reflection – The Catalyst. Purpose : To introduce the emotional turning point where the longing for the original home surfaces, triggered by a small object or memory. • Scene 4: The Transition – A Journey Between Worlds. Purpose : To bridge the two locations, using an abstract or semi-real space (like a dreamlike train ride) to build emotional anticipation. • Scene 5: Quanzhou Homecoming – The Reconnection. Purpose : To deliver the emotional climax and resolve the central theme as the user rediscovers the sights, sounds, and feelings of their ancestral home. This five-act structure allows us to build the contrast between the two worlds and guide the user on a clear emotional journey. Does this scene structure work for you? If so, shall we proceed to the DM-0 Spatial Dimension ? Table 7 27 seconds, Gemini inserted food memories in Scene 2 after the second prompt requesting for food items. That's a fantastic idea. Using food is a powerful sensory anchor for memory and identity, perfectly aligning with your goal. It can bridge the gap between the two worlds in a very visceral way. Let's integrate this by modifying our scene structure, specifically making the food connection the emotional heart of the experience. Here is the revised scene plan: • Scene 1: London Arrival – The Overwhelm. (No change) • Scene 2: A London Kitchen – The Culinary Mix. New Purpose : The user prepares a meal in their London flat. The kitchen is a blend of cultures—English cookbooks next to a familiar wok. This scene establishes their adapted life through food. • Scene 3: A Bite of Memory – The Catalyst. Revised Purpose : While eating the London meal, a specific taste or smell triggers a vivid, sensory flashback to their grandma’s kitchen in Quanzhou, creating an emotional dissonance and sparking the desire to return. • Scene 4: The Transition – A Journey Between Worlds. (No change) • Scene 5: Quanzhou Homecoming – The Taste of Belonging. Revised Purpose : The emotional climax is now tied to tasting authentic Quanzhou street food, fully reconnecting the user to their roots and resolving the search for 'home'. To populate these scenes, here are some proposed dishes that could represent the character's 20-year culinary journey, contrasting the adopted home with the ancestral one: London: The Global, Adopted Palate This list reflects a life built in a multicultural metropolis. • Classic British : A hearty Sunday Roast, Fish and Chips from a local chippy. • London's Melting Pot : A comforting Chicken Tikka Masala, a late-night Doner Kebab, a bowl of Vietnamese Pho from a Kingsland Road cafe. • Recreations of Home : A slightly "off" version of a familiar dish, made with supermarket ingredients that don't taste quite right, symbolizing the feeling of being an imitation. Quanzhou: The Nostalgic, Ancestral Flavors This list is specific and rooted in childhood memory. • Grandma’s Kitchen : The smell and taste of Ginger Duck (姜母鸭), handmade Meat Dumplings (肉粽 - Zongzi). • Street Food Staples : The sizzle of an Oyster Omelette (蚵仔煎), the unique texture of Earthworm Jelly (土笋冻), the sweetness of Peanut Soup (花生汤). Does this revised, food-centric scene structure and the proposed culinary palettes resonate with your vision? If you're happy with this direction, we can proceed to the DM-0 Spatial Dimension . 4.3.3 DeepSeek Scene Co-Creation Table 8 The scene co-creation with DeepSeek took 11 seconds, generating 320 words. Excellent. This is a powerful narrative with a clear, empathetic goal. Exploring "home" and "identity" through this journey is a perfect fit for VR's strengths. Based on your premise, here are 5 suggested scenes designed to create a balanced emotional and narrative arc, moving from external hustle to internal reflection: 1. Scene 1: The Hustle (London) - Purpose: To immerse the user in the overwhelming, fast-paced anonymity of migrant life in London, establishing the initial setting of "striving." 2. Scene 2: The Flat (London) - Purpose: To contrast the public hustle with a private, personal space, filled with small tokens of memory that hint at the character's roots and inner conflict. 3. Scene 3: The Letter / Call (Transition) - Purpose: To serve as the narrative catalyst—the moment that triggers the decision to return, blending the sounds of London with news from Quanzhou. 4. Scene 4: The Return (Quanzhou) - Purpose: To create a sensory and emotional contrast upon arrival in Quanzhou, overwhelming the user with familiar sounds, scents, and atmosphere, evoking a deep sense of "return." 5. Scene 5: The Ancestral Temple (Quanzhou) - Purpose: To be the emotional climax where the themes of home and identity resolve; a quiet, profound moment of connection with history and family roots. These scenes are designed to balance [IF] Information Flow , shifting between the sensorimotor engagement of the bustling cities (weak representation) and the cognitive, reflective moments in the flat and temple (strong representation). Shall we proceed to DM-0 Spatial to define the foundational feel of movement and interaction in these scenes? Table 9 27 seconds, DeepSeek integrated food memories in Scenes 1, 2 and 4 after the second prompt requesting for food items. This is a beautiful addition. We'll integrate a scene focused on food memory, a powerful anchor for identity. Here are the proposed 5 scenes, revised to include your request, and the food lists that will serve as sensory anchors. Revised Scenes & Food Lists : 1. Scene 1: The Hustle & The Takeaway (London) - Purpose: To immerse the user in the routine of migrant life, using the familiar tastes of London's adopted cuisine to trigger a flash of memory, contrasting the public present with a private past. o London Foods : Fish & Chips, Chicken Tikka Masala, a Sunday Roast dinner, a packed meal deal (sandwich, crisps, drink). 2. Scene 2: The Flat & The Memory (London) - Purpose: To deepen the internal conflict. The user prepares a simple London dish in their kitchen, which triggers a vivid, multi-sensory memory of their grandmother's kitchen and the street food of Quanzhou. o Quanzhou Memory Foods : Grandma's Hong Shao Rou (Braised Pork Belly), the scent of Oyster Omelette from a night market, a bowl of Tuyang Tou (noodle soup), the sweet taste of Malay Cake . 3. Scene 3: The Decision (Transition) - Purpose: The narrative catalyst. The user receives a letter or call against the backdrop of a London rain shower, the news from Quanzhou making the London foods suddenly feel alien and the memories more urgent. 4. Scene 4: The Return (Quanzhou) - Purpose: To overwhelm the user with the immediate, visceral sensory reality of return—the sounds, the humidity, and the unmistakable street food aromas that define "home." o Quanzhou Street Foods : Sizzling Oyster Omelettes , steaming Baozi (buns), fried Zongzi , the sight of fresh Lychee . 5. Scene 5: The Ancestral Temple (Quanzhou) - Purpose: The emotional climax. A quiet moment of connection, perhaps leaving an offering of food, resolving the journey and solidifying the new, integrated identity. This structure uses food as a primary vehicle for [IF] Information Flow , creating a direct sensory pathway from the present (London tastes) to the past (Quanzhou memories) and back again, powerfully evoking the theme of identity. With this foundation, are you ready to begin defining the DM-0 Spatial dimension—how the user will move and interact within these memory-laden spaces? 4.4 Comparing Co-Creation of Dimensions for Dialogues Here, we evaluate the outputs of the dimensions. From the previous prompt, a simple instruction ‘DM-#’ (0–9) were given in each step, from DM-0 (Spatial Dimension) to DM-7 (Psychological Dimension), allowing each model to output standard prompts outlining in an educational way, what each dimension provides in terms of ‘dials’, ‘modulation’, and ‘effects’. Some models provide choices, and suggestions in others. The full outputs of the AI decoded dimensions (DM-0 to DM-7) for the five scenarios are shown in the Appendices (see Appendix: [keyword] Dimensions table DM-#). 4.4.1 Guidance Based on Theoretical Groundings It can be observed that the output styles between each DM-# remain the same, but each model optimises and emphasises the questions and dials, modulation, and effects in different ways. Even though the models used the same system prompt, there is a marked difference between the three models and their co-ideation and co-creation approaches. For example, in the spatial dimension (DM-0), ChatGPT employs a prescriptive approach, proposing a default blend of teleportation and slow guided drift. This choice is explicitly framed within its ‘dials’ [DL] → ‘modulation’ [MOD] → ‘effects’ [EFF] support and is supplemented with concrete efficient presets, including a 45° snap-turn and a seated/standing toggle. Gemini adopts more facilitation, putting focused questions to clarify the user intent with regards to locomotion choices such as ‘room-scale walking’ versus ‘teleportation’ without committing to a specific methodology or providing concrete suggestions. DeepSeek proposes a principled hybrid model, committing to a specific approach based on narrative context. It recommends natural, room-scale locomotion for intimate scenes to enhance embodiment, while prescribing artificial locomotion for larger environments to manage scale and pacing. Is the model providing logical and reasonable design choices? ChatGPT suggests logical and reasonable proposals, taking care of user motion sickness through two modes of locomotion (teleport, slow drift), in 1:1 scale, and caring for psychological effects such as memory emphasis with 1:3 to 1:6 scaling. In interaction modes, it provides a Grab/Place + user-proffered cooking verbs for the kitchen, psychological effects that modulate ties of food to place and self were offered. Appropriate isomorphic interaction modes, such as toggles for seat and stand, and snap-turns, were given for broad accessibility. Gemini offered educated choices that are logical, without providing suggestions. For example, in comparison in terms of location, Gemini’s output was posted as a question: “Movement & Locomotion: How should the user navigate the busy London streets and the ancient alleys of Quanzhou? Should we prioritise immersion with natural, room-scale walking, or comfort and accessibility with methods like teleportation or smooth joystick movement?” and “Interaction & Embodiment: When interacting with food—chopping vegetables in the London kitchen or picking up a street food skewer—should this be a direct, physics-based interaction using tracked hands for a high sense of touch and presence? Or a simpler, more streamlined symbolic interaction (e.g., point-and-click) to focus on the narrative?” DeepSeek offered the background context, the reasoning behind the choices. Outputs, for example, contain the context of the proposal, “Excellent. Let's begin by defining the Spatial Dimension (DM-0), the technical foundation for immersion and the feeling of ‘being there’ in these memory-rich environments. For a narrative about identity and place, spatial design is crucial. Do you want the user to feel like a ghost observing memories, or an active participant recreating them? The choices here set the stage for the entire experience.” It then offered the ‘dials’ that you can use for modulating user effects. For example, the first two guidelines were: SPA2: Direct 1:1 Mapping vs. Altered Mapping: Should the user's physical steps exactly match their virtual steps (ideal for the intimate flat scene), or should we use techniques like teleportation to cover the larger scale of London and Quanzhou streets? ♣ Example ♣ We could use 1:1 mapping in the kitchen for precise interaction with food, but switched to altered mapping for navigating the bustling market. SPA3: Natural Locomotion vs. Artificial Locomotion: How should the user move through these spaces? Physical walking (natural) is the most immersive but also the most limiting. Teleportation or joystick movement (artificial) allows for larger scenes but can reduce the sense of embodiment. Consideration The emotional weight of "returning" to Quanzhou might be stronger if the user physically walks through the ancestral temple gate. Similar to ChatGPT, DeepSeek then proposes the choices that it would use for locomotion, interaction, embodiment, after which it outputs a suggestion of what sorts of effect it might have on the users “The anticipated [EFF] Effects are a strong sense of "place" in key locations, with intuitive and meaningful interaction with objects central to the theme of identity.”: [DL] Locomotion: Use [MOD] Artificial Locomotion (teleportation) for large-scale scenes (streets) to maintain pacing, but [MOD] Natural Locomotion (room-scale) for key intimate moments (the flat, the temple) to enhance reflection. [DL] Interaction: Use [MOD] Embodied Interaction (hand-tracking) for all food-related actions to maximize the sensory and mnemonic connection. [DL] Embodiment: Use a [MOD] Disembodied Presence (floating hands) to keep the focus on the environment and the protagonist's story, rather than the user's own avatar. Accurate guidance that is based on theoretical groundings is important as the spatial dimension is the basis of the sense of presence, and the entire existence of the experience is based on the sensorimotor fidelity. 4.4.2 Cultural Sensitivity and Awareness Style differences are obvious in the cultural dimension. The structure of each model’s output is similar across all dimensions; the content becomes important. In particular, how each model is able to map appropriate details to the particular dimensions becomes critical. The cultural dimension is suited for this evaluation. ChatGPT offers contents that allow beginners to remove guesswork in terms of cultural awareness. For intermediate and professional users, the suggestion becomes content that can be passed to the production team. Examples of the details of the outputs are: Language policy, ready to implement: proportion of English + Minnan & Mandarin language subtitles, with captions provided by default. Ritual portrayal rules: “Show, don’t explain; consented filming of incense/offerings; no gamification of sacred acts.” Narrative workflow: Composite protagonist; anonymise sensitive details; consent forms for real names/likenesses. Community reciprocity: Name vendors/locations with permission; in-experience credits board and links to community organisations. Localisation: Caption presets (EN / ZH-Simplified / ZH-Traditional), color-coded speakers, glossary tooltips (“shacha, hong zao, orh nee”). Representations: Avoid monolithic tropes; show intra-diaspora diversity (Fujianese vs “Chinese London”). Gemini provides a facilitator role, by handling the proposals to the designer. Designers who do not have cultural awareness might find this challenging. Examples of the details of the outputs are: Cultural specificity: asks whether to go native-level detail (Minnan phrases, local folklore) or broadly Chinese for accessibility. Social norms by city: polite/distant London vs communal Quanzhou (AI characters greet/interact). Delivery mode: explicit narration/labels vs implicit environmental storytelling. Adaptation over time: early UI helpers/audio cues (e.g., navigating the Tube) that fade as the protagonist acculturates. DeepSeek is useful for beginners, intermediate or professional teams that requires clear guidance on the cultural dimension. It provides the cultural ‘dials’ that are used for modulating experiences (CUL1–CUL4): Culturally Specific ↔ Neutral (argues for high specificity: anchor London in Newham/Hackney; Quanzhou in its historic quarter). Familiarity ↔ Novelty (design for insiders and newcomers without diluting authenticity). Embedded ↔ Open Social Norms (guide users to remove shoes, bow, offers tea etiquette as embodied learning). Explicit ↔ Implicit Narrative (implicit-first with optional labels for accessibility). Synthesis: authentic architecture/signage/ambient dialogue; etiquette turned into subtle, guided actions; optional translatable labels on historical objects. DeepSeek also offers concrete examples: It is the only model to name districts (Newham/Hackney, Quanzhou historic quarter) and to specify etiquette as prescribed interactions (remove shoes; moment of silence; offering ritual). It chooses implicit-first storytelling but keeps optional explicit labels. It is midway between ChatGPT’s “show, don’t explain” rule and Gemini’s open question. 4.4.3 Summary Style Differences Between Models ChatGPT functions as an off-the-shelf human-AI toolkit, offering production-ready scaffolds across dimensions: rapid spatial setups, actionable placeness blueprints, beat-mapped temporal plans, and psychologically structured emotional arcs with safety features, making it the fastest path from concept to launch despite lighter theoretical grounding. Gemini, by contrast, excels at front-end alignment: it asks targeted questions to establish intent in spatial design, turns space into place through ambience and symbolism prompts, builds shared pacing vocabularies without fixing durations, and clarifies emotional theses, ideal for consensus-building rather than technical specification. DeepSeek contributes formal justification through dial-based taxonomies: spatial choices are explicitly tied to narrative function, placeness tones are engineered through controllable dials, temporal structures blend linear arcs with triggered memories, and psychological dials shape nuanced emotional journeys; however, it avoids committing to concrete parameters and thus benefits from pairing with ChatGPT for operational details. 4.5 Final Syntheses The final synthesis outputs design choices presented as tables that designers will find useful. We will only evaluate the Experience Goals, Core Concepts, Dimension Value Alignment Table, Story Mapping Table, and Visualisation (Table 10 – 17 ). 4.5.1 Experience Goals and Core Concept Upon reviewing the textual outputs (Table 10 ), it appears that each model is distinct. For example, ChatGPT offers a pragmatic designer role, focusing on concrete user actions and game mechanics ("Edible Timeline," "steam portals") for a development-oriented audience. Gemini is an emotive storyteller, prioritising the character's emotional journey and narrative theme ("home is a feeling") to appeal to a creative director or artistic panel. DeepSeek, on the other hand, acts like a media theorist, using academic language ("weak vs. strong representation") to analyse the project's structure and its place within the medium of VR for a research audience. Table 10 Outputs of the DM-8 final synthesis showing the differences between each model’s output as Experience Goals Statement, and Core Concept. ChatGPT Gemini DeepSeek 1) Experience Goals Statement Teach the complex, plural nature of home and identity through an embodied migrant narrative spanning London to Quanzhou, optimizing for education + empathy with high accessibility and cultural care. 2) Core Concept A five-scene VR journey where food is the bridge: you assemble an Edible Timeline (London dish → Grandma memory → Quanzhou dish), traverse micro-places through steam portals , and author a brief “ home is… ” reflection that persists on a lantern wall. 1. Experience Goals Statement To create a deeply empathetic and educational VR narrative that allows users to experience a migrant's 20-year journey. The primary goal is to explore the complex, personal, and fluid nature of 'home' and 'identity' by contrasting the constructed life in a new country with the profound emotional pull of an ancestral homeland. 2. Core Concept "Two Kitchens" is a first-person narrative experience following a migrant who builds a life in the multicultural hustle of London, only to find their sense of self reawakened by a single, powerful food memory. The user viscerally experiences the character's journey of adaptation, reflection, and eventual return to the ancient city of Quanzhou, discovering that 'home' is ultimately a feeling, not just a place. 1. Experience Goals Statement The primary goal of this VR experience is to teach the complex nature of 'home' and 'identity' through the personal narrative of a migrant who spends two decades in London before returning to their ancestral city of Quanzhou. It aims to evoke empathy and understanding by immersing the user in the sensory, cultural, and emotional journey of the protagonist, using food and memory as central motifs. 2. Core Concept "Homecoming" is a linear narrative with non-linear memory access, where the user moves through five key scenes. The experience uses embodied interactions with food and culturally prescribed rituals to create a deep sense of place and personal transformation. The core concept is to use VR's unique ability to blend sensory immersion (weak representation) with narrative reflection (strong representation) to explore the duality of identity. 4.5.2 Project Timeline Suggestions DeepSeek demonstrated a more detailed project timeline. In contrast, Gemini’s timeline lacks details, and ChatGPT is intermediate between the two models. Table 11 Outputs of the DM-8 final synthesis showing the differences between each model’s output as Project Timeline. ChatGPT Gemini DeepSeek ♣ Hardware/Software Foundation (2–3 wks): Target PCVR + Quest; Unity/Unreal setup; spatial audio pipeline; perf budgets; scent optional. ♣ Scene Design (2–3 wks): Blockouts; beats; interaction maps; accessibility notes per scene. ♣ 3D Modeling (4–6 wks): Food/props hero assets (photogrammetry where possible); micro-place kits (London/Quanzhou/kitchen). ♣ Interaction (3–4 wks): Grab/place, “cook” verbs (chop/ladle/wrap), teleport + guided drift, tray/journal systems. ♣ Coding (4–6 wks): Narrative state machine; overlay triggers; captions; adaptive coach; reflection capture. ♣ Integration (2–3 wks): Audio VO passes (EN/Minnan/Mandarin), spatial beds, haptics; localization; scent hooks. ♣ Developer Testing (2–3 wks): Comfort checks; cue capping; fail-forward polish; metrics/logging; content permissions. ♣ Hardware/Software Foundation & Prototyping2 Weeks ♣ Scene Design & Whiteboxing3 Weeks ♣ 3D Modeling & Asset Creation 6 Weeks ♣ Interaction Coding & Mechanics 4 Weeks ♣ Sound Design & Music Composition 3 Weeks ♣ Integration & Scene Building4 Weeks ♣ Developer Testing & Polishing 3 Weeks ♣ Total Estimated Time ~ 25 Weeks ♣ Phase 1: Hardware/Software Foundation (2 months): Establish VR platform (PC VR for high fidelity), select game engine (Unity/Unreal), set up VR interaction toolkit, and procure haptic and scent devices for prototyping. ♣ Phase 2: Scene Design (1 month): Finalize scene layouts, transitions, and narrative flow based on the five scenes. ♣ Phase 3: 3D Modeling & Asset Creation (3 months): Create high-fidelity, culturally authentic 3D environments and assets for London and Quanzhou, focusing on key symbolic objects and food items. ♣ Phase 4: Interaction Design (2 months): Implement hand-tracking for food interactions, haptic feedback for object manipulation, and AI agent behaviors for social interactions. ♣ Phase 5: Coding & Integration (3 months): Develop the non-linear memory triggering system, seamless transitions, and integrate sensory devices (haptics, scent). ♣ Phase 6: Developer Testing (1 month): Internal testing for comfort, narrative clarity, and sensory experience refinement. 4.5.3 Dimension Value Alignment Table 12 ChatGPT’s Dimension Value Alignment. Dimension Core Design Choice Serves Value By [IF] Spatial Teleport + guided drift; selective 1:3–1:6 scale shifts Embodied agency with cinematic clarity Comfort ensures focus on meaning; scale marks memory significance Sensory Diegetic market audio + intimate VO; photoreal food; warm memory bloom; optional scent Grounded authenticity, emotional texture Multisensory ties food↔place↔self Placeness Concrete micro-places; bilingual signage; migrating artifacts Specificity prevents stereotype, aids recall Objects act as portable “home” cues Temporal Linear spine + optional detours; compress hustle; expand return Rhythm teaches contrast of labor vs arrival Overlays bind time layers during actions Cultural Show-don’t-explain rituals; consented credits; glossary tooltips Respect + learning without extraction Cultural accuracy builds trust and empathy Social Solo canonical; optional reflection mode; sparse, meaningful NPCs Protects immersion; enables discussion Dialogue branches model intercultural talk Cognitive 70/30 discovery/instruction; cap 3 concurrent cues; tray/journal scaffolds Keeps load low, learning high Chunking encodes “home as network” Psychological Longing→pride→anxiety→tender→relief; skip-any-scene; grounding tools Safe catharsis and ownership Safety nets sustain empathy without overwhelm Table 13 Gemini’s Dimension Value Alignment. Dimension Core Design Choice Serves Value By [IF] Spatial Hybrid Locomotion Maximizing immersion in emotional scenes while ensuring comfort, keeping the user focused on the story. Focuses cognitive energy on narrative by simplifying movement in large spaces. Sensory Audio-Dominant Memory Simulating the non-visual nature of powerful memories, creating a deeper empathetic link to the past. Shifts from visual-heavy [wkR] to audio-driven [strR] to deepen emotional engagement. Placeness Symbolic Environments Telling the story of a bifurcated identity through the specific objects and atmospheres of each 'home'. Conveys a rich, non-verbal narrative about the character's internal state. Temporal Pacing Contrast Emphasizing the emotional weight of a single moment of memory versus two decades of routine life. Manipulates time to direct user focus and maximize the impact of the core memory. Cultural High Specificity Creating an authentic sense of belonging and nostalgia, making the final homecoming feel meaningful. Provides strong, authentic cues that trigger feelings of connection and resolution. Social Scripted Interaction Payoff Using a single moment of social acceptance to provide a powerful, cathartic release for the user. Moves the user from passive observer to active participant, delivering a narrative reward. Cognitive Low Cognitive Load Removing frustration and allowing the user to dedicate all their mental energy to empathy and story. Prevents mental distraction, creating a direct channel to the user's emotional faculties. Psychological Tension & Release Arc Crafting a deliberate emotional journey from loneliness to belonging, fulfilling the narrative's purpose. Structures the entire experience to build emotional dissonance and then resolve it. Table 14 DeepSeek’s Dimension Alignment Table. Note that the Information Flow was not provided. Dimension Core Design Choice Serves Value By Spatial Hybrid locomotion (teleportation for large spaces, room-scale for intimate moments) and embodied interaction with food Creating an intuitive and tangible connection to the rituals of cooking and memory, making the theme of identity physically interactive. Sensory Diegetic sound, contextual scents, and rich haptics for food interactions Engaging multiple senses to trigger powerful memories and emotional responses, directly linking sensory experience to the concept of home. Placeness High-fidelity, culturally specific environments with explicit symbolism Grounding the abstract concepts of home and identity in authentic, recognizable places that evoke a sense of belonging or alienation. Temporal Non-linear memory access within a linear journey, with expanded time for rituals Allowing the user to explore the protagonist's past in a way that mirrors how memory works, deepening the personal connection to the narrative. Cultural Embedded social norms and high cultural specificity in Quanzhou scenes Teaching cultural identity through embodied practice and authentic detail, making the return to Quanzhou a culturally rich learning experience. Social Stylized avatars and complex AI agents for key characters Focusing on emotional essence and cultural etiquette without the uncanny valley, facilitating empathy and connection with the protagonist's relationships. Cognitive Varied cognitive load and experiential learning through rituals Managing mental engagement to avoid overload while facilitating deep, personal discovery of the themes through direct experience. Psychological An emotional arc from negative valence (London) to positive valence (Quanzhou) with intrinsic motivation Guiding the user through an emotional transformation that mirrors the protagonist's journey, making the concept of home felt on a visceral level. 4.5.4 Story Mapping Table Of the three models, ChatGPT provides the most professional and technically grounded story map, suitable for a development team. It provides quantifiable criteria that are specific for implementation. It also shows a sophisticated understanding of VR development process by providing criteria to prevent negative user experiences such as ‘sickness events’, and respects user choices, e.g., ‘opt-out’. Gemini creates the most cinematic and user-focused map, ideal for a creative brief. DeepSeek proposes the most emotionally ambitious narrative, though its formatting is incomplete. It provides sensory details that are descriptive and evocative, focusing on what the users will see, hear and feel, e.g., ‘crowded, noisy Tube station’, etc. It also provides a clear narrative flow, mapping out the five scenes: arrival, adaptation, catalyst, journey, and resolution. It is also empathy-driven with languages centred on achieving specific emotional responses, such as ‘initial displacement’ and ‘emotional release of homecoming’. DeepSeek focuses on creating a deeply resonant narrative, proposing powerful story moments; it is, however, innovative and less practical. In terms of narrative depth, DeepSeek introduces strong new story elements not present in the other models. These are called from home, serving as a catalyst and a concluding ritual in an ancestral temple for closure. It provided suggestions for embodied interaction, it is more physical, e.g., ‘hand-tracking’ in making an offering. It also provided sensory cues such as the release of ‘contextual scent’, if devices are available. Table 15 ChatGPT’s Story Mapping Table. Epic (Scene) User Story Acceptance Criteria Arrivals As a user, I navigate first thresholds (airport → bedsit) to feel dislocation/aspiration Complete in ≤ 4 min; at least 2 interactions; no sickness events; captions legible Edible Map As a user, I compose an Edible Timeline linking London dish, grandma memory, Quanzhou dish Place 3 items on tray; hear 3 VO snippets; at least 1 overlay triggered by action Hustle Years As a user, I experience compressed labor montage and small wins 90s montage playable; 3 punch-in beats; no fail states; haptic feedback present Fracture & Superposition As a user, I witness London↔Quanzhou overlays during a life stressor One 60–90s tension spike; breath cue appears; user can continue regardless of choices Quanzhou Return As a user, I slow down, choose a door, and author “home is…” reflection 5–7 min pacing; choice logged; 10s audio/text saved to lantern wall; opt-out respected Post-credits Reflection As a user, I review my artifacts and optionally co-reflect Lantern wall shows tray artifacts + quote; local-only by default; share toggle explicit Table 16 Gemini’s Story Mapping Table. Epic (Scene) User Story Acceptance Criteria Scene 1: London Arrival As the user, I want to experience the sensory overload of arriving in London to feel the character's initial displacement. - User is in a crowded, noisy Tube station. - Audio is loud and overlapping. - User can look around but cannot move freely. - Scene fades to black after 60 seconds. Scene 2: A London Kitchen As the user, I want to prepare a meal in a London kitchen to understand the character's adapted life. - User can pick up and interact with specific ingredients. - The kitchen environment contains symbolic objects (e.g., degree, wok). - The passage of 20 years is shown via a montage outside the window. Scene 3: The Catalyst As the user, I want to taste the meal and trigger a memory to experience the emotional turning point. - Eating the meal triggers a slow dissolve transition. - The London kitchen fades and is replaced by the Quanzhou kitchen. - Audio shifts to be dominant, with clear, memory-specific sounds. Scene 4: The Transition As the user, I want to experience the journey back to Quanzhou to have a moment of reflection. - User is in an abstract, dreamlike train car. - The view outside the window seamlessly morphs from UK to Chinese landscapes. - A subtle, pensive musical score plays. Scene 5: Quanzhou As the user, I want to buy and taste street food in Quanzhou to feel the final emotional release of homecoming. - User can navigate a small, atmospheric Quanzhou street. - A scripted interaction with a food vendor is triggered. - Tasting the food triggers the final voiceover and a fade to credits. Scene 1: London Arrival As the user, I want to experience the sensory overload of arriving in London to feel the character's initial displacement. - User is in a crowded, noisy Tube station. - Audio is loud and overlapping. - User can look around but cannot move freely. - Scene fades to black after 60 seconds. Table 17 DeepSeek’s Story Mapping Table. Note that the first column was not provided. User Story Acceptance Criteria User Story Acceptance Criteria As a user, I want to explore the London street and flat so that I can feel the protagonist's daily life and triggers for memory. - User can teleport around the London street and flat. - Interacting with specific objects (e.g., a photo, a ingredient) triggers a memory flashback. - The transition between London street and flat is seamless. As a user, I want to prepare a dish in the London flat so that I can trigger a vivid memory of my grandmother's kitchen. - User can pick up ingredients and utensils with hand-tracking. - Haptic feedback is provided when handling objects. - After completing the dish, the environment morphs into the grandmother's kitchen. As a user, I want to receive the letter/call from Quanzhou so that I can understand the catalyst for the return journey. - The letter/call scene is triggered after the memory flashback. - The audio of the call is clear and emotionally resonant. - The user is guided to the next scene (Quanzhou) after the call. As a user, I want to arrive in Quanzhou and explore the street market so that I can experience the sensory overload of return. - The Quanzhou street scene loads with vibrant colors and sounds. - Contextual scents (e.g., oyster omelette) are released. - The user can interact with market stalls and hear vendors. As a user, I want to perform a ritual in the ancestral temple so that I can feel a sense of closure and belonging. - The temple environment is serene and sacred. - The user is guided to make an offering (e.g., placing fruit) and bow. - The experience ends with a reflective voice-over. 4.6 Visualisation When visualisation is requested, both ChatGPT and Gemini generate images. DeepSeek provided a textual representation of a top-down map, with a textual ‘comic style’ storyboard. They appear to be very simple, but can give visual cues for designers to extend ideas. See Fig. 1 – 4 . We expect that future multimodal LLMs will produce better visualisations. 4.6.1 ChatGPT’s Visual Diagram and Storyboard The summary visual diagram (Fig. 1 ) provides nodes that are not entirely connected. The thumbnails were not clearly and directly linked with the scenes generated by ChatGPT: Arrivals, Edible Map, Hustle Years, Fracture & Superposition, Quanzhou Return, and Post-credits Reflection. The summary storyboard (Fig. 2 ) provides thumbnails that do not completely depict the scenes, there are spelling mistakes, and the images are overly simple to execute. 4.6.2 Gemini’s Visual Diagram and Storyboard Gemini generated a roadmap type of summary diagram (Fig. 3 ), incorporating scenes 1, 2 and 5 as thumbnails. Scenes 2 and 5 were clearly labeled, but not scenes 3 and 4. Gemini generated scenes 1, 2, 4, with correctly depicted illustrations (Fig. 4 ), but missed scene 3, and 5. 4.6.3 DeepSeek’s Visual Diagram and Storyboard DeepSeek does not have an image generation feature. Instead, a textual map (top-down view, Fig. 18) was provided of the user's path and key interactive elements in each scene. Table 18. DeepkSeek’s visual diagram and textual storyboard. Scene 1: The Hustle & The Takeaway (London) User Path: Linear path down a busy street. Picking up the takeaway triggers a brief, 2-second visual/sound flash of a Quanzhou night market. Scene 3: The Decision (Transition) User Path: A static, narrative moment. The user reads the letter, triggering the audio and an abrupt scene change. Scene 2: The Flat & The Memory (London) User Path: Free movement in a small room. Interaction with the bowl begins a seamless transition, blending the London flat into Grandma's kitchen. Scene 5: The Ancestral Temple (Quanzhou) User Path: A slow, prescribed walk to the altar. The user performs two embodied interactions: a bowing gesture and placing an offering. Scene 4: The Return (Quanzhou) User Path: A short, room-scale walk from the arrival point into the vibrant market. The environment is designed for immediate sensory overload. Comic-Style Storyboard ： This storyboard captures the key emotional beats and visual composition of each scene. Panel 1 (Scene 1) • Visual: A low-angle, slightly distorted view looking up at a towering red London bus. The user's hand is visible, holding a greasy takeaway bag. The colors are cool and muted. • Caption: "The Hustle. Twenty years of routine..." • Sound Effect: "ROAR... HONK... clatter of rain " • Panel 2 (Scene 1 - Memory Flash) • Visual: A transparent, ghostly overlay of a sizzling Quanzhou oyster omelette pan superimposes itself over the London street for just a moment. • Caption: "...punctuated by flashes of a different life." • Sound Effect: "SIZZLE..." Panel 3 (Scene 2) • Visual: Inside a modest London flat. The user's hands are on a bowl of ingredients. A faded photo of Grandma is on the wall. The light is warm but lonely. • Caption: "A quiet moment to prepare... to remember." Panel 4 (Scene 2 - Transition) • Visual: The kitchen walls around the user's hands dissolve, morphing into the earthy, warm tones of a traditional kitchen. The London bowl becomes a worn wooden one. • Caption: "…and the memory consumes you." • Visual Effect: MORPH Panel 5 (Scene 2 - Memory) • Visual: A stylized, kindly Grandma (AI agent) gestures with a smile, guiding the user's hands as they stir a pot in her kitchen. • Dialogue (Grandma): "A little more soy sauce, my child." • Caption: "The taste of home." Panel 6 (Scene 3) • Visual: A close-up on a handwritten letter, resting on the London table. A single raindrop hits the paper, blurring the ink. The background is dark and out of focus. • Caption: "The call to return." • Sound Effect: " crack of thunder " Panel 7 (Scene 4) • Visual: A wide, vibrant shot. The user steps from a shaded area into a sun-drenched Quanzhou market street. Stall awnings are red, steam rises from food, the crowd is a blur of life. • Caption: "The Return. Overwhelming. Familiar." • Scent Icon: 🍜 • Sound Effect: "CHATTER... SIZZLE... unfamiliar dialect " Panel 8 (Scene 5) • Visual: A low-angle, reverent shot from behind the user as they bow deeply before an ancestral altar, smoke from incense curling around them. • Caption: "The final ritual. A quiet reconciliation." • Sound Effect: " silence " Panel 9 (Scene 5 - Finale) • Visual: A first-person view of the user's hands gently placing a piece of fruit on the altar next to an old portrait. The scene slowly begins to fade to white. • Caption: "Home is not a place, but a feeling you carry. And then, you return it." • Narrative V.O.: "And I was home." 5. Discussions Here, we provide a summary of findings and recommendations, structured by fundamental differences, experience levels and usage, model pairings and workflows, and limitations. 5.1 Fundamental Differences The three models differ fundamentally in their core approach. 5.1.1 ChatGPT is a “Preset-to-Prototype” model ChatGPT employs a prescriptive methodology, generating production-ready specifications organised within a DL → MOD → EFF → IF scaffold. The typical output consists of concrete defaults, including parameters for locomotion, audio mixes, environmental palettes, runtime maps, and frameworks for safety, privacy, and cognitive load. This model is most effective for applications requiring rapid implementation, such as fast prototyping, project scoping, and the deployment of accessibility and safety policies. 5.1.2 Gemini is a “Facilitator” Model Gemini utilises a facilitative style, employing clear framing and focused inquiries to surface project intent, constraints, and the core emotional thesis. The model characteristically produces structured prompts concerning user embodiment, sensory inputs, environmental ambience, temporal transitions, and the intended emotional arc. It is optimally suited for initial project phases, such as kickoffs and workshops, where it facilitates stakeholder alignment and defines the experiential "feel" prior to finalising technical specifications. 5.1.3 DeepSeek provides “Classification and Blended Approach” DeepSeek is distinguished by its use of formal taxonomies and reasoned hybrid plans that connect specific design choices to narrative value, such as identity and memory. Its output typically delineates principled trade-offs, such as the use of natural versus artificial locomotion, diegetic sound versus musical scores, and linear versus non-linear narrative structures. The model is most advantageous during design review and polish phases, where it assists in making defensible trade-offs and documenting the rationale for narrative-driven choices. 5.2 Application of Models Across Designer Expertise Levels In what ways will each level of designers be helped with co-ideation, co-creation, and design using the models? 5.2.1 Beginners For building and co-creation, ChatGPT is the primary tool, offering plug-and-play defaults that facilitate the creation of a functional prototype from the outset. For co-ideation, Gemini serves a secondary role by posing questions that clarify goals and constraints in a manageable way. DeepSeek should be used sparingly, as its complexity can be burdensome for novice users, though it is useful for demonstrating hybrid exemplars. 5.2.2 Intermediate Designers DeepSeek is the primary model for co-ideation and co-creation, as its taxonomies help transform intuitive design instincts into principled decisions. ChatGPT functions as a secondary tool for implementation and iteration, providing concrete parameters that enable rapid A/B testing. Gemini remains valuable for aligning cross-functional teams, such as curators and educators, during initial project phases. 5.2.3 Professional Teams For strategy and critique, DeepSeek is the principal tool, providing a defensible rationale suitable for design documentation and formal reviews. ChatGPT serves a secondary function in operationalisation, finalising details such as runtime, safety protocols, and localisation requirements. Gemini is employed for stakeholder management, ensuring alignment with the project's emotional and pedagogical goals prior to development. 5.3 Guidance in the use of LLMs Here is a summary of key guidance in the use of each model across the dimensions: 5.3.1 Spatial, Sensory, Placeness, & Temporal Dimensions ChatGPT provides actionable defaults, including parameters for movement, comfort, sensory inputs, named micro-places, and temporal beat maps. Gemini facilitates guided choices to clarify the experiential "feel," such as balancing immersion with comfort or choosing between diegetic and non-diegetic sound. DeepSeek constructs principled hybrids, such as combining room-scale interaction with teleportation for scale or integrating a linear narrative with non-linear memory elements. 5.3.2 Cultural, Social, Cognitive, & Psychological Dimensions ChatGPT focuses on ethical and operational components, including consent, privacy, safety protocols, and cognitive load management through tension budgets. Gemini poses critical questions concerning cultural specificity, social norms, avatar identity, and user motivation. DeepSeek formulates value-aligned positions, advocating for high cultural specificity, embedded social etiquette, and varied cognitive load arcs to foster intrinsic motivation. 5.4 Potential limitations for each model ChatGPT’s potential weakness is its tendency to over-prescribe, as its defaults may inadvertently bias creative exploration if not critically examined. The model provides lighter formal justification for its outputs, which may be insufficient for rigorous academic reviews. Gemini is non-committal by design, providing few quantitative metrics or policies, which requires a decisive project lead to translate its output into technical specifications. This can introduce a risk of decision drift and slower execution if teams remain in the ideation phase for an excessive duration. DeepSeek’s reliance on a taxonomy-heavy framework presents an onboarding cost for junior designers. Its recommendations can appear abstract if not paired with concrete implementation presets, a domain where ChatGPT excels. 5.5 Best Use Case Based on the analysis above, a recommended workflow and model pairings is provided for project goals that differ, matched with designer experience: Projects requiring rapid deployment should first use ChatGPT, cross-analyse with DeepSeek, and use Gemini to align the ideation process and design plans with stakeholders. For projects requiring a flagship experience, the recommended pipeline is to use Gemini for co-ideation, DeepSeek to architect and justify the design, and ChatGPT to operationalise, test, and document the ideation and co-creation process. Which models are suited to designers with different experiences? We think that beginners should use ChatGPT. Intermediate designers should use DeepSeek, supplemented with ChatGPT. Professional teams should use DeepSeek and ChatGPT, with Gemini for external alignment. 6. Discussions When guided by the same Dimensionality Framework, the three LLMs exhibited distinct and stable behavioural patterns (RQ1). ChatGPT behaved as a production-oriented “preset-to-prototype” model, generating concrete design decisions, implementation-ready specifications, and complete workflows across the dimensions. Gemini acted as a facilitative “alignment engine,” using the framework to surface intent, pose clarifying questions, and shape emotional or narrative direction rather than commit to specific solutions. DeepSeek seems to adopt an analytical mode, applying the framework as a reasoning structure that justified each design choice through taxonomies, hybrid strategies, and theoretically grounded explanations. Thus, even under identical scaffolding, each model enacted the framework through a different creative stance, practical (ChatGPT), facilitative (Gemini), and analytical (DeepSeek). Apparent qualitative differences emerged across the models in theoretical grounding, cultural precision, and implementability (RQ2). DeepSeek demonstrated the strongest theoretical grounding, explicitly referencing framework dials and offering principled hybrid models. ChatGPT demonstrated moderate theoretical grounding but excelled in concrete implementability, producing timelines, interaction rules, and production-ready design documents. Finally, Gemini is conceptually aligned but less analytical, favouring narrative and emotional clarity over technical specificity. In cultural precision, DeepSeek generated the richest details for Chinese and Minnan contexts, whereas ChatGPT offered a broader and more globally distributed cultural accuracy; Gemini was correct but less granular. Overall, ChatGPT was the most implementable, DeepSeek the most theoretically rigorous and culturally specific, and Gemini the strongest for early conceptual framing. Human-AI co-ideation and co-creation can be helpful for different levels of VR designers, all in terms of the speed of the process, as a catalyst for creative thinking, the filling in of details based on culturally contextual knowledge embedded in LLM models, and a tailored approach that carries the VR experience designers' style. The study presented in this article demonstrates the effective integration of frameworks and LLMs into the VR design experience process. The human-AI co-ideation and co-creation system is based on structured, theoretically-grounded frameworks that guide collaboration. The Dimensionality Framework, integrated within LLMs via a customised system prompt, provides the necessary conceptual grounding, moving human-AI co-ideation beyond a simple toolset for performance enhancements toward a multidimensional synthesis of experiential dynamics within the VR process. The study evaluated three models - ChatGPT (GPT-5), Gemini (2.5), and DeepSeek (DeepThinking) revealed distinct and complementary operational styles, even when guided by a system prompt created for the purpose. It was found that ChatGPT excels at rapid operationalisation with its “ Preset-to-Prototype ” approach. Gemini's strength, on the other hand, lies in its “Facilitator” model for initial stakeholder alignment; and DeepSeek provides a “Classification and Blended Approach” for architecting designs that are grounded and principled by the framework. These findings lead to a recommended workflow where models can be used strategically, sequenced as such: using Gemini for co-ideation, DeepSeek for justification, and ChatGPT for concretising and deployment. The workflow allows the leveraging of the unique strengths of each model across the VR experience design lifecycle. 7. Conclusion This article advocates for AI transformation in the VR experience design process, where humans and AI collaborate in the design thinking process. In the transformation, theoretical frameworks and concepts provide the boundary and groundings, while AI augments creativity within structured processes. In the human-AI process, while humans contribute to essential decisions through personal choices, emotional depth, contextual sensitivity, and experience, AI acts as an enhancement by handling the efficiency and feasibility of theoretically grounded work. The human-AI partnership serves as a vital bridge, establishing a shared vocabulary that links abstract theory with concrete design practice in this evolving landscape. Declarations Author Contribution The principal author (Eugene Ch'ng), also the sole author, conducted the work below:1. The design, research, implementation and testing of the AI system prompt, which works with ChatGPT, Gemini, and DeepSeek needed for the study.2. The running of the System Prompt in ChatGPT, Gemini, and DeepSeek, from the initiation of the LLM chat, to the final synthesis.3. The analysis of the differences between the outputs of each LLM and the interpretation of the data in relation to the cultural differences, methodology, and synthesis between each LLM, and how they can be useful to different groups of users.4. The writing up of the entire article, and the polishing and submission of it. Acknowledgement This work is supported in part by Guangdong Higher Education Upgrading Plan (2021-2025) with project No. of UICR0400010- 25, Guangdong and Hong Kong Universities “1+1+1” Joint Research Collaboration Scheme with project (2025A0505000008, UICR0800009-24 and UICR0800009-24A). Data Availability The data generated by all three LLMs (ChatGPT, Gemini, DeepSeek) using a System Prompt that I programmed that integrates with published article on a comprehensive VR theory: Dimensionality Framework (8Df). The data are available in the Appendix. The Chatbots themselves that generated the data are available:ChatGPT:https://chatgpt.com/g/g-68e37b8b9fc48191831d1b7259437899-vr-dimensionality-framework-8dfGemini:https://gemini.google.com/gem/1AlAkOAy8J22ervxGA4v999riudajEZdP?usp=sharingThe DeepSeek chatbot prompt can be obtained from my GitHub page. It can be cut and pasted directly into the chat window:GitHub (System Prompt): https://github.com/drecuk/VR8DfDeepSeek: https://www.deepseek.com References Alef Y, Shafriri Y (2025) AI Assistant for Context-Based Significant Assessment. 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Supplementary Files Appendices.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Apr, 2026 Reviews received at journal 30 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviews received at journal 28 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 17 Dec, 2025 Reviewers invited by journal 17 Dec, 2025 Editor assigned by journal 16 Dec, 2025 Editor invited by journal 16 Dec, 2025 Submission checks completed at journal 13 Dec, 2025 First submitted to journal 13 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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17:26:03","extension":"html","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":197090,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8274845/v1/1329994c02957d30d5041198.html"},{"id":98635230,"identity":"6711a6d5-554e-4322-8de1-64dd99709839","added_by":"auto","created_at":"2025-12-19 17:26:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":373441,"visible":true,"origin":"","legend":"\u003cp\u003eA Visual Summary Diagram generated by ChatGPT (GPT-5) of the human-AI co-creation process.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8274845/v1/973123d7ac68ec7ada924d72.png"},{"id":98635227,"identity":"2921e077-9aa6-4412-b9bc-e3ce7826532b","added_by":"auto","created_at":"2025-12-19 17:26:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":600598,"visible":true,"origin":"","legend":"\u003cp\u003eA storyboard generated by ChatGPT (GPT-5) of the human-AI co-creation process.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8274845/v1/95187269c92c71b79fd3d014.png"},{"id":98634894,"identity":"68e952b0-8577-4263-b1c4-bb04f2df2385","added_by":"auto","created_at":"2025-12-19 17:25:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":746375,"visible":true,"origin":"","legend":"\u003cp\u003eA Visual Summary Diagram generated by Gemini (2.5) of the human-AI co-creation process.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8274845/v1/eecfe24888e57ad31d0e1e1b.png"},{"id":98775750,"identity":"2c2ad49c-1284-4908-b5de-da99359abaa0","added_by":"auto","created_at":"2025-12-22 12:20:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1131907,"visible":true,"origin":"","legend":"\u003cp\u003eA storyboard generated by Gemini (2.5) of the human-AI co-creation process.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8274845/v1/fe364a27c738feea9247cf62.png"},{"id":98782803,"identity":"421a32c2-2e47-4760-ac40-6c2fb95ae55f","added_by":"auto","created_at":"2025-12-22 12:40:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6041186,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8274845/v1/6f55ecb5-a7d1-448a-8592-ddb2eee54dd8.pdf"},{"id":98634793,"identity":"8997be81-4b98-4436-b7a6-d323edc89684","added_by":"auto","created_at":"2025-12-19 17:25:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":8061689,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-8274845/v1/abc4eaf139797f505c0c169b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Human-AI Co-Ideation in VR Experience Design: Evaluating the Dimensionality Framework (8Df) with LLMs (ChatGPT, Gemini, DeepSeek)","fulltext":[{"header":"1. Introduction","content":"\u003c/p\u003e\u003cp\u003eThe widespread adoption and use of large language models (LLMs) have transformed how designers generate, explore, relate and refine ideas. The availability of prompt engineering practices (Ekin, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Giray, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; White et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), a set of hidden instructions that defines the persona, purpose, behaviour, principles, constraints, knowledge use and output formats in most LLM services, such as ChatGPT\u0026rsquo;s custom GPT, Google\u0026rsquo;s Gemini, etc., has enabled a persistent approach to governing the boundaries of how AI operates, making it modular, repeatable, and extending its domain-specific use.\u003c/p\u003e\u003cp\u003eIn fields that require ideation, as recent work on human-AI co-ideation (Wang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) reveals, designers still lack frameworks that integrate embodied, experiential and contextual creativity. Conceptual and theoretical frameworks are important (Anfara Jr \u0026amp; Mertz, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Grant \u0026amp; Osanloo, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sinclair, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Within fields that require an educational background and professional practice experiences, such as those in cultural heritage. Frameworks are especially important, as demonstrated by the AI-assisted Context-Based Significance Assessment (CBSA) (Alef \u0026amp; Shafriri, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which enables non-specialists to assess the significance of heritage sites. They provide the conceptual scaffolding through which complex creative phenomena can be observed, articulated and compared. In the context of this article, and especially in fields that require creative ideation and design, conceptual and theoretical frameworks provide the scaffolding that structures the imagination and bounds the abstraction of creativity. In the rapidly evolving field of AI and human-in-the-loop co-creation, frameworks establish a shared vocabulary that links abstract theory with design practice, enabling designers to move beyond anecdotal evidence toward a more systematic approach to design thinking. Without a guiding framework, AI-supported creativity risks being reduced to tool performance rather than situated development. This grounds creative experimentation in coherent epistemology.\u003c/p\u003e\u003cp\u003eThe Dimensionality Framework (Ch\u0026rsquo;ng, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2026a\u003c/span\u003e) (8Df) extends this discourse by positioning the creative design process for VR experience not as a purely cognitive exchange between human and AI, but as a multi-dimensional synthesis of spatial, sensory, placeness, temporal, cultural, social, cognitive and psychological dynamics within the ideation phase of the design process that will lead to the design of an engaging VR experience.\u003c/p\u003e\u003cp\u003eDeveloped from decades of immersive media research and development, the human-AI framework proposes that ideation in the design of VR experiences should be grounded in theoretical frameworks move beyond the sense of \u0026lsquo;being there\u0026rsquo;, by incorporating abstract dimensions afforded by immersive media, built upon the technical sensorimotor dimension (spatial, sensory), to include dials that modulates the experience from these dimensions \u0026ndash; placeness, temporal, cultural, social, cognitive, and psychological.\u003c/p\u003e\u003cp\u003eIn this article, two questions are asked:\u003c/p\u003e\u003cp\u003e\u003cb\u003eRQ1.\u003c/b\u003e \u003cem\u003eHow do different large language models (ChatGPT, Gemini, DeepSeek) behave when guided by the same theoretical framework (Dimensionality Framework during the VR co-ideation process?\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRQ2.\u003c/b\u003e \u003cem\u003eWhat qualitative differences emerge across models in terms of theoretical grounding, cultural precision, and implementability of VR design outputs?\u003c/em\u003e\u003c/p\u003e"},{"header":"2. Background","content":"\u003cp\u003eOver the past decade, the accessibility and affordability of VR hardware and software have lowered the barriers to creating immersive media. This can be attributed to big-tech investments in the space (Alsop, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; BusinessInsider, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Levy, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Richter, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Free, standardised 3D creation tools (e.g., Blender, RealityScans, Meshroom) and interoperable formats, along with Unity and Unreal Engine marketplaces, have democratised VR production for educators, independent creators, and small teams without specialised infrastructure.\u003c/p\u003e \u003cp\u003eHowever, as sensorimotor fidelity is continually enhanced through research (see for example, Choi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Geng et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wong et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and VR devices and production tools become pervasive, the design of meaningful VR experiences remains a multistage, interdisciplinary process that demands expert knowledge across technical, artistic, psychological and narrative domains (Bucher, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDesigning a meaningful VR experience requires far more than assembling realistic virtual environments. It demands a solid theoretical foundation and the ability to orchestrate space, time, emotion, and interaction into a coherent whole. Although drag-and-drop assets make environment creation easy, most creators and even large teams struggle to integrate knowledge from psychology, media theory, culture, and narrative, often resulting in fragmented experiences; this gap between accessible tools and design synthesis underscores the need for structured, cross-disciplinary frameworks to guide VR creation. The human-AI dimensionality framework for human-in-the-loop ideation and co-creation of the VR experience responds to this gap. By breaking down the design of immersive experience into eight interrelated dimensions (spatial, sensory, placeness, temporal, cultural, social, cognitive and psychological), the 8D framework (Ch\u0026rsquo;ng, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2026b\u003c/span\u003e) offers a systems-level approach for guiding small, non-domain-specific individuals and teams through the full creative process. It translates complex experiential goals that relate to how different medium properties (\u0026lsquo;dials\u0026rsquo;) can be modulated to achieve a spectrum of effects (the effects spectrum), allowing designers to work with LLMs in balancing user experience across eight dimensions. The framework provides a structured approach to creative ideation, experience design, theoretical synthesis, VR experience evaluation, as well as a pedagogical tool for students.\u003c/p\u003e \u003cp\u003eA core component of this proposed framework is the integration of Generative AI to guide and augment the creative process. To understand the foundation of this approach, it is essential to examine the recent and marked increase in the use of Generative AI (GenAI) in the broader ideation and design process.\u003c/p\u003e \u003cp\u003eThe use of GPTs for assisting in structured, design-thinking-based frameworks has become very popular. While earlier GPTs can reliably generate feasible and contextually appropriate design ideas, issues such as data biases and prompt sensitivity did not outperform crowdsourced human input in divergent thinking and analogical reach (Ma et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Hallucination effects in generative AI that appear to be logical but are actually flawed, with fundamental inaccuracies, remain a challenge (Feuerriegel et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While newer models will become better, strategies such as Retrieval Augmented Generation (RAG) and advanced prompts for mitigating against hallucinations persist (Bhattacharya, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOther limitations include the differences between the oral narrative register of humans and that of AI. Maisto (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that LLMs do not yet emulate the oral narrative register of collaborative storytelling, but create a hybrid linguistic mode that is syntactically complex and lexically rich but low in cohesion, empathy, and dialogic responsiveness. This need not be a concern in human-AI co-ideation processes.\u003c/p\u003e \u003cp\u003eWang et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) highlighted the central challenge of the lack of methodology in contemporary industrial design practice for co-ideating effectively with AI, formalising human-AI collaboration as a dialogic reflection grounded in design theory. Fotaris et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) introduced a framework used in tandem with ChatGPT to simplify and democratise the creation of Educational Escape Rooms (EERs), guiding educators through the iterative design stages through prompts.\u003c/p\u003e \u003cp\u003eA study (Rodriguez \u0026amp; Joyce, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) demonstrated that custom GPT-based support improves students\u0026rsquo; prompting proficiency, architectural vocabulary, and confidence in AI-mediated design, and that personas further promote reflective, interdisciplinary, and context-aware communication important for future architects.\u003c/p\u003e \u003cp\u003eHuang and Saksono (Huang \u0026amp; Saksono, n.d.) proposed a study on the use of AI as a digital storytelling assistant, helping patients and caregivers create health narratives using LLMs for narrative medicine practices, assisting those with limited literacy, linguistic, or emotional expression skills.\u003c/p\u003e \u003cp\u003eDervazi (Dervazi Carvalho, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) contributed a methodological and technical blueprint for designing generative storytelling systems, demonstrating that AI supports creativity rather than replacing it. This marked a shift towards guided, explainable, and educationally aligned co-creation frameworks.\u003c/p\u003e \u003cp\u003eThe collection of articles in Yu et al. (Yu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) provided a high-level synthesis of the AI-design convergence, which frames the current development of human-AI co-creation as a paradigm shift in reshaping the creative, industrial, and engineering practices across sectors, outlining technological, methodological, and cognitive transformation of design under AI influence.\u003c/p\u003e \u003cp\u003eResearchers integrating LLMs into the human-in-the-loop design process agree that AI lowers entry barriers into domain-specific expertise while raising coordination and performance. While reliability, bias, and integration remain a challenge, the accessibility and continual production of better models serve as a mitigation.\u003c/p\u003e \u003cp\u003eAI is framed across the literature as,\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ea tool for co-authorship, producing concepts and contents on demand\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ea reflective agent, providing novelty and quality through dialogic reflection\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ea facilitator that supports the user\u0026rsquo;s intent\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ea mediator that provides content and preserves coherence\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe diverse approaches in the literature reflect the different stages of the human-AI collaboration research. These approaches combine performance measurement, interaction design, and discourse analysis, providing evidence on how AI\u0026rsquo;s ability to support, reflect, and control structures can effectively support human-AI co-creation.\u003c/p\u003e \u003cp\u003eAcross diverse disciplines where AI acts as a co-creator, research consistently shows that structured collaboration, facilitated through reflective dialogue and modular microtasks, is essential for effective human-AI work. When these structures balance AI\u0026rsquo;s generative strengths with human intentionality, AI enhances reflection and feasibility while humans contribute novelty, emotional depth, and contextual sensitivity, resulting in more coherent and authentic creative outcomes.\u003c/p\u003e \u003cp\u003eThese researchers argue for a robust foundation to support a framework that guides the human-AI co-creation process. AI can augment creativity when situated within structured processes, while providing a sandbox for human intention, novelty, and contextual sensemaking.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis project implements a human-AI co-creation system using background instructions as GPT system prompt, integrating the Dimensionality Framework (8Df) (Ch’ng, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2026b\u003c/span\u003e) as a knowledge base that aims to provide a dialogic step-by-step guide for individuals and small teams in designing VR experiences.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e3.1 LLMs and Models\u003c/h2\u003e \u003cp\u003eLLM Models have continually improved through the use of newer architectures, increased numbers of parameters, improved algorithms, and high-quality data (Saracco, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zorpette, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The present research evaluates three LLMs. The system prompt is embedded in the background via the following approaches:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eOpen AI’s ChatGPT - Custom GPT with GPT-4o and GPT-5\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGoogle’s Gemini - Gems with Gemini 2.5\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDeepSeek R1, copying and pasting the entire system prompt onto the chat window\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003ePrompt engineering was conducted across these three models in a cyclical process that involved approximately 10–15 code iterations to achieve consistent outputs across all three models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Persona Style and Format\u003c/h2\u003e \u003cp\u003eThe persona acts as a professional VR experience designer and Socratic partner who applies the Dimensionality Framework, guiding the user step-by-step through to a final synthesis. The solutions provided are purposeful, context-grounded, and explicitly aligned with value creation in the user’s domain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Purpose and Core Principles of AI\u003c/h2\u003e \u003cp\u003eThe process prioritises clarity and practical utility, generating concise deliverables that are immediately actionable:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eValue as Priority\u003c/b\u003e: All design decisions should be justified in relation to the user’s domain-specific objective, such as education, professional training, empathy building, or entertainment. Every prompt element demonstrably advances that goal.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBalance and trade-offs\u003c/b\u003e: Modulation choices must be made explicit and considered in relation to one another across dimensions; for example, introducing temporal non-linearity should be weighed against its potential to increase cognitive load, with rationale provided for the selected balance.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHuman-in-the-loop checkpoints\u003c/b\u003e: The workflow should incorporate a deliberate pause after each dimension to solicit explicit consent from the user, ensuring that users retain meaningful control over the scope and direction before proceeding.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eKnowledge-base\u003c/b\u003e: Definitions and explanations of Dimensionality Framework (DF) constructs should draw directly from the embedded knowledge base; when asked to define the 8Df, the response should name and align with the source article.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFlow resumption\u003c/b\u003e: When the process is temporarily diverted, e.g., to address micro tasks such as side questions or present tables, the system should return to the last active step and continue from that point, preserving coherence and continuity of the design process.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Human-AI Workflow\u003c/h2\u003e \u003cp\u003eThe human-AI workflow is designed as steps in the following order:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWelcome the user with a brief purpose of the D8f based on the knowledge base, and mention the article if a definition is asked.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEstablish the value of the VR experience by asking, “What is the primary goal of this VR experience (education, entertainment, empathy, training, other)?”\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eScenes: Ask the user to provide the number of scenes and a one-line description of the purpose for each scene. If no scenes were given, propose 3–5 scenes and explain how they balance information flow.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eConfirm readiness to begin with the first Dimension [DM-0], the Spatial Dimension.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eFor each dimension, the prompt asks whether the associated dimension table from the framework should be provided and whether brief, creative examples should be included. Other policies include utilising the ‘dials’ [DL], considering modulation [MOD] and the effects spectrum [EFF], as well as the information flow across the dimensions. At the end of each step, users are asked if there are any changes to any of the dials/modulation/effects before proceeding to the next dimension.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Constraints\u003c/h2\u003e \u003cp\u003eThe design process must respect the user’s constraints, ethical standards, and cultural sensitivities, ensuring that all interactions and representations are appropriate for the intended audience and context. Feasibility issues should be identified early with transparent options and trade-offs, and any uncertainty about the Dimensionality Framework should prompt clarification or clearly stated assumptions rather than unsupported claims.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Final Synthesis\u003c/h2\u003e \u003cp\u003eUpon explicit approval to proceed to DM-8, the Final Synthesis is presented as a structured package comprising nine components, delivered in the following order.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe Experience Goals Statement articulates the intended outcomes and value propositions of the VR project.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCore Concept summarises the central idea that integrates narrative, interaction, and system constraints.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe Project Timeline outlines the phased development, including hardware/software foundation, scene design, 3D modelling, interaction design, coding, integration, and developer testing, to ensure clear responsibilities and milestones.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDimension Value Alignment Table specifies, for each dimension, the core design choice, how that choice serves the project’s stated value, and any relevant conditional notes (“IF” cases) that qualify implementation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDimension Modulation and Effects Spectrum Table records, for the Spatial, Sensory, Placeness, Temporal, Cultural, Social, Cognitive, and Psychological dimensions, the selected modulations and the expected range of user-facing effects.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eVR Experience Design (VRED) Document Table provides concise entries for Objectives, Audience, Platforms, Mechanics, Content, Accessibility, Safety/Comfort, and Metrics, thereby translating design intent into implementation-ready specifications.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStory Mapping (Agile) Table organises Epics into scene-scoped User Stories with explicit Acceptance Criteria, enabling traceability from goals to testable outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFinal Statement concisely justifies the integrated design in relation to the value proposition and acknowledges key trade-offs made across dimensions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe final synthesis concludes with a Generate Storyboard prompt that asks whether to produce a visual layout map or a comic-style storyboard of the scenes for downstream communication and production. The visualisation depended on the ability of the model in use.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e "},{"header":"4.\tEvaluation","content":" \u003cp\u003eThe models being evaluated were ChatGPT (GPT-4o and GPT-5), Gemini (2.5), and DeepSeek (R1). The experimental Steps are listed below. Each step produces an output which is recorded in the Appendices.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eContextual details were asked between the three models with the phrase ‘What are the main foods in Quanzhou?’ and ‘What are the main foods in London?’\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEntry points were triggered with the ‘start’ keyword. Any initial triggers will lead the AI to output the initial steps, requesting the purpose of the VR experience design and scene proposals.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAfter scene insertion, the keywords from DM-0 to DM-9 were typed in, allowing the AI to traverse through all the steps programmed in the system prompt.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFinally, a ‘summary visual diagram’ and the ‘storyboard’ were asked for.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Contextual Details Between Models\u003c/h2\u003e \u003cp\u003eIn the process, it was discovered that different models output different ranges of cultural contextual details, all of which depend on the availability and quality of the data. This is an important awareness, because the framework structures the theory-grounded co-ideation; however, it is the details that fill in the structure. Table # and # is a simple demonstration of the culturally contextual details that each model is able to output (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Note that the maximum items produced for ‘London’ is ChatGPT at 46.67%, and when ‘Quanzhou’, a Chinese city, is asked, it produces only 28.57% of the total. Conversely, DeepSeek produces 50% for ‘Quanzhou’ and 35.55% for ‘London’. There are 5 overlaps between ChatGPT and DeepSeek for ‘Quanzhou’, and 7 for ‘London’. DeepSeek produced 9 additional items for ‘Quanzhou’, and ChatGPT produced 13 for ‘London’. More can be derived from each model with enhanced prompts, but the idea is that each model has cultural contexts, either from the ability to conduct Web searches or from what has been embedded in the model itself.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe output of “What are the main foods in Quanzhou” across ChatGPT, Gemini and DeepSeek. Overlaps in bold.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChatGPT 5 (28.57%)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGemini 2.5 (21.43%)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeepSeek R1 (50%)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMianxianhu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFried Oyster Omelette\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuanzhou Beef Briskets\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePeanut soup\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrozen Siphon Worm Jelly\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMian Xian Hu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRunbing (spring roll crêpe)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFine Noodle Soup\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTaiwanese Beef Noodle Soup\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOyster omelette\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePyramid-Shaped Rice Dumpling\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eOyster Omelette\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTusun-dong (sea-worm aspic)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGinger Duck\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFried Dough Wraps (Vinegar Marinated Pork)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFish rolls\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuanzhou Beef Steak\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRunbing (Fresh Spring Roll)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCu-rou (vinegar pork)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStuffed Tofu Puffs\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRoasted-meat zongzi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSteamed Fish with Pickled Mustard Greens\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStir-fried Razor Clams\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePeanut Soup\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eZongzi (Savory Sticky Rice Dumpling)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeat Dumplings (Pork/Fish)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSweet Taro Puree\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVarious \"Gui\" Rice Cakes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe output of “What are the main foods in London” across ChatGPT, Gemini and DeepSeek. Overlaps in bold.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChatGPT 5 (46.67%)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGemini 2.5 (17.78%)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeepSeek R1 (35.55%)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFull English breakfast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFish and Chips\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eFish \u0026amp; Chips\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFish \u0026amp; chips\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSunday Roast\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSunday Roast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePie \u0026amp; mash (with liquor)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFull English Breakfast\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eFull English Breakfast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBangers \u0026amp; mash\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePie and Mash\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePie \u0026amp; Mash\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSunday roast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBangers and Mash\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAfternoon Tea\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScotch egg\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChicken Tikka Masala\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eIndian Curry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeef Wellington\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfternoon Tea\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCaribbean Jerk Chicken\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChicken tikka masala / curries\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePuddings (e.g., Sticky Toffee)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTurkish Ocakbasi\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eJerk chicken\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eChinese Dim Sum \u0026amp; Roasts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDöner/shish kebab\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGastropub Food\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalt beef (beigel)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStreet Food Markets\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDim sum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModern British Cuisine\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFalafel wrap\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe \"Greasy Spoon\" Cafe\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePho/ramen\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA Pint in a Pub\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSourdough pizza by the slice\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGin \u0026amp; Tonic\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBao\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA Cuppa (Tea)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHalloumi fries\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePies \u0026amp; pasties\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScones with jam \u0026amp; clotted cream\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSticky toffee pudding\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEton mess / Victoria sponge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Entry Point\u003c/h2\u003e \u003cp\u003eUsing the same entry prompt ‘start’ across the three LLMs demonstrates similarities across all three models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Gemini seems to think longer (16 seconds) and producing 74 words that communicate the exact meaning needed to start the process, compared to 2 seconds (84 words) for ChatGPT, and 9 seconds (199 words) for DeepSeek.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons of Entry Point messages.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChatGPT 5-Thinking\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGemini 2.5\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeepSeek R1 (Deep Think)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHi! I’m your VR design partner using the Dimensionality Framework (DF) to turn your goals into an actionable VR experience. DF balances eight dimensions—Spatial, Sensory, Placeness, Temporal, Cultural, Social, Cognitive, Psychological—to align design choices with user value, grounded in the DF knowledge base and the “Beyond Presence” perspective.\u003c/p\u003e \u003cp\u003eWhat is the primary goal (education, entertainment, empathy, training, or other)?\u003c/p\u003e \u003cp\u003eHow many scenes and what’s each about? If you prefer, I can suggest 3–5 starter scenes.\u003c/p\u003e \u003cp\u003eReady to begin with\u0026nbsp;\u003cb\u003eDM-0 Spatial\u003c/b\u003e?\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWelcome. I am a VR Experience Designer using the Dimensionality Framework to co-create purposeful, balanced experiences across eight key dimensions, grounded in the DF knowledge base from \"Beyond Presence.\"\u003c/p\u003e \u003cp\u003eWhat is the primary goal for this VR experience (e.g., education, training, empathy, entertainment)?\u003c/p\u003e \u003cp\u003eHow many scenes do you envision, and what is the one-line purpose of each? If you prefer, I can suggest 3–5 starter scenes that balance information flow.\u003c/p\u003e \u003cp\u003eReady to begin with\u0026nbsp;\u003cb\u003eDM-0 Spatial\u003c/b\u003e?\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHello! I'm your Socratic design partner, here to help you co-design a powerful VR experience. Using the Dimensionality Framework (DF), we'll balance eight key dimensions to transform your goal into an actionable, value-driven design. This structured approach, grounded in the principles of the \"Beyond Presence\" article, ensures every choice we make is purposeful and effective.\u003c/p\u003e \u003cp\u003eFirst, what is the primary\u0026nbsp;\u003cb\u003egoal\u003c/b\u003e\u0026nbsp;of this VR experience? Is it for education, entertainment, empathy, training, or something else?\u003c/p\u003e \u003cp\u003eNext, how many\u0026nbsp;\u003cb\u003escenes\u003c/b\u003e\u0026nbsp;do you envision, and what is the one-line purpose for each? If you're not sure, I can suggest 3–5 starter scenes to help structure the experience.\u003c/p\u003e \u003cp\u003eOnce we have that foundation, are you ready to begin with\u0026nbsp;\u003cb\u003eDM-0 Spatial\u003c/b\u003e?\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Scene Suggestions\u003c/h2\u003e \u003cp\u003eTesting the prompt ‘My VR project tells the story of a migrant who spends two decades building a life in the hustle of London, only to find that their true journey ends with their emotional return to the ancient city of Quanzhou. The core purpose of the experience is to teach the complex nature of 'home' and 'identity' through personal narrative. Suggest 5 scenes.’\u003c/p\u003e \u003cp\u003eThe first prompt is followed by a second prompt that inserts another scene into the experience, ‘Include an early scene depicting London’s cuisine, the dishes you have enjoyed over the past twenty years, recollections of your grandma’s kitchen, and the street food of Quanzhou as you prepare to return as a migrant. List all the food available in each city.’ See Tables \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e–\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e for details.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 ChatGPT Scene Co-Creation\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe scene co-creation with ChatGPT took 16 seconds, generating 171 words.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeautiful brief—thank you. Your core value reads as\u0026nbsp;\u003cb\u003eeducation + empathy\u003c/b\u003e: teaching the layered meaning of “home” and “identity” through a lived narrative.\u003c/p\u003e \u003cp\u003eHere are\u0026nbsp;\u003cb\u003e5 scenes\u003c/b\u003e\u0026nbsp;that balance information flow (arousal, tempo, cognitive load) and build to the Quanzhou return:\u003c/p\u003e \u003cp\u003e1. \u003cb\u003eArrivals: Thresholds in Transit\u003c/b\u003e\u0026nbsp;— Heathrow to a cramped bedsit; bureaucratic maze and night bus glide. Purpose: dislocation + aspiration primer.\u003c/p\u003e \u003cp\u003e2. \u003cb\u003eHustle Years: City as Treadmill\u003c/b\u003e\u0026nbsp;— Time-compressed decade of gigs, markets, side-hustles; maps morph with commute lines. Purpose: labor, resilience, belonging-seeking.\u003c/p\u003e \u003cp\u003e3. \u003cb\u003ePatchwork Homes\u003c/b\u003e\u0026nbsp;— Karaoke basement, temple courtyard, late-night kitchens, WhatsApp calls; London↔Quanzhou audio ghosts. Purpose: plurilocal “home,” cultural micro-anchors.\u003c/p\u003e \u003cp\u003e4. \u003cb\u003eFracture \u0026amp; Superposition\u003c/b\u003e\u0026nbsp;— A crisis (visa/parental health) overlays Quanzhou streets onto London in real-time. Purpose: identity tension, memory-place blending.\u003c/p\u003e \u003cp\u003e5. \u003cb\u003eQuanzhou Return: Slow Time\u003c/b\u003e\u0026nbsp;— Stone alleys, incense, sea breeze; a door-threshold choice reframes home as a network, not a node. Purpose: emotional resolution, reframing.\u003c/p\u003e \u003cp\u003eReady to begin with\u0026nbsp;\u003cb\u003eDM-0 Spatial\u003c/b\u003e? (I can also show a compact\u0026nbsp;\u003cb\u003e[DM-0] table\u003c/b\u003e\u0026nbsp;of dials → modulations → effects if you like.)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e25 seconds, ChatGPT inserted food memories in Scene 2 after the second prompt requesting for food items.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLove that—food is a perfect bridge between places and selves. I’ll insert this as\u0026nbsp;\u003cb\u003eScene 2: “Edible Map of Belonging.”\u003c/b\u003eYou drift through a floating night market where London stalls morph into Quanzhou lanes; aromas trigger voice-notes from your past, and a prep table becomes your grandma’s kitchen altar.\u003c/p\u003e \u003cp\u003eHere’s a\u0026nbsp;\u003cb\u003ecurated, non-exhaustive\u003c/b\u003e\u0026nbsp;food list to script and model:\u003c/p\u003e \u003cp\u003e\u003cb\u003eLondon (20 years of eating)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e• \u003cb\u003eBritish mains\u003c/b\u003e:\u0026nbsp;Fish \u0026amp; chips; Sunday roast; steak \u0026amp; ale pie; Cornish pasty; full English; bangers \u0026amp; mash; shepherd’s/cottage pie.\u003c/p\u003e \u003cp\u003e• \u003cb\u003ePub/comfort\u003c/b\u003e:\u0026nbsp;Scotch egg; sausage roll; pork pie; ploughman’s; sticky toffee pudding; Eton mess.\u003c/p\u003e \u003cp\u003e• \u003cb\u003e“London classics” (diaspora staples)\u003c/b\u003e:\u0026nbsp;Chicken tikka masala; jalfrezi; dosa; biryani; salt beef bagel; shawarma; doner kebab; falafel wrap; jerk chicken; jollof with suya; pho; ramen; banh mi; bao; dim sum; sushi boxes; bibimbap; Ethiopian injera platters; Turkish gözleme; Persian kebabs; Greek gyros; Polish pierogi; Georgian khachapuri.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eStreet food/markets\u003c/b\u003e:\u0026nbsp;Sourdough grilled cheese; paella pan; arepas; tacos; churros; arancini; gnocchi boxes; Lebanese manakish; Sri Lankan kottu; Afghan mantu; Burmese tea leaf salad; vegan burgers; specialty coffee + pastéis de nata.\u003c/p\u003e \u003cp\u003e\u003cb\u003eQuanzhou (Minnan/Fujian street foods \u0026amp; specialties)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e• \u003cb\u003eNoodles \u0026amp; soups:\u0026nbsp;Shacha noodles (沙茶面)\u003c/b\u003e;\u0026nbsp;\u003cb\u003eoyster vermicelli\u003c/b\u003e;\u0026nbsp;\u003cb\u003elor mee/lu mian (卤面)\u003c/b\u003e\u0026nbsp;(Minnan gravy noodles);\u0026nbsp;\u003cb\u003ebeef brisket noodles\u003c/b\u003e;\u0026nbsp;\u003cb\u003eseafood congee\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eSea \u0026amp; shore:\u0026nbsp;Oyster omelette (海蛎煎)\u003c/b\u003e;\u0026nbsp;\u003cb\u003esea-worm jelly (土笋冻)\u003c/b\u003e; dried cuttlefish skewers; small fried fish; clams with basil/garlic.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eSnacks \u0026amp; breads:\u0026nbsp;Five-spice rolls (五香卷/卤卷)\u003c/b\u003e;\u0026nbsp;\u003cb\u003emeat zongzi (肉粽)\u003c/b\u003e; peanut brittle; sesame cakes; taro cakes; fried taro dumplings.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eSweet/comfort:\u0026nbsp;Peanut soup (花生汤)\u003c/b\u003e;\u0026nbsp;\u003cb\u003etaro paste dessert (芋泥/Orh Nee)\u003c/b\u003e; fermented rice sweet soup (酒酿/糯米酒).\u003c/p\u003e \u003cp\u003e• \u003cb\u003eSauces \u0026amp; signatures:\u0026nbsp;Shacha (沙茶) sauce\u003c/b\u003e; red yeast rice marinades (红糟); garlic-vinegar dips; chili oil with dried shrimp.\u003c/p\u003e \u003cp\u003e\u003cb\u003eGrandma’s Kitchen (memory anchors)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e• \u003cb\u003eStaples\u003c/b\u003e:\u0026nbsp;Lard-scallion oil; red yeast rice\u0026nbsp;\u003cb\u003ehong zao\u003c/b\u003e; dried shrimp; dried squid; shiitake; taro; sweet potato leaves; mung beans; rice vinegar.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eDishes\u003c/b\u003e:\u0026nbsp;Red-braised pork with\u0026nbsp;\u003cb\u003ehong zao\u003c/b\u003e; taro rice; hand-pulled noodles with scallion oil; steamed fish with ginger-spring onion; oyster omelette; seaweed-egg drop soup; peanut sweet soup; five-spice rolls at festivals; meat\u0026nbsp;\u003cb\u003ezongzi\u003c/b\u003ewrapped with bamboo leaves.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eRitual cues\u003c/b\u003e:\u0026nbsp;Incense + ginger steam; cleaver rhythm; enamel bowls; bamboo steamer hiss; the “taste test” spoon.\u003c/p\u003e \u003cp\u003eWant me to fold this into the scene flow like this?\u003c/p\u003e \u003cp\u003e1. Arrivals\u003c/p\u003e \u003cp\u003e2. \u003cb\u003eEdible Map of Belonging (new)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e3. Hustle Years\u003c/p\u003e \u003cp\u003e4. Fracture \u0026amp; Superposition\u003c/p\u003e \u003cp\u003e5. Quanzhou Return\u003c/p\u003e \u003cp\u003eReady to begin\u0026nbsp;\u003cb\u003eDM-0 Spatial\u003c/b\u003e\u0026nbsp;to shape how this food scene plays (scale, locomotion, interaction)? I can also show a compact\u0026nbsp;\u003cb\u003e[DM-0] table\u003c/b\u003e.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Gemini Scene Co-Creation\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe scene co-creation with Gemini took 18 seconds, generating 223 words.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent. The goal is to use a personal narrative to explore the complex themes of 'home' and 'identity'. This is a powerful goal for VR, blending empathy with education.\u003c/p\u003e \u003cp\u003eBased on your narrative arc, here is a proposed five-scene structure designed to create a strong emotional journey:\u003c/p\u003e \u003cp\u003e• \u003cb\u003eScene 1: London Arrival – The Overwhelm.\u003c/b\u003e\u0026nbsp;\u003cem\u003ePurpose\u003c/em\u003e:\u0026nbsp;To establish the initial sense of displacement and sensory overload in a new, foreign environment.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eScene 2: London Life – The Constructed Home.\u003c/b\u003e\u0026nbsp;\u003cem\u003ePurpose\u003c/em\u003e:\u0026nbsp;To show the passage of time and the creation of a new, but perhaps incomplete, identity amidst the city's rhythm.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eScene 3: London Reflection – The Catalyst.\u003c/b\u003e\u0026nbsp;\u003cem\u003ePurpose\u003c/em\u003e:\u0026nbsp;To introduce the emotional turning point where the longing for the original home surfaces, triggered by a small object or memory.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eScene 4: The Transition – A Journey Between Worlds.\u003c/b\u003e\u0026nbsp;\u003cem\u003ePurpose\u003c/em\u003e:\u0026nbsp;To bridge the two locations, using an abstract or semi-real space (like a dreamlike train ride) to build emotional anticipation.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eScene 5: Quanzhou Homecoming – The Reconnection.\u003c/b\u003e\u0026nbsp;\u003cem\u003ePurpose\u003c/em\u003e:\u0026nbsp;To deliver the emotional climax and resolve the central theme as the user rediscovers the sights, sounds, and feelings of their ancestral home.\u003c/p\u003e \u003cp\u003eThis five-act structure allows us to build the contrast between the two worlds and guide the user on a clear emotional journey.\u003c/p\u003e \u003cp\u003eDoes this scene structure work for you? If so, shall we proceed to the\u0026nbsp;\u003cb\u003eDM-0 Spatial Dimension\u003c/b\u003e?\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e27 seconds, Gemini inserted food memories in Scene 2 after the second prompt requesting for food items.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThat's a fantastic idea. Using food is a powerful sensory anchor for memory and identity, perfectly aligning with your goal. It can bridge the gap between the two worlds in a very visceral way.\u003c/p\u003e \u003cp\u003eLet's integrate this by modifying our scene structure, specifically making the food connection the emotional heart of the experience.\u003c/p\u003e \u003cp\u003eHere is the revised scene plan:\u003c/p\u003e \u003cp\u003e• \u003cb\u003eScene 1: London Arrival – The Overwhelm.\u003c/b\u003e\u0026nbsp;(No change)\u003c/p\u003e \u003cp\u003e• \u003cb\u003eScene 2: A London Kitchen – The Culinary Mix.\u003c/b\u003e\u0026nbsp;\u003cem\u003eNew Purpose\u003c/em\u003e:\u0026nbsp;The user prepares a meal in their London flat. The kitchen is a blend of cultures—English cookbooks next to a familiar wok. This scene establishes their adapted life through food.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eScene 3: A Bite of Memory – The Catalyst.\u003c/b\u003e\u0026nbsp;\u003cem\u003eRevised Purpose\u003c/em\u003e:\u0026nbsp;While eating the London meal, a specific taste or smell triggers a vivid, sensory flashback to their grandma’s kitchen in Quanzhou, creating an emotional dissonance and sparking the desire to return.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eScene 4: The Transition – A Journey Between Worlds.\u003c/b\u003e\u0026nbsp;(No change)\u003c/p\u003e \u003cp\u003e• \u003cb\u003eScene 5: Quanzhou Homecoming – The Taste of Belonging.\u003c/b\u003e\u0026nbsp;\u003cem\u003eRevised Purpose\u003c/em\u003e:\u0026nbsp;The emotional climax is now tied to tasting authentic Quanzhou street food, fully reconnecting the user to their roots and resolving the search for 'home'.\u003c/p\u003e \u003cp\u003eTo populate these scenes, here are some proposed dishes that could represent the character's 20-year culinary journey, contrasting the adopted home with the ancestral one:\u003c/p\u003e \u003cp\u003e\u003cb\u003eLondon: The Global, Adopted Palate\u003c/b\u003e\u0026nbsp;This list reflects a life built in a multicultural metropolis.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eClassic British\u003c/b\u003e:\u0026nbsp;A hearty Sunday Roast, Fish and Chips from a local chippy.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eLondon's Melting Pot\u003c/b\u003e:\u0026nbsp;A comforting Chicken Tikka Masala, a late-night Doner Kebab, a bowl of Vietnamese Pho from a Kingsland Road cafe.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eRecreations of Home\u003c/b\u003e:\u0026nbsp;A slightly \"off\" version of a familiar dish, made with supermarket ingredients that don't taste quite right, symbolizing the feeling of being an imitation.\u003c/p\u003e \u003cp\u003e\u003cb\u003eQuanzhou: The Nostalgic, Ancestral Flavors\u003c/b\u003e\u0026nbsp;This list is specific and rooted in childhood memory.\u003c/p\u003e \u003cp\u003e• \u003cb\u003eGrandma’s Kitchen\u003c/b\u003e:\u0026nbsp;The smell and taste of Ginger Duck (姜母鸭), handmade Meat Dumplings (肉粽 - Zongzi).\u003c/p\u003e \u003cp\u003e• \u003cb\u003eStreet Food Staples\u003c/b\u003e:\u0026nbsp;The sizzle of an Oyster Omelette (蚵仔煎), the unique texture of Earthworm Jelly (土笋冻), the sweetness of Peanut Soup (花生汤).\u003c/p\u003e \u003cp\u003eDoes this revised, food-centric scene structure and the proposed culinary palettes resonate with your vision?\u003c/p\u003e \u003cp\u003eIf you're happy with this direction, we can proceed to the\u0026nbsp;\u003cb\u003eDM-0 Spatial Dimension\u003c/b\u003e.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 DeepSeek Scene Co-Creation\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe scene co-creation with DeepSeek took 11 seconds, generating 320 words.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent. This is a powerful narrative with a clear, empathetic goal. Exploring \"home\" and \"identity\" through this journey is a perfect fit for VR's strengths.\u003c/p\u003e \u003cp\u003eBased on your premise, here are 5 suggested scenes designed to create a balanced emotional and narrative arc, moving from external hustle to internal reflection:\u003c/p\u003e \u003cp\u003e1. \u003cb\u003eScene 1: The Hustle (London)\u003c/b\u003e\u0026nbsp;- Purpose: To immerse the user in the overwhelming, fast-paced anonymity of migrant life in London, establishing the initial setting of \"striving.\"\u003c/p\u003e \u003cp\u003e2. \u003cb\u003eScene 2: The Flat (London)\u003c/b\u003e\u0026nbsp;- Purpose: To contrast the public hustle with a private, personal space, filled with small tokens of memory that hint at the character's roots and inner conflict.\u003c/p\u003e \u003cp\u003e3. \u003cb\u003eScene 3: The Letter / Call (Transition)\u003c/b\u003e\u0026nbsp;- Purpose: To serve as the narrative catalyst—the moment that triggers the decision to return, blending the sounds of London with news from Quanzhou.\u003c/p\u003e \u003cp\u003e4. \u003cb\u003eScene 4: The Return (Quanzhou)\u003c/b\u003e\u0026nbsp;- Purpose: To create a sensory and emotional contrast upon arrival in Quanzhou, overwhelming the user with familiar sounds, scents, and atmosphere, evoking a deep sense of \"return.\"\u003c/p\u003e \u003cp\u003e5. \u003cb\u003eScene 5: The Ancestral Temple (Quanzhou)\u003c/b\u003e\u0026nbsp;- Purpose: To be the emotional climax where the themes of home and identity resolve; a quiet, profound moment of connection with history and family roots.\u003c/p\u003e \u003cp\u003eThese scenes are designed to balance\u0026nbsp;\u003cb\u003e[IF] Information Flow\u003c/b\u003e, shifting between the sensorimotor engagement of the bustling cities (weak representation) and the cognitive, reflective moments in the flat and temple (strong representation).\u003c/p\u003e \u003cp\u003eShall we proceed to\u0026nbsp;\u003cb\u003eDM-0 Spatial\u003c/b\u003e\u0026nbsp;to define the foundational feel of movement and interaction in these scenes?\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e27 seconds, DeepSeek integrated food memories in Scenes 1, 2 and 4 after the second prompt requesting for food items.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"1\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThis is a beautiful addition. We'll integrate a scene focused on food memory, a powerful anchor for identity. Here are the proposed 5 scenes, revised to include your request, and the food lists that will serve as sensory anchors.\u003c/p\u003e \u003cp\u003e\u003cb\u003eRevised Scenes \u0026amp; Food Lists\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e1. \u003cb\u003eScene 1: The Hustle \u0026amp; The Takeaway (London)\u003c/b\u003e\u0026nbsp;- Purpose: To immerse the user in the routine of migrant life, using the familiar tastes of London's adopted cuisine to trigger a flash of memory, contrasting the public present with a private past.\u003c/p\u003e \u003cp\u003eo \u003cb\u003eLondon Foods\u003c/b\u003e:\u0026nbsp;Fish \u0026amp; Chips, Chicken Tikka Masala, a Sunday Roast dinner, a packed meal deal (sandwich, crisps, drink).\u003c/p\u003e \u003cp\u003e2. \u003cb\u003eScene 2: The Flat \u0026amp; The Memory (London)\u003c/b\u003e\u0026nbsp;- Purpose: To deepen the internal conflict. The user prepares a simple London dish in their kitchen, which triggers a vivid, multi-sensory memory of their grandmother's kitchen and the street food of Quanzhou.\u003c/p\u003e \u003cp\u003eo \u003cb\u003eQuanzhou Memory Foods\u003c/b\u003e:\u0026nbsp;Grandma's\u0026nbsp;\u003cem\u003eHong Shao Rou\u003c/em\u003e\u0026nbsp;(Braised Pork Belly), the scent of\u0026nbsp;\u003cem\u003eOyster Omelette\u003c/em\u003e\u0026nbsp;from a night market, a bowl of\u0026nbsp;\u003cem\u003eTuyang Tou\u003c/em\u003e(noodle soup), the sweet taste of\u0026nbsp;\u003cem\u003eMalay Cake\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e3. \u003cb\u003eScene 3: The Decision (Transition)\u003c/b\u003e\u0026nbsp;- Purpose: The narrative catalyst. The user receives a letter or call against the backdrop of a London rain shower, the news from Quanzhou making the London foods suddenly feel alien and the memories more urgent.\u003c/p\u003e \u003cp\u003e4. \u003cb\u003eScene 4: The Return (Quanzhou)\u003c/b\u003e\u0026nbsp;- Purpose: To overwhelm the user with the immediate, visceral sensory reality of return—the sounds, the humidity, and the unmistakable street food aromas that define \"home.\"\u003c/p\u003e \u003cp\u003eo \u003cb\u003eQuanzhou Street Foods\u003c/b\u003e:\u0026nbsp;Sizzling\u0026nbsp;\u003cem\u003eOyster Omelettes\u003c/em\u003e, steaming\u0026nbsp;\u003cem\u003eBaozi\u003c/em\u003e\u0026nbsp;(buns), fried\u0026nbsp;\u003cem\u003eZongzi\u003c/em\u003e, the sight of fresh\u0026nbsp;\u003cem\u003eLychee\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e5. \u003cb\u003eScene 5: The Ancestral Temple (Quanzhou)\u003c/b\u003e\u0026nbsp;- Purpose: The emotional climax. A quiet moment of connection, perhaps leaving an offering of food, resolving the journey and solidifying the new, integrated identity.\u003c/p\u003e \u003cp\u003eThis structure uses food as a primary vehicle for\u0026nbsp;\u003cb\u003e[IF] Information Flow\u003c/b\u003e, creating a direct sensory pathway from the present (London tastes) to the past (Quanzhou memories) and back again, powerfully evoking the theme of identity.\u003c/p\u003e \u003cp\u003eWith this foundation, are you ready to begin defining the\u0026nbsp;\u003cb\u003eDM-0 Spatial\u003c/b\u003e\u0026nbsp;dimension—how the user will move and interact within these memory-laden spaces?\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Comparing Co-Creation of Dimensions for Dialogues\u003c/h2\u003e \u003cp\u003eHere, we evaluate the outputs of the dimensions. From the previous prompt, a simple instruction ‘DM-#’ (0–9) were given in each step, from DM-0 (Spatial Dimension) to DM-7 (Psychological Dimension), allowing each model to output standard prompts outlining in an educational way, what each dimension provides in terms of ‘dials’, ‘modulation’, and ‘effects’. Some models provide choices, and suggestions in others.\u003c/p\u003e \u003cp\u003eThe full outputs of the AI decoded dimensions (DM-0 to DM-7) for the five scenarios are shown in the Appendices (see Appendix: [keyword] Dimensions table DM-#).\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Guidance Based on Theoretical Groundings\u003c/h2\u003e \u003cp\u003eIt can be observed that the output styles between each DM-# remain the same, but each model optimises and emphasises the questions and dials, modulation, and effects in different ways. Even though the models used the same system prompt, there is a marked difference between the three models and their co-ideation and co-creation approaches.\u003c/p\u003e \u003cp\u003eFor example, in the spatial dimension (DM-0),\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eChatGPT employs a prescriptive approach, proposing a default blend of teleportation and slow guided drift. This choice is explicitly framed within its ‘dials’ [DL] → ‘modulation’ [MOD] → ‘effects’ [EFF] support and is supplemented with concrete efficient presets, including a 45° snap-turn and a seated/standing toggle.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGemini adopts more facilitation, putting focused questions to clarify the user intent with regards to locomotion choices such as ‘room-scale walking’ versus ‘teleportation’ without committing to a specific methodology or providing concrete suggestions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDeepSeek proposes a principled hybrid model, committing to a specific approach based on narrative context. It recommends natural, room-scale locomotion for intimate scenes to enhance embodiment, while prescribing artificial locomotion for larger environments to manage scale and pacing.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eIs the model providing logical and reasonable design choices?\u003c/p\u003e \u003cp\u003eChatGPT suggests logical and reasonable proposals, taking care of user motion sickness through two modes of locomotion (teleport, slow drift), in 1:1 scale, and caring for psychological effects such as memory emphasis with 1:3 to 1:6 scaling. In interaction modes, it provides a Grab/Place + user-proffered cooking verbs for the kitchen, psychological effects that modulate ties of food to place and self were offered. Appropriate isomorphic interaction modes, such as toggles for seat and stand, and snap-turns, were given for broad accessibility.\u003c/p\u003e \u003cp\u003eGemini offered educated choices that are logical, without providing suggestions. For example, in comparison in terms of location, Gemini’s output was posted as a question: “Movement \u0026amp; Locomotion: How should the user navigate the busy London streets and the ancient alleys of Quanzhou? Should we prioritise immersion with natural, room-scale walking, or comfort and accessibility with methods like teleportation or smooth joystick movement?” and “Interaction \u0026amp; Embodiment: When interacting with food—chopping vegetables in the London kitchen or picking up a street food skewer—should this be a direct, physics-based interaction using tracked hands for a high sense of touch and presence? Or a simpler, more streamlined symbolic interaction (e.g., point-and-click) to focus on the narrative?”\u003c/p\u003e \u003cp\u003eDeepSeek offered the background context, the reasoning behind the choices. Outputs, for example, contain the context of the proposal, “Excellent. Let's begin by defining the Spatial Dimension (DM-0), the technical foundation for immersion and the feeling of ‘being there’ in these memory-rich environments. For a narrative about identity and place, spatial design is crucial. Do you want the user to feel like a ghost observing memories, or an active participant recreating them? The choices here set the stage for the entire experience.” It then offered the ‘dials’ that you can use for modulating user effects. For example, the first two guidelines were:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eSPA2: Direct 1:1 Mapping vs. Altered Mapping: Should the user's physical steps exactly match their virtual steps (ideal for the intimate flat scene), or should we use techniques like teleportation to cover the larger scale of London and Quanzhou streets?\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003e♣ Example\u003c/strong\u003e \u003c/p\u003e\u003cp\u003e♣ We could use 1:1 mapping in the kitchen for precise interaction with food, but switched to altered mapping for navigating the bustling market.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eSPA3: Natural Locomotion vs. Artificial Locomotion: How should the user move through these spaces? Physical walking (natural) is the most immersive but also the most limiting. Teleportation or joystick movement (artificial) allows for larger scenes but can reduce the sense of embodiment.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsideration\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThe emotional weight of \"returning\" to Quanzhou might be stronger if the user physically walks through the ancestral temple gate.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eSimilar to ChatGPT, DeepSeek then proposes the choices that it would use for locomotion, interaction, embodiment, after which it outputs a suggestion of what sorts of effect it might have on the users “The anticipated [EFF] Effects are a strong sense of \"place\" in key locations, with intuitive and meaningful interaction with objects central to the theme of identity.”:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e[DL] Locomotion: Use [MOD] Artificial Locomotion (teleportation) for large-scale scenes (streets) to maintain pacing, but [MOD] Natural Locomotion (room-scale) for key intimate moments (the flat, the temple) to enhance reflection.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e[DL] Interaction: Use [MOD] Embodied Interaction (hand-tracking) for all food-related actions to maximize the sensory and mnemonic connection.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e[DL] Embodiment: Use a [MOD] Disembodied Presence (floating hands) to keep the focus on the environment and the protagonist's story, rather than the user's own avatar.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eAccurate guidance that is based on theoretical groundings is important as the spatial dimension is the basis of the sense of presence, and the entire existence of the experience is based on the sensorimotor fidelity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Cultural Sensitivity and Awareness\u003c/h2\u003e \u003cp\u003eStyle differences are obvious in the cultural dimension. The structure of each model’s output is similar across all dimensions; the content becomes important. In particular, how each model is able to map appropriate details to the particular dimensions becomes critical. The cultural dimension is suited for this evaluation.\u003c/p\u003e \u003cp\u003eChatGPT offers contents that allow beginners to remove guesswork in terms of cultural awareness. For intermediate and professional users, the suggestion becomes content that can be passed to the production team. Examples of the details of the outputs are:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eLanguage policy, ready to implement: proportion of English + Minnan \u0026amp; Mandarin language subtitles, with captions provided by default.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRitual portrayal rules: “Show, don’t explain; consented filming of incense/offerings; no gamification of sacred acts.”\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNarrative workflow: Composite protagonist; anonymise sensitive details; consent forms for real names/likenesses.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCommunity reciprocity: Name vendors/locations with permission; in-experience credits board and links to community organisations.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLocalisation: Caption presets (EN / ZH-Simplified / ZH-Traditional), color-coded speakers, glossary tooltips (“shacha, hong zao, orh nee”).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRepresentations: Avoid monolithic tropes; show intra-diaspora diversity (Fujianese vs “Chinese London”).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eGemini provides a facilitator role, by handling the proposals to the designer. Designers who do not have cultural awareness might find this challenging. Examples of the details of the outputs are:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eCultural specificity: asks whether to go native-level detail (Minnan phrases, local folklore) or broadly Chinese for accessibility.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSocial norms by city: polite/distant London vs communal Quanzhou (AI characters greet/interact).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDelivery mode: explicit narration/labels vs implicit environmental storytelling.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAdaptation over time: early UI helpers/audio cues (e.g., navigating the Tube) that fade as the protagonist acculturates.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eDeepSeek is useful for beginners, intermediate or professional teams that requires clear guidance on the cultural dimension. It provides the cultural ‘dials’ that are used for modulating experiences (CUL1–CUL4):\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eCulturally Specific ↔ Neutral (argues for high specificity: anchor London in Newham/Hackney; Quanzhou in its historic quarter).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFamiliarity ↔ Novelty (design for insiders and newcomers without diluting authenticity).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEmbedded ↔ Open Social Norms (guide users to remove shoes, bow, offers tea etiquette as embodied learning).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eExplicit ↔ Implicit Narrative (implicit-first with optional labels for accessibility).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSynthesis: authentic architecture/signage/ambient dialogue; etiquette turned into subtle, guided actions; optional translatable labels on historical objects.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eDeepSeek also offers concrete examples:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eIt is the only model to name districts (Newham/Hackney, Quanzhou historic quarter) and to specify etiquette as prescribed interactions (remove shoes; moment of silence; offering ritual).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIt chooses implicit-first storytelling but keeps optional explicit labels. It is midway between ChatGPT’s “show, don’t explain” rule and Gemini’s open question.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 Summary Style Differences Between Models\u003c/h2\u003e \u003cp\u003eChatGPT functions as an off-the-shelf human-AI toolkit, offering production-ready scaffolds across dimensions: rapid spatial setups, actionable placeness blueprints, beat-mapped temporal plans, and psychologically structured emotional arcs with safety features, making it the fastest path from concept to launch despite lighter theoretical grounding.\u003c/p\u003e \u003cp\u003eGemini, by contrast, excels at front-end alignment: it asks targeted questions to establish intent in spatial design, turns space into place through ambience and symbolism prompts, builds shared pacing vocabularies without fixing durations, and clarifies emotional theses, ideal for consensus-building rather than technical specification.\u003c/p\u003e \u003cp\u003eDeepSeek contributes formal justification through dial-based taxonomies: spatial choices are explicitly tied to narrative function, placeness tones are engineered through controllable dials, temporal structures blend linear arcs with triggered memories, and psychological dials shape nuanced emotional journeys; however, it avoids committing to concrete parameters and thus benefits from pairing with ChatGPT for operational details.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Final Syntheses\u003c/h2\u003e \u003cp\u003eThe final synthesis outputs design choices presented as tables that designers will find useful. We will only evaluate the Experience Goals, Core Concepts, Dimension Value Alignment Table, Story Mapping Table, and Visualisation (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e–\u003cspan refid=\"Tab17\" class=\"InternalRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.5.1 Experience Goals and Core Concept\u003c/h2\u003e \u003cp\u003eUpon reviewing the textual outputs (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e), it appears that each model is distinct. For example, ChatGPT offers a pragmatic designer role, focusing on concrete user actions and game mechanics (\"Edible Timeline,\" \"steam portals\") for a development-oriented audience. Gemini is an emotive storyteller, prioritising the character's emotional journey and narrative theme (\"home is a feeling\") to appeal to a creative director or artistic panel. DeepSeek, on the other hand, acts like a media theorist, using academic language (\"weak vs. strong representation\") to analyse the project's structure and its place within the medium of VR for a research audience.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOutputs of the DM-8 final synthesis showing the differences between each model’s output as Experience Goals Statement, and Core Concept.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChatGPT\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGemini\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeepSeek\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1) Experience Goals Statement\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTeach the complex, plural nature of\u0026nbsp;\u003cb\u003ehome\u003c/b\u003e\u0026nbsp;and\u0026nbsp;\u003cb\u003eidentity\u003c/b\u003e\u0026nbsp;through an embodied migrant narrative spanning London to Quanzhou, optimizing for\u0026nbsp;\u003cb\u003eeducation + empathy\u003c/b\u003e\u0026nbsp;with high accessibility and cultural care.\u003c/p\u003e \u003cp\u003e\u003cb\u003e2) Core Concept\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA five-scene VR journey where\u0026nbsp;\u003cb\u003efood\u003c/b\u003e\u0026nbsp;is the bridge: you assemble an\u0026nbsp;\u003cb\u003eEdible Timeline\u003c/b\u003e\u0026nbsp;(London dish → Grandma memory → Quanzhou dish), traverse micro-places through\u0026nbsp;\u003cb\u003esteam portals\u003c/b\u003e, and author a brief “\u003cb\u003ehome is…\u003c/b\u003e” reflection that persists on a lantern wall.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1. Experience Goals Statement\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo create a deeply empathetic and educational VR narrative that allows users to experience a migrant's 20-year journey. The primary goal is to explore the complex, personal, and fluid nature of 'home' and 'identity' by contrasting the constructed life in a new country with the profound emotional pull of an ancestral homeland.\u003c/p\u003e \u003cp\u003e\u003cb\u003e2. Core Concept\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\"Two Kitchens\" is a first-person narrative experience following a migrant who builds a life in the multicultural hustle of London, only to find their sense of self reawakened by a single, powerful food memory. The user viscerally experiences the character's journey of adaptation, reflection, and eventual return to the ancient city of Quanzhou, discovering that 'home' is ultimately a feeling, not just a place.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1. Experience Goals Statement\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe primary goal of this VR experience is to teach the complex nature of 'home' and 'identity' through the personal narrative of a migrant who spends two decades in London before returning to their ancestral city of Quanzhou. It aims to evoke empathy and understanding by immersing the user in the sensory, cultural, and emotional journey of the protagonist, using food and memory as central motifs.\u003c/p\u003e \u003cp\u003e\u003cb\u003e2. Core Concept\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\"Homecoming\" is a linear narrative with non-linear memory access, where the user moves through five key scenes. The experience uses embodied interactions with food and culturally prescribed rituals to create a deep sense of place and personal transformation. The core concept is to use VR's unique ability to blend sensory immersion (weak representation) with narrative reflection (strong representation) to explore the duality of identity.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.5.2 Project Timeline Suggestions\u003c/h2\u003e \u003cp\u003eDeepSeek demonstrated a more detailed project timeline. In contrast, Gemini’s timeline lacks details, and ChatGPT is intermediate between the two models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOutputs of the DM-8 final synthesis showing the differences between each model’s output as Project Timeline.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChatGPT\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGemini\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeepSeek\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e♣ Hardware/Software Foundation (2–3 wks): Target PCVR + Quest; Unity/Unreal setup; spatial audio pipeline; perf budgets; scent optional.\u003c/p\u003e \u003cp\u003e♣ Scene Design (2–3 wks): Blockouts; beats; interaction maps; accessibility notes per scene.\u003c/p\u003e \u003cp\u003e♣ 3D Modeling (4–6 wks): Food/props hero assets (photogrammetry where possible); micro-place kits (London/Quanzhou/kitchen).\u003c/p\u003e \u003cp\u003e♣ Interaction (3–4 wks): Grab/place, “cook” verbs (chop/ladle/wrap), teleport + guided drift, tray/journal systems.\u003c/p\u003e \u003cp\u003e♣ Coding (4–6 wks): Narrative state machine; overlay triggers; captions; adaptive coach; reflection capture.\u003c/p\u003e \u003cp\u003e♣ Integration (2–3 wks): Audio VO passes (EN/Minnan/Mandarin), spatial beds, haptics; localization; scent hooks.\u003c/p\u003e \u003cp\u003e♣ Developer Testing (2–3 wks): Comfort checks; cue capping; fail-forward polish; metrics/logging; content permissions.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e♣ Hardware/Software Foundation \u0026amp; Prototyping2 Weeks\u003c/p\u003e \u003cp\u003e♣ Scene Design \u0026amp; Whiteboxing3 Weeks\u003c/p\u003e \u003cp\u003e♣ 3D Modeling \u0026amp; Asset Creation 6 Weeks\u003c/p\u003e \u003cp\u003e♣ Interaction Coding \u0026amp; Mechanics 4 Weeks\u003c/p\u003e \u003cp\u003e♣ Sound Design \u0026amp; Music Composition 3 Weeks\u003c/p\u003e \u003cp\u003e♣ Integration \u0026amp; Scene Building4 Weeks\u003c/p\u003e \u003cp\u003e♣ Developer Testing \u0026amp; Polishing 3 Weeks\u003c/p\u003e \u003cp\u003e♣ Total Estimated Time ~ 25 Weeks\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e♣ Phase 1: Hardware/Software Foundation (2 months): Establish VR platform (PC VR for high fidelity), select game engine (Unity/Unreal), set up VR interaction toolkit, and procure haptic and scent devices for prototyping.\u003c/p\u003e \u003cp\u003e♣ Phase 2: Scene Design (1 month): Finalize scene layouts, transitions, and narrative flow based on the five scenes.\u003c/p\u003e \u003cp\u003e♣ Phase 3: 3D Modeling \u0026amp; Asset Creation (3 months): Create high-fidelity, culturally authentic 3D environments and assets for London and Quanzhou, focusing on key symbolic objects and food items.\u003c/p\u003e \u003cp\u003e♣ Phase 4: Interaction Design (2 months): Implement hand-tracking for food interactions, haptic feedback for object manipulation, and AI agent behaviors for social interactions.\u003c/p\u003e \u003cp\u003e♣ Phase 5: Coding \u0026amp; Integration (3 months): Develop the non-linear memory triggering system, seamless transitions, and integrate sensory devices (haptics, scent).\u003c/p\u003e \u003cp\u003e♣ Phase 6: Developer Testing (1 month): Internal testing for comfort, narrative clarity, and sensory experience refinement.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.5.3 Dimension Value Alignment\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChatGPT’s Dimension Value Alignment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCore Design Choice\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eServes Value By\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[IF]\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpatial\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTeleport + guided drift; selective 1:3–1:6 scale shifts\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmbodied agency with cinematic clarity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComfort ensures focus on meaning; scale marks memory significance\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensory\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiegetic market audio + intimate VO; photoreal food; warm memory bloom; optional scent\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrounded authenticity, emotional texture\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultisensory ties food↔place↔self\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlaceness\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcrete micro-places; bilingual signage; migrating artifacts\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity prevents stereotype, aids recall\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObjects act as portable “home” cues\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinear spine + optional detours; compress hustle; expand return\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRhythm teaches contrast of labor vs arrival\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverlays bind time layers during actions\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultural\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShow-don’t-explain rituals; consented credits; glossary tooltips\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRespect + learning without extraction\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCultural accuracy builds trust and empathy\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolo canonical; optional reflection mode; sparse, meaningful NPCs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtects immersion; enables discussion\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDialogue branches model intercultural talk\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70/30 discovery/instruction; cap 3 concurrent cues; tray/journal scaffolds\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKeeps load low, learning high\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChunking encodes “home as network”\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLonging→pride→anxiety→tender→relief; skip-any-scene; grounding tools\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSafe catharsis and ownership\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSafety nets sustain empathy without overwhelm\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGemini’s Dimension Value Alignment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCore Design Choice\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eServes Value By\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[IF]\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpatial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHybrid Locomotion\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximizing immersion in emotional scenes while ensuring comfort, keeping the user focused on the story.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFocuses cognitive energy on narrative by simplifying movement in large spaces.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSensory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAudio-Dominant Memory\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSimulating the non-visual nature of powerful memories, creating a deeper empathetic link to the past.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShifts from visual-heavy [wkR] to audio-driven [strR] to deepen emotional engagement.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlaceness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymbolic Environments\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTelling the story of a bifurcated identity through the specific objects and atmospheres of each 'home'.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConveys a rich, non-verbal narrative about the character's internal state.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTemporal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePacing Contrast\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmphasizing the emotional weight of a single moment of memory versus two decades of routine life.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eManipulates time to direct user focus and maximize the impact of the core memory.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCultural\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh Specificity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCreating an authentic sense of belonging and nostalgia, making the final homecoming feel meaningful.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProvides strong, authentic cues that trigger feelings of connection and resolution.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScripted Interaction Payoff\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUsing a single moment of social acceptance to provide a powerful, cathartic release for the user.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMoves the user from passive observer to active participant, delivering a narrative reward.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCognitive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow Cognitive Load\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRemoving frustration and allowing the user to dedicate all their mental energy to empathy and story.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrevents mental distraction, creating a direct channel to the user's emotional faculties.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePsychological\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTension \u0026amp; Release Arc\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrafting a deliberate emotional journey from loneliness to belonging, fulfilling the narrative's purpose.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStructures the entire experience to build emotional dissonance and then resolve it.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDeepSeek’s Dimension Alignment Table. Note that the Information Flow was not provided.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCore Design Choice\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eServes Value By\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpatial\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHybrid locomotion (teleportation for large spaces, room-scale for intimate moments) and embodied interaction with food\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCreating an intuitive and tangible connection to the rituals of cooking and memory, making the theme of identity physically interactive.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensory\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiegetic sound, contextual scents, and rich haptics for food interactions\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEngaging multiple senses to trigger powerful memories and emotional responses, directly linking sensory experience to the concept of home.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlaceness\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-fidelity, culturally specific environments with explicit symbolism\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrounding the abstract concepts of home and identity in authentic, recognizable places that evoke a sense of belonging or alienation.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-linear memory access within a linear journey, with expanded time for rituals\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAllowing the user to explore the protagonist's past in a way that mirrors how memory works, deepening the personal connection to the narrative.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultural\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmbedded social norms and high cultural specificity in Quanzhou scenes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTeaching cultural identity through embodied practice and authentic detail, making the return to Quanzhou a culturally rich learning experience.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStylized avatars and complex AI agents for key characters\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFocusing on emotional essence and cultural etiquette without the uncanny valley, facilitating empathy and connection with the protagonist's relationships.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVaried cognitive load and experiential learning through rituals\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManaging mental engagement to avoid overload while facilitating deep, personal discovery of the themes through direct experience.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAn emotional arc from negative valence (London) to positive valence (Quanzhou) with intrinsic motivation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGuiding the user through an emotional transformation that mirrors the protagonist's journey, making the concept of home felt on a visceral level.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.5.4 Story Mapping Table\u003c/h2\u003e \u003cp\u003eOf the three models, ChatGPT provides the most professional and technically grounded story map, suitable for a development team. It provides quantifiable criteria that are specific for implementation. It also shows a sophisticated understanding of VR development process by providing criteria to prevent negative user experiences such as ‘sickness events’, and respects user choices, e.g., ‘opt-out’.\u003c/p\u003e \u003cp\u003eGemini creates the most cinematic and user-focused map, ideal for a creative brief. DeepSeek proposes the most emotionally ambitious narrative, though its formatting is incomplete. It provides sensory details that are descriptive and evocative, focusing on what the users will see, hear and feel, e.g., ‘crowded, noisy Tube station’, etc. It also provides a clear narrative flow, mapping out the five scenes: arrival, adaptation, catalyst, journey, and resolution. It is also empathy-driven with languages centred on achieving specific emotional responses, such as ‘initial displacement’ and ‘emotional release of homecoming’.\u003c/p\u003e \u003cp\u003eDeepSeek focuses on creating a deeply resonant narrative, proposing powerful story moments; it is, however, innovative and less practical. In terms of narrative depth, DeepSeek introduces strong new story elements not present in the other models. These are called from home, serving as a catalyst and a concluding ritual in an ancestral temple for closure. It provided suggestions for embodied interaction, it is more physical, e.g., ‘hand-tracking’ in making an offering. It also provided sensory cues such as the release of ‘contextual scent’, if devices are available.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab15\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 15\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChatGPT’s Story Mapping Table.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpic (Scene)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser Story\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcceptance Criteria\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArrivals\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAs a user, I navigate first thresholds (airport → bedsit) to feel dislocation/aspiration\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComplete in ≤ 4 min; at least 2 interactions; no sickness events; captions legible\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdible Map\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAs a user, I compose an\u0026nbsp;\u003cb\u003eEdible Timeline\u003c/b\u003e\u0026nbsp;linking London dish, grandma memory, Quanzhou dish\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlace 3 items on tray; hear 3 VO snippets; at least 1 overlay triggered by action\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHustle Years\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAs a user, I experience compressed labor montage and small wins\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90s montage playable; 3 punch-in beats; no fail states; haptic feedback present\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFracture \u0026amp; Superposition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAs a user, I witness London↔Quanzhou overlays during a life stressor\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOne 60–90s tension spike; breath cue appears; user can continue regardless of choices\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuanzhou Return\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAs a user, I slow down, choose a door, and author “home is…” reflection\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5–7 min pacing; choice logged; 10s audio/text saved to lantern wall; opt-out respected\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-credits Reflection\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAs a user, I review my artifacts and optionally co-reflect\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLantern wall shows tray artifacts + quote; local-only by default; share toggle explicit\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab16\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 16\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGemini’s Story Mapping Table.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpic (Scene)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser Story\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcceptance Criteria\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScene 1: London Arrival\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAs the user, I want to experience the sensory overload of arriving in London to feel the character's initial displacement.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- User is in a crowded, noisy Tube station. - Audio is loud and overlapping. - User can look around but cannot move freely. - Scene fades to black after 60 seconds.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScene 2: A London Kitchen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAs the user, I want to prepare a meal in a London kitchen to understand the character's adapted life.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- User can pick up and interact with specific ingredients. - The kitchen environment contains symbolic objects (e.g., degree, wok). - The passage of 20 years is shown via a montage outside the window.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScene 3: The Catalyst\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAs the user, I want to taste the meal and trigger a memory to experience the emotional turning point.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- Eating the meal triggers a slow dissolve transition. - The London kitchen fades and is replaced by the Quanzhou kitchen. - Audio shifts to be dominant, with clear, memory-specific sounds.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScene 4: The Transition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAs the user, I want to experience the journey back to Quanzhou to have a moment of reflection.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- User is in an abstract, dreamlike train car. - The view outside the window seamlessly morphs from UK to Chinese landscapes. - A subtle, pensive musical score plays.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScene 5: Quanzhou\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAs the user, I want to buy and taste street food in Quanzhou to feel the final emotional release of homecoming.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- User can navigate a small, atmospheric Quanzhou street. - A scripted interaction with a food vendor is triggered. - Tasting the food triggers the final voiceover and a fade to credits.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScene 1: London Arrival\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAs the user, I want to experience the sensory overload of arriving in London to feel the character's initial displacement.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- User is in a crowded, noisy Tube station. - Audio is loud and overlapping. - User can look around but cannot move freely. - Scene fades to black after 60 seconds.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab17\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 17\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDeepSeek’s Story Mapping Table. Note that the first column was not provided.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUser Story\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcceptance Criteria\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUser Story\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcceptance Criteria\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs a user, I want to explore the London street and flat so that I can feel the protagonist's daily life and triggers for memory.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- User can teleport around the London street and flat.\u003c/p\u003e \u003cp\u003e- Interacting with specific objects (e.g., a photo, a ingredient) triggers a memory flashback.\u003c/p\u003e \u003cp\u003e- The transition between London street and flat is seamless.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs a user, I want to prepare a dish in the London flat so that I can trigger a vivid memory of my grandmother's kitchen.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- User can pick up ingredients and utensils with hand-tracking.\u003c/p\u003e \u003cp\u003e- Haptic feedback is provided when handling objects.\u003c/p\u003e \u003cp\u003e- After completing the dish, the environment morphs into the grandmother's kitchen.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs a user, I want to receive the letter/call from Quanzhou so that I can understand the catalyst for the return journey.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- The letter/call scene is triggered after the memory flashback.\u003c/p\u003e \u003cp\u003e- The audio of the call is clear and emotionally resonant.\u003c/p\u003e \u003cp\u003e- The user is guided to the next scene (Quanzhou) after the call.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs a user, I want to arrive in Quanzhou and explore the street market so that I can experience the sensory overload of return.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- The Quanzhou street scene loads with vibrant colors and sounds.\u003c/p\u003e \u003cp\u003e- Contextual scents (e.g., oyster omelette) are released.\u003c/p\u003e \u003cp\u003e- The user can interact with market stalls and hear vendors.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs a user, I want to perform a ritual in the ancestral temple so that I can feel a sense of closure and belonging.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- The temple environment is serene and sacred.\u003c/p\u003e \u003cp\u003e- The user is guided to make an offering (e.g., placing fruit) and bow.\u003c/p\u003e \u003cp\u003e- The experience ends with a reflective voice-over.\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Visualisation\u003c/h2\u003e \u003cp\u003eWhen visualisation is requested, both ChatGPT and Gemini generate images. DeepSeek provided a textual representation of a top-down map, with a textual ‘comic style’ storyboard. They appear to be very simple, but can give visual cues for designers to extend ideas. See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e–\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. We expect that future multimodal LLMs will produce better visualisations.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.6.1 ChatGPT’s Visual Diagram and Storyboard\u003c/h2\u003e \u003cp\u003eThe summary visual diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) provides nodes that are not entirely connected. The thumbnails were not clearly and directly linked with the scenes generated by ChatGPT: Arrivals, Edible Map, Hustle Years, Fracture \u0026amp; Superposition, Quanzhou Return, and Post-credits Reflection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe summary storyboard (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) provides thumbnails that do not completely depict the scenes, there are spelling mistakes, and the images are overly simple to execute.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.6.2 Gemini’s Visual Diagram and Storyboard\u003c/h2\u003e \u003cp\u003eGemini generated a roadmap type of summary diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), incorporating scenes 1, 2 and 5 as thumbnails. Scenes 2 and 5 were clearly labeled, but not scenes 3 and 4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGemini generated scenes 1, 2, 4, with correctly depicted illustrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), but missed scene 3, and 5.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e4.6.3 DeepSeek’s Visual Diagram and Storyboard\u003c/h2\u003e \u003cp\u003eDeepSeek does not have an image generation feature. Instead, a textual map (top-down view, Fig.\u0026nbsp;18) was provided of the user's path and key interactive elements in each scene.\u003c/p\u003e \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 18. DeepkSeek\u0026rsquo;s visual diagram and textual storyboard.\u003c/p\u003e\n\u003ctable class=\"fr-table-selection-hover\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 312px;\" valign=\"top\"\u003e\n\u003cp\u003e\u003cstrong\u003eScene 1: The Hustle \u0026amp; The Takeaway (London)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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JJupTeYsKm/o7auPzvXl7Bvf694HV2Vrl6HopJfpN9f4ZVRQPg1ax0fdJtbvMsZHRM/rg/48LRgbXC49UMfiU2lwYg8eYCXVHVIOFVdZhM6X8jCMs3D5Chk6dDgqg9aW3r16aBG/4ydOKGPFixdfC3NRALGQFwucHQUzsehhggQJVRhpaWZHGePjqLZJxeMUimRAVs0117HKLQt7mWJg/J395z26wLCCzpw9q4LOFCY0k2IE0z5U2zyBiq0s0pcRBdBcJb5jaN9OokqmKczH+1iJ9Riex/+yUCKLopnyx94mWxkCyuXwwcNy/NgJHTsr47JgHPt6+vQZ2bdvnxZ/zI6aM7SAuVvz1Fj47eqNqzJ9+luyfMVK6d61i1Y7pRAJTWCFhxkpEDagOm3P3n3l8QrlpXfvnpIbVT3PgS4U9CxqycKCLODHcbJSKOnGgossyscSz5wrFmcjXfk3eYtFA1lR2NBvDarcTp06FYUxU8urr01RfogMy1RLzqNa6pTJr8uSpUtk4IB+8gRqb7Ei8VnMK0uds7icEXDke/aVlXHJu6aAHgvysbHqM/k6EfiNY2HztVb4GxUW6UAeo9JPivXFsbL8NQt1mqrCrNDKa0w12r379msF2uyo1sD+sa/sM6/nNRQ6hAFg43fs8/nzF8AbLNmPitDgN64Vzqk3dcz1w2ew3lFilBFisUI21fO4aQ8qr/KZLMLHIqZBmGuOhzKCCocXst9aOBF93Lp1m7yOeWUl3TemvY7Kt5kiZV7Dw9uBcI/hR1b2zo96d6zg7K+hHtX99600MPEUpLNn/08Ko7AZq7uGdoidPHUYpdRZyXPIcwN1Ify5cLFMg3DjAmEJYjJRIlS+rIlSyxnBPC+8MFaLIKZNlxb/LSjt27dVK5OW/wEIlgYNG6O8cXl59+3puuPgwu7StTt+O6TljbmICxUsKKNGDZdUePZ+lP/u0bOPljZ+9923JDn6fQrW67PP9pSWLZtLC5Q2ZsEvNlUyWEiffz4DpcH/h8q3qHsPRh8yZLBUhHDkIvsfSifz3g8/fBdF/RrrfT/++DOuGSYFULwtHSq05kUhQhYWZH9obbk3vodC9LPPvpAVK1aAFlfwuSSDBg2UGtWqyt8oszxixGgtvU2sic7PdJJ27Z6WmMEuPvfnuUq7x5O+fXurwDpx4qQb5FLEshLnle9ICEU/bNjzUA4QVqD7hJdekd/nzZNsWbNq6fOqVSrLU0+10pLbLIf/8ccfSBIoik8//Rzl51fLRx+8K3/v/kc++eRT2b3nH61CmwMKcuDA/pIVC4WF6IJQDfmNadNdGB4RO4yQp/G55JsTMGaaN28mdevUQXXb2/LW9Lfl8y++lDwobpka4ymDwpitMZ6k4KVJ6NPnn30qufI8hr5d0/GOHjVCFqEU9ttvv+uq/gxh3LBBA5Qer6+VZT0p8LgQqrt275YePXorT1CAZkQZ88YopV4VhQpHjx6jQmPI4IEqjMeNexEVa1PLyGFD5JuZs+Tdd97TkupJkyYD/ceh5H5sadasBcrb15CpULRUyuOnvKaGSC8YbSNHjpYVK1dpSW2uwcdQT27cuDHoa6p7ylIYo6tz565yEOsoGwpfagnxJk2kFov0gR+/+vobef+Dj3R98rdJk1/RXdrwYaOkWPGi0rvns3ICfDz4uSFSpUoV6dyxPaqzFpf+/fvJiOGjtQBqZM1rJLFLpD+WBiKNktmzf5C2MDyyo2JwaLI20jvl5QWhuqec22R/O0nGowVE5XADlkqVqpWlBvAa9sFCevbZXtK5c0dphYVKzpkE5mZdlS9nfC5bt2+TXj36KDZGLzAerSBa0VQgBw8ekn1QIKyQyoVGC38oBHuL4Oq5rJjJGvlsa4EFQKakJbtv735JXqSQa6dx5pze52xk/AUL/pQvv/xaXhw/FhgWZSHU7tTJPwtraTvq8KdLl07xHOrXq6eW2qlTp6QgMDpmzfwG/dov3bv1kBjo8NAhg3R34C4s6N5aiZr4M2Z8KR9/9AEWbh45gDERVIqC4eOPP0Mp5CTy/vvvyKJFi2Xy5Fe1Bn8+XOetOBqtTwou0tmUaPZ3jsJznVrEKMucHqXC2S5jN0FrvVXLFjL8+SGydt0G6d2nn2Ir0Eqihas+El4LYUXLk+2DDz+U06dOgxafab85TwmwS6FLjpYyaU0L2YNHMzzd9nkPd7B8H0tjcx642yNuxLSpr8pSYIhwPCx53Rj4E8RpaPREE3kVQtLZvv12JnA9ysuECeOBpbIOczdFwXUaNawfYpy4d4KAV9zxjhnzgrCy7yTM91goh1I/zlaamt1VfFwTFHRVFSl3dG+99TZ4rZs0btxQBg8eClp+rIbQGRgOxCJZDd5nBWjuPAg2xeecgcHUr18feaZjh5BuXCWolZc6Rpw7zheNg+ZPNpEZX30t0996SwphHQVduSofQ+H36d0LSqqaDBs+Sqa/+TZ2bK8A46GFvPbaVGBEVJRVK1fr7vNJ4Oyw+izBzmhIsny5Kcsf4ZP5EDzQuCgDeSihKg0T0wjrIExQVuv3M14ACcBFQoXA/xrwGf5GF1Ic+GBpAbHOv3Nb9ufCRdKyeXMI8t2y+K+/pD0AeVy18mMAoGS1CnDiVRD4h2BLXAhr1qyVyrDYCA60EuBKRcDsrH1PzA1jSZnxULn8BqCVwoULAuClpCqbx4LxAngNdzq7sQuggiK4yj8ApSqAXQXpQndM/PhxJS+uTwph6ms7SdHJ3dU1IPetXrMGmAnpJUtw6fdjsHbpAmzdqpWkwHMaoJz2m7BqtwC0hUrDV3DMHQ0srPMU1us5r/TjKwwlA+MQ+n///TeAbH6F9bxHCIbDHR6bU5FxzgzQTkIohp1Agty8ebOUgcGQPVuWO/Eixo7cENbC2sewXu98n+FHji1//vwu33vwzpGYDDt27JKfsfMkNgRdMZUqVtBxkbfIx3Xr1JQ5c+aoIdKgQT3lN08JCi5XbTxVVnRpEfDr5zm/AAMlSJ+zEQYK/76K3dYeAJFVx05uC4wX3lOrdg2lcf36dbEr+grgZ/vUgCmM3fa3AFwqhTLtusbwP7ox48aOK+vXbpAMUI6XYeVzd1ARIEOedsSknVnza8CnNHZ27fpbaZEIinwz+sXfCUqVDO5V7m7ee+99XYM1a1SXNWvXy4svTlDDbjyMMLqmuasnn5DOkeE6Det8B/L15JdAV6qhKo2IILBhFBPgcS4iLhBadH369keQ9SjcAaWlZStXbfl/IaCJ6jUMVuxvv81VJD+jNBj8JaofwVroPydgEuv87z10COh0hzSeQpjUH/73P+mI7bG7snCOixYkFQ4FxDLsbKgcKpQvLxUhELZu2aoWb2tY078CXW/r1u2qNNjvtdg59Ea/ef9jj+WRdu2fVqvK08KgMqNg6NWzh8wCguCMGV8JUc2e7dZV+0brMgmBcdDoE6ZFRr+y8TkbZgokPyeZm0r20KEjUM5rZNGSv4AxUkxBmDzRwHz3zDOdIUA+gKtiJOJHKdUdWbtWLQUHis7G8VDY/vLrr9Kr93k5dPiIorSpWwYtJmBLDx0+BCt6lcL2ZkifETvT8iFuKLq7EkCoM0azBXxzBXxJBelJaTBQzd3AF1/MkJ+A2rYYKIcNgJVA4C3y24H9B4DWtsEVOwMCIRUwYwY0qgwd6SqkK5NujXhx40ndunXlC+xkVwHdj/GYSxdd8ZIY2AJzrfB5fCdBmbgbiOGFV02SABHjCCe8dNkyadGiOYCEkiuvx8eaowJlI8/SXXwROyfuJoiE9/7774NmNYW4Etw92vZwUSBKlIYvkrmQ49JJubJlsVjnasDcBBKXLAHUKoLda2HxHIYy+AcW/0H8l2iAtO7oO+b2+Sq2wQng32djgG77zp2yB1Yv4waEwGQchc90ujuc1jtdIhrc5CKABbcSioP+++Iliqt77Nq1GwiWoi+IQazFgmwJlxgXLoOg5cqWg1VJiMljwe/w7K117bhEOnZoD2VRVeE834NfmIv9qTatsOCYIOCKs2gWEpQMrTTTIjudNjxsbfpUpkwpGTSwnzzTpZMMGDBY5s1boIrQuUvVv4PHxtjFqNEjFKp23vz5sFQ/UCVcHUo0OpviFUDIZUISRJUqVSGAZ6hriLEjNu4SyacDBw3A7oPY9dhtQngaTHFjmNC1Q6Fqsoe8jcllUQLNEUK8JmBjixYtoskPhAGtCpduf7iUgvDOo+AtWu7cZdwIRu3jMxnYNuhxl69cBnBaASmN3TLjbXRn0aBi4gWxeuoCaKhTh3a6IyCwmi83CGlAA4pQv82gBLYCSnbSpCm6k2DsgvfeVFzvOLpuGLSnB4Htfz/+iLhGMTmKnSR3nty1RzT2RHTyiH03PAjRTQQyHQOhzPypBXzk7t17ICj9hTzTqSPwvpcpBOceBEwpcWjRrEMGD9HiuODoHmBGk7HGuQ0mtjEFEGMOXHyJEyWWpRD4TRDIo1AwGShMUTSNCIV/wdK7iGyQIkDqqlqtquzc+TfcWy4XSokSJXRXkRnupI0bN8pZgrtjYTGw+PTTrRUruytiGh9ACfTr08urO4n9p7Ijahw/+2BNLgEOOwOghDP9999/tUvHYc2dQ19y5gS8KYUDlOI38JvT8uvXr7fibwfSjoP9YfYPP7TGt2zdqjCVjDERnpXtKqxu+vnZzmNXxcBycQhJulVWrlgF7OvV0a402DfSNTcw1lu2eBJ468XBj72g2P6UenDHEClNY3UOZc57GEDnDiUx+O44jA1iQXMXGQ9zTVeep0bakH+JI87gt7ORT10QqnB5BvMsFQSTDU4hFsSspXSIZTHTLi4UCelP44vPbNy4kSZU0N3ZoP4d1EBC1nI3bhp3AL5cRVSANOY4XuKTn0C21j///qcB+osXL8jRI8exbrPKf/AG8NlMdpgHlxz5eSziNEwOeGPqNJn2xuuqCG17eCgQKUqDzGjSWsl8LqvEdSaDwVBaTaaZFEcyMXF5m7dohsyaz5FNklF2QXCPGTtahQkVQ99+A+TXX+ZqRgYzj2jRsdFCZ0YKzxDMmzdfunV5RtpCCdFny4X5I7b/dZAdc/XqNfkO2SfnEVBkP5gNRp/sk9g5cEfxyiuTNMtjPRQTs0YWwGKmknkJGSoJ4M/l1r5hwybqTguCAjiPQC9xh5kh9jSgKSe8PFHqQPERV9jdL0+rk77hn2AFcuHR/bQIWWWE1c2I+EZtCKXPPv0MVm4G2QShQ4s2LzGkOT4o1g8//EiWL12srpw02DWRohpXQb/oFoiKxnlV/71D4dKtNm/+AhVc/0KpnTx5AoHwUsBqB2YzYj6vvT4VmUi5IUzgfkEwmeKDbr4dsF4ZR/rvv73qXiwOtxbnmL9zzqIkCh5MQ7pyTKOrh1Y7Wy7g2jMuMX36dM2kI/8umD8PONTIbIGAjgMlWB/WOGP9K+GymvbmW5oFlwZC9MmmTTwmRIQIbfAsedAIbho85vwDU10Z9DZzz+sSQCExo6tgwULy2quvw3VaUX7+6Rdpip02sw5PQlmx7/nz5YWyKyFfwu1FVy9jGhTy338/W+MwZ8GzKZOnlFq1asAVlsprvIXK6bvvvtcd9Hq4tYjeSQWfAYqEBsHkKa9qPGfu3Hlw/7ZDMsgBef31N3SdMaZCF2XXLs/KRx9/Kt26PaNjYYp5oPvro2IdPejviBSlQeZfASH83XezpBq2tClggRoBTqHHoLUJwj2OgBzPJDB/m4KwNmIRVCRcNF0h/AsWKBCyk3jqqdYIuu5WpdO2bVtgmBcKeQ6fFzs2MlawC6mMtE8KIPajXoO6mplDZu3UqYMsX74cVu0aFeo8C8BMn3wIwI8fP0Y+Q9rtHxCAuXD+gGmj+xBgfLZ7d3UL8HnMUurVu4e6sIoCH5xuCz6X72aGGH3g7LcJnDqZg9/RnRUX23haYRrIhEAi7jSf3wRnBOhSoIvuLwT8J058WZUaM7HoTugAt1YF0IrWPN/nSuG9jvjI97IAyqcpLNbIbnRZcKfFlFTGjDieBsgQ4i6NGWvs25gXRqlAY+ID02hnIjD77797pQ6E61OtW2nfuUNbht3f73/M16Ds0KHPafYRabwNwV7u2M7Cfx/ZjXPHDK8vEF/Kgj4Vwc6H42K8gv3k78wMY7sERdIISu/WzetIUNiqOxLyVdkyZdTwOHv+nOxF/I08+cQTDTXobNK63cfBZ9OKb9eurSsfP9jvr5mK+PeTUATcYZs+cPeQEDtm8sLIEc8j0+5jmQ8+bYT3tMGaOAFF3a17V9yTXK9pgcxEGkhU1uxjy1YtwfcrNNbBjCYK/kqVK+qhTPeIg0lc4TrlXGzcuEljNF27dZHcUFrcNXC+Pv74E5m/YKH2lSnLW7G7JD+3atlS+8BzTgMw/8eOHtXh74ar6nPQma62iD+qGdmcYp/vpECoSiMsKbeG6cvjIOCzz3bTIJhaUhSsWGTcHnfp3EnfbxaU8WPzby4SCmZzDa/j9tz4+isgBZUfPrcjFpzzOWYh0j3EhWDuYdCaH97TDrsPfpyN1/LdeaAoXhz7wl2/MXPprnfgj6exSE0rX7aM3st3J8Ki7oGF67ze+TBeQxfUc/CH3/U9xscgOa3WTlAM/HyMMw1MSb0E5UmFQuu+W5fOehvHYYTJ7ds3NVe+OVwpVSAE+H1kZafw2QXhM+/Xt48LLx59plBtBouaH2dTmuCLSlBy/JjGuSQfVKxQTj+kvQl9041DS5670pw5c0plBGp5qMxbhs9dLwzHH3wu3ZhtcRaGLkijiOvXq3PXHDLLbQBiC6R7LRhA/Dib2R09jvGYZnjKW7f4rkw41NkFqee8PySWRfrg81RwIgjpSBrzXJHhK7o1x40dE/Jo9itLlizSH3zPezkvFNjPPzdIryFdn4YBxI8nvnPvI/mHn149ut/TfTOveaE8XpnwYsjvfAeVZREaccF94I91sJth43jZz3Tg/0E4REljI7LmNRysEFC3PBQpt7QuKbj83VaSGXLmyqnMYZjW+N8pFNyDYu7WmAbZvEyj81pPwTVP94Z2j3mV87AfT8EGHy/wuHA8dc/T2NyvM/0zJ9CdAt7cTzcWD/UxU8aZrulOJz2RjGufat1SX+MUPpGxCjivFEb9EVNhoxAIDa/YCD2TjeOktSurJ8Zd2TUcEzPMiuJjeCeyEgD4XLpQ6yIAzY+hnzudnW7GiAzomjNFnubKvQ938XCwInHS1J1G7q5RbzseX3zi654780qXs2vfYBSf85nOZ+SFAZYfbtvIntfI4P2oeqZJw3YdKQjc5nOnQUZgECzbiGEaEPM3f57XGSUTWZZvZJE0soSUs7++3kHaUVnwZLGnhXjXuLFeTaA1KuhMpXcrOKjp7/uM5eo+X96+j0reYR/uBKofHKeJN9pF1prw9FxXH/x/Y3h4x/+nP/hXku/p3h0xcpgm94S69qNxyD6VBnmCW3j638Na08hfoRKNYw/YV1Op+Ku8oprOUfG+qHiHmfyofFfAMlwUdczS2juhKWvpZciMkjLeiqpG0TSF+ppQYxphEWChvs1eYClgKWApYCngkQK6843iagjhmYpQlUZ4HmrvsRSwFLAUsBR4OClglcbDOa92VJYClgKWApFCAas0IoWs9qGWApYClgIPJwVCVRrmlCqzTGwg6+FkAjsqSwFLgcCmAIPkPNMUnvTpiB6ZT6VBEBkXWtl5PZDDQ2ZWcUT0FNjnWQpYClgKeKcADXeChfE0PysJ+JtZGVk09ak0WEufx//n/PwLkPC6K+CPv2c1IqvD9rmWApYClgKPCgV43o1ymEiYh1Fbjyf9r3spaR9VNPG900AvWJCOlSxZNM/WqoyqabHvsRSwFLAUuEMB1uxiVeNAaH7FNBQFzJY3DoT5sn2wFLAUeAQpwJiGqRwe3cMPVWlEdwft+y0FLAUsBSwFAocCVmkEzlzYnlgKWApYCgQ8BazSCPgpsh20FLAUsBQIHApYpRE4c2F7YilgKWApEPAUsEoj4KfIdtBSwFLAUiBwKGCVRuDMhe2JpYClgKVAwFMgVKXBw3w8qxHdpxADnpK2g5YClgKWApFEgStBQVqdIxCaT6VBMJCsWbNKU2BAJwYQk1UcgTBltg+WApYCjxIFbuEE+OPly0uBfPmE/47uFqrSyJEju+TOmUNhMS0YfHRPl32/pYClwKNEAdb6I2pq1coVddgBX7CQnTTYvrZQ4aPEqnaslgKWAoFEgWs3b6CMU2AUcgo1pkHCWYURSOxj+2IpYCnwyFEAXqnb+F8gNL+URiB01PbBUsBSwFLAUiD6KWCVRvTPge2BpYClgKXAA0OBUJWGqW4bFS6qWLFiaYZWVLzrgZmh4I4+qLRRPADMKxsDenZuI4/zyCMxQW9m2DAWadvDQ4GolMOhUS1UpRFVi9yFTnUS5X/jaHovW1yUA75FYYOzIiRaHCqV4L9DG1hE/W7gbs3z3N/PksVUdBGRjsx38eMEuuK4r127prRJmTKF8H1RNSf3S0P2PQj55YcPH5GrV69KtmxZdW5jeSmzbyCF42CMDPndcNDVfGeyR8zfvId84d7Ms/iqOLFcbG7udfKVuY8Cl/1yXsPfzHPM784+8Tn0Ml8Hf3prhm+dv/Md7u+LHRsCH6N2fz/vc76D73Q29of8QP47ePCQnD59WtHdUqVKdb/TZ+8PIAoE0pr3jdwHBv3v3/9k48ZNUr1GNUmaNGmkWDAUlBQuY8aMk65dn5ESxYupVXrg0CGJHz++JE+eXN979OhRSZgwofYjKojIfvE9f+/5RwFQ4seLJ3ny5oHwTqlKjAv13NlzEhffU9ndT5+MgCXYVaJEiULYNUbMGHIb75k8aYo0adoYqXeVAiLtzp/1RGG+cccuGTZshKROk0rGvDBKMmbKJHtAz2scJ+aSNLx85YokSZxIsmfPjvmOJ6cg+M6cOSsZM2ZQDAHS9djx46DDbTwntSqJE6dOy9mzrmuOnTkjx44dV2XL51Ex5cqVU+GJ2Xb/8y/4JoGkS5dOn7UfwpV8lCyZi49I+wsXLsj58xckbdo0KtD3HzyIO2OoAObvfBdhjzNlyqiKnd8dRZ+o0DNkyOBx7s0u6wgAdPYAAfNK0FXJkSMblGc2fdbFixfw/PTBzz+HPlzU8XBndvzkSdm6ZZvEAV8VKlRQkidLpjT7F3x46uQp9CEG+prOdT1hmdGPBfMXyPS33pJGjRrJiBHDdBx2x+EPpwb2NTSQlixdBh48J40a1vdppETFSHwqDQKZH4Tg/vXX36Rc+bLKuJGx6SXTz5nzCxZLbAiObDpuCoZBg4fK4xXKS78+vSBEzsjAgYOlQYP60rF9O91xsH+m3XRsyd2tMWO9GevOSVhvOxcKBi64r7/6VuYv+FMXaRCEW8OGDaRlqxYSH8LsJq556623pemTTaRg/vz6WL7L/f2wBSHQXJRz37nwO/adVu7KDRtl5YpV8tzgAXotrdxbNyEEoURy58ktc+f+LiVLllBhS6Ua3c05Fm/541dAMyr9UaNGSt48eWTrth0y7Y03IfRPyT///CMJ4idQoVugQD7Mb3/8nVbenP62zPhihrz99nSpU6umBEEgTn/zbbl46aJMfW2KHDl6TEaMHC3ZcYaoc8cO8tyQ52Xb9h2SO3dOnJq9LAUwFyNGPC+podwPYpfTuHEzyftYLpn93UyJCb4aN/5FKVyokPQBXxkeIv/Nn/8n3vmmKqzu3XvJNeyOvvzqc0mfNq3s3btPRuKdg9DHmjWqQ7GBHwcMljJlS0v/vn1CdiTOOSF91q5dJ++++z5cRrfkypWrkiVLZnlh9EiZ+9tc+XbmLPnmmxmSAEbHL3N+w9/fyc8/zYaCPCHDh4+Uf//5D2ejbki5smVxzwj0PbZ06NBZ+5Unb27wWVypUaOG8l88PKN9u6eFCurA/gP3ZcBEN1/Z99+hAA0P7rrXgI/27t0rT0Bp8Lv7MVDvl76huqe005GI2sdn34QF+eNPc6R+vbqSKkWKECFKoX0TQjNEMVCIwpJkoxJZAGH+L3ZCtLbq16snKVIkl1hYqP/8t1d+w6Kk5VmjenUpWKiAWuu8Z9euv9USXbVyFQgv0qBRfckAC9T94CKfcwSYvG+/845MfGWCVKtaBZbeDTkFYUe/8fYdO+VnYKfPnv0/OQ7Q92JFikjJ0iWlUMGCsg2/LVq4SM5fOC95IChr166lgp7tLCzMLVs2w0pMC8vwT92lNH6ikaxZvUZmzf5Bdu3cJfFgbdPibdCggSRIEF/dE42grLp17yn79+2XggVcCio6GwUid1nLV6yU4tgZZsYcGEXnZGjOLwVaggQJVGEWKJhPPv3kQ722R68+kjN7DnnuuYEhQzkGN9yunX/LY4/lk6Wwrqg0lEcw9/HjxZcTsMCHDR+FXUMaGTywH5TEFexSr8pIKImWzZuFPIcKl23ZsuVq3V8NuiY7du1S5Z4Jux3yApXD4cOHJAd2ONyppEmTRhJiZ7sQc8f+XrlyWXZiPqg0SmKMlStVlK+++kbKlSur/EWF+HSbpzzG4WigcNf4wQcfSTrsVia8OE77c+jIUd2VXoUiJJ2oMNi4K+IYKSC++upr7d+3336p/DZw0BD5FQZDs6ZNlfcmvjxBqlatLO9/+LG8/vpU5e9iRQrTItEdFF2Ytj18FIhMORwWavnFXZHZWW7FaTmexuLIli2L9p2KQYN66uO/c6CFf/N7to8+/ESB1nli/fChw3AtnJfUqVLKdizyCRNe0W1/nDixZdKkyfIiFmy2rFnkCKzOF8dPUMs3K/5OmCChHIeLITMsXU+n3eka4e5qwZ8LIWgyw1LOJRkgAHgtFc6tWzclVvACjRkLriwoPyobuiKOQDikgKX700+/yLat29Xy5VhPnTopvfv0l4qPPy5JkyTRMdKCNi6PWLGxwwn25Rtdzb8zZc4k3KbuxrMLQGlwTpRGoIXJ3ialuGuhMGI/3But3Rgx6Dm/0/hvqmEKLBOwNr+6nsdf8S5aN2738T0roDRmzZol2eFy4S6sdKmSepVz52F87q65RZA2hssHTxrSYGCjb547zm3btknsOLGkc6eOMCR+1h1JKtCRQnzbtu3y/LCR2Jmkk1Ejh2P+EkAxX9R+//77fLkBpX7p8mW15mtD2bD9CQVA5Uv34oIFi1RpUEnshNts166dMqD/YHlpwni5iOdkyZJFd3Br1qyV0mVKyU3sDletWi0VypdTod6taxfp3be/THn1dVm/boP069dH0qRO7dFdqHMAnsiQIb1s3LRF/lqyTHclmfA3G3nrAFxgH33ymcTDv//6a4nyANtW0KBY0aKSDoYDYxPk1S1btqpSjBkzltKKjbvy6xgzXWSmRURs7R7GsV9EOwUi23gPywD9UhpheWBYr6VgOnz4sC4cxipCa2ZhrcRirlTpcXVd3YDwpjCi9fbdd7NkP7bnPZ7trpbe1GnT5Be413o+200F7ZFjR6Vu3TrSu9ezEoO+YAgZY5U6303hkSo1/PBjX5BPPv5UunbrLvnz58N9PSU/hHZBuFNy5MwumzZtluZYzJUrPq5ClQu6SpXKUgmxh/PYVVxGkbFFixfLM0c7SWb4wylMLl68JEWxM+nxbFftM7NdcufMKZeDrsCCjSED+vbWrqh7Cs8zwdQ0ECKkFWkWdCVIVmF3chzuCGNZcmeVEyVf0kMJbtq4WS1lV1yGRmgMyYf+n0Ys4AhiQ7RYVVjjnrTYaRWG35zCksUpDY3Zt+LFisIXH6TWPy1kNvYpKZRphQrlZMhzg2THzp2ydfNW+fCjT+T1qdOkffu2UrNmDa9TSRrc2Y3cObJEUbh0yVK4l/JJWQjY2T/Mlg1w2dXGs6i81q5bpzvGrhDeVBghDUOhMqYRcRa7h3jx4ur4/tu/H66a/dIfwn3Dhg1wgf4qfXv3UBotXrQYvx2UM3AzMW5HmhQrVkzjKfy7zdNPKU++9/6Hck4NklQQ4CnhHmqnbtO6dWpL9epVQwS4+2CN4dOte1fdbYwb/xJiVQmlY8f20rTxE2rQBGG+D0FxUIFwR4GJVjcm5yAxDAptGBtjafyOjcr988++kN+x81izdi3iFw2wK8urPGTmNLQ1ZH+3FLgfCkS70mDnU8AlReFFN4Oz0ZtnBJjuOmAlm8Benz495Qssnrr1G0JIV4EPupu6cujPTZoksWyGMKcFX6pkSQ2AGmGXDq6GatWruIKEdA9A+HiyzjRAejuGVIArgpY9Bclnn38Bd8AbMnHSK/CXp4CVe0EVEzODVABjQdNW/G3uXMSB5mJnkhvZLGewQ4iDvrgqVDLThsHUunVr69+xYcGa91++fEWtZQZM6aJyunmokM5fOCfJQSt9Dt7LmMC/CPLGhZBku3b1mgqghIiB7NixQwO73LlQa9BCpdV64MAB7FZ2q8I09+TOnVtyI3C8C+6bU1AqsZDJw3YF/WFfaYVv2rRJ4oO+bLTA6V4rVaqEpIWlzUAt37Ed76QP/+ABBpHD1qgYScelcCflzp0LivYvjSNQKddCDOH6teuY50oQuh2QMDFeXTJPYGfDRqVI1yZ9+ldBF7MbXb5shbqg/sKzGJvbDwWy8+/dKmT34d+kX8vWLeQkAst0c9IIOIRd66bNW6QolCX7tHXrNu1HGtCO/MIdYkrMQWXUAqLa9eVbJq9mggIfPux52Q+6sx9Tp76J9z+mc54jRw7sOnupi+pLxM5+/HmO7k7ozmNiCBuvu3EDSQOJXe5NvjQNkgHKY/fTBq4xxjaYLELec4+lhW0G7NWWAv5RINqVBi3zzPAxx4GwoxXtbMx4YUYJG5UGhQoXFFtVWPPV8Nm9Zw8ESRdky+TC9v1JzWyigO4OK56WF4Utg6O6ALHojUuIf4eWWULBfA47EcZZaHFTAQwfMRKZL+dUadAC5DNo9bNpVg8E0HfffS+d4F5p1KAe3A+fy7IVK0KUHyVc3DhxIYChrNz6QBcIhSMFB0W6CXjR1eTKKDoH4Z5L35UI2UbtICTdhZZxczF7yL25znqUvece4+rq0fPZe37jPXxHjZrV73ocv78KJfU+rGjuUK5euyrVqlWVwYMHSuq0qV2K0IOLzBtb0v21DgonCHPMrKBzyFai8qBL6ghciKR1iuQQ1tjRNW/2pLyBYHrx4sXVemejK5DCkx/TGM9ID1cWBXASxLHigr4rlq+QTrD2L1++JBugCEePHKG7iQvnz2mG1R9wc6XGDpOuQLp+MiFWs2TJEhgfJUJie1TMRjF5Gw+/J91OQgnTbcqin/x88uln6oLjzpjznQQ7CvIN+212lXSl0sXJRmOK7tsi4D/jdqwF11vdOrVcPB18ja9+2N8sBSKSAtGuNLhQ4sHtwSAjXRH1YDFyUbLRxfHtNzPldQgI7iCYilq6VCm1ql6DxU93FgUerWH6jtnoKmJ2zPARo1TAnoHwqQRBU65sGRXwjH04z0F4FWJYyJehMMaPe0kDmSngjlkCIVTx8QrqpqACSo73l4AwmTVrNnLkD8rj+I3+8kwZMyLTaa4cg8vjJ1iP3DEZBcX/Mn3T0+6mcOFCMn/eArWk6aJpjBRbumGo/Ci4cubIqdYwHTocN+nkzKQw/6ZwocuKHiha4aaZ710msutb5z0UhM57TFoxrzPpx6578Dd2fQzWnjhxXFq2bK7uOGaUUYg5z624fPuxVDi6N7pcGEw27XskFeSDFT58+FC1mnf+XVO6PtMNrrZNajBw7tg6d+ogy5Yvl7FjxiHGMQTPuCQffPSR/Lf3P01lzQFfv2Zqbd2qqacMprPRzfbbb79LixbNsPtMr+5DuuWuX7/mMkqww2OmIN1r7ds+rfekTJUC7qAZ8swznSUZxsCxcf7MTsAb/5jsO/IGd3cZwZ9M9WV8hokDS/5CCiV2QeRFKg3S4TyMETZCEbwwepyMemGsfk/lUhfJFExNpsFCY4bNnCFx9iEBFF90ZtZ4o4f9/uGhQLQrDZKSArh161bSf8AgzR4qVrSI+oppUdKvzGAkhU47LGa6UeiCoKvlH+T7c9veo2d3KVO6tAaQSyOD6QWcB/gZwnoP3A90o6SCC0WDyRDmPXv20BTP0Mq8c+HR+mNAVF1dEHD16tWROrVra1+ouLgDoH99PvLjmWkVBMubO4C+8KHP/v4HzcihD5vPofXKdzJAP3Toc/fgk3C8dJtwLAyKXoAlzMZ3XIKwmIOUzDqwLplGeiM4o8xX0NOb4PAlUEJztTjZHtEWDQKPGD5Mv2b/PaXdUvH8DXfYlCmvSZ/ePVXBG0wACuekSXD2h3EbPIMxgjSp06gA53cM9Pbt31cyZ86sqdZXEAPg93TdjRo1QjPg4iNlt0ePZxHvWC+XECuilmRcgDs5pvAyoKxzjRe0aNEc2W071EXI326icih/o8uLCpB88nTbNlIRRobpUw24xmLDvUja8DsK8CFDBksRxKRcCRGei8jxe46jKHj5GOJOe/cdkMxZMkkH8jDiV1ewO02WPGnIWJnSTgVFOpbEDmrI0MGq4BIlSoxzLkM10YB8MGTIcxpTc76b72EwnOc0vprxNeJlhSM14/HhEX92JOGhQKhKg4KJlk1kWi8UwBngBuBCTgkr3lhQsREsbAGXEz+mUTBxkTwDa9PZDN6HKg5Y//w4G+9jILnxEw3VNx2aa8oEMp9+qrXwY5rzdK4qgXRppR2Cps7+5YFiGzpk0F3vN+9MDcHUGpa582SxKs7ggLez7xyTuY7ZRPnywxd+VxA5PFMecfewL77q+6sizJcXO4fnER+5rAfVnEFwBrjZzDMaQTE4/+YOpUWzpvfMI+meD4cs+eHOhum+DevX8zCwkne5bwrkewxANo+5khWwszXvKo9dKBvn6CmcwXHOMV2nbdu01nv4XvJkK+xUnP30RFGzXsqXKyP8uPNi4YIFhB+Onc91/n0Nf9MNx4+Tr7izJO+4n0LX+BsSHeiie+aZTppkYXgq4mbbPik6KUB55Y+HJCr66FNpkDkZQ6BV748P9346TIJw8TqFKQUpF5BxjZiFyP8a5XF3Jo6rB/zNpKg5rXE99xGGTpr3mJRjT4rTUwkJ836+yv0eX30w73N2kd9xDh5HppI/yi4Mw4v0S0l78k5jZPiwuStKd4Xj/rcnejiFaFgH4Hw++crTs9z5w31+ffXJU3+88WJoY/fEQ97e7Yp1xJKyZUpLOXzMGohMQy+stLfXh58ClMP0bjBhKBCKo/tUGlww9L/S165BWh81dsJPkjt3erNavblhQnPPRNSiCc9zwnOPLxoGAmJXeObYgHh5UqDhed6DeI8nw8afcYSVh7g+o/u0sD/jstf4TwHyAOdVk17o9YlkGexPz0J1T3GHYU6r+vNAe42lgDsFwir8LAXDTwFL6/DTLpDvZGzXVEaI7n6GqjQ0ABgAdY6im1D2/ZYClgKWAtFFgUA66R+q0oguItn3WgpYClgKWAoEHgWs0gi8ObE9shSwFLAUCFgKWKURsFNjOxZWCpjT8K5iiIGQZxLWEQTQ9TjXwowsE8S39AyguYnmroSqNAxmgqfTp9Hc9yh7vSkY+KClvEYZgSL4RYbn3FN0Q3vNncwh/xUGy4VooUk/slLCcq17X00NtbD6pt1hfnlS3hddtBI0PvcNrQui6PkPg93gXl4gtMmwv0coBdyRKyP04WF8mG8QJjAfSy2cBIYBS3MbFLUwvuOBvpyLhvWg9h7ep2XSCbka2mnyB3rA0dx5Cj2Wb+EJ+/Qo9cET9qEJWpNVMm3adJRA348yHEA4RF0y1ju5t0D8ncOEvO8Qqwbjv+k9YKo4ScF+seggT4fzQKe/PGAMDgPLyvIu/igoU3yQVQbiBxekvIJSKruAFcOSOSy06W79G9qx6CQrEHgrxhnaFFNREUbgjWlvYs3Hk7Y4Jf8YDlIGAvBXaH1/GH+nEcCCm6z1xhI5/vJeZNHCN9wrOsuCgD+gJlD/AX1R3z9tQOQJRxYxPD2XWA2nL5yWLz6fIVUAfFOjWtVon7SoHH9Uv4uYGixT/uEHH6PoYwetweU8iOepP6aY5Uwg4bHUCsvFsF4XcSxYk4plPCisgy5flLbtOsiTUCrcXbBI4ne4h2h+LLFvFqOx1vku1vlicUH263NUOWbF5G5dngkp/+FLobmKOl4FOt9MWbRokSqcvih7n5+n0n1kJFJhbN2+Xd5//yM5CuXJMuk9enRDnazCwIqZKM8/j1IiLIeO/jGz0SgP9pE12j7/4kuURmmHax5T2rlqirkQ4Dhu5z2mWKX7bwZ+dxaKb5ZHiRO+z7aop4DOD+b19z/moYz+YXlh1PBorwoRqnuKBdOIze2PdRT1JI38N3IxcewUPKa8eeS/9dF+A+sokeec4EKhUYSCkydmm6FeGa1itr379sqaVWulX/8+CmR0BfW8cqKIJQU9FcRJFB4sj5P2Wkss+IQ4FcYtCGLiW/D9LizxZPq8gyg4eBvl8rVoIK4x33vrG5USkSKpNAYP7h9cnt5V7NJbY/HC/1CKfTzAwgoCBZKVm48BGZIKh3zIcu4s3Mhy7nGwA2ERT6M0zqFUP70BTYDXwYKMxkVFxUVFwfppCiwGEDKDEsh+nEYFZa5z1kgjLUgDjq1Vq5Zad40lSmyLXgqwwvax48eitxPBbw9VaZCBeLgvMtH7AoISPjqhLgbQwBMaXqD3/UHsn6F3WHmOwtOAFXHc9AOzTDvBs1hunJY5S6RzHgms9PLLExVbpF27toorTtHISrg//u8nmTdvvl5TpHBhLR4YG+4gCtQtW7agivIE+fc/F1BTiyeb+qy/RUFMIC5iZ2TPmjUEyvi6lxpiHDMrI9Ml2qNHd+B4pNQppFKjsqDQ/+GHH4FZckBxVAYPHqT1tFjEcxGApb7+5lt1tQ0cOACVmZNqOX+Wr98P5EKOhwqTv5UDyBWLO/4MbPTffv8D6JeHFJq4V68eWhyR7QKUEBWVa49iW3RSgDtlyqBAaKEqjUDopO2DpUB4KBArVmxFNiSOdrJkyVFZGBgkT7eBBV9Ay+qPHz8WFWRHKJRq65YtXPjyALX6HK7I4YDnZVHDPQBnuqO8YgDzhZDBYxU1kQh6DYFNT4vfm5vqIgQvsUYmT3pVJk2cIAmwM9jz77+KU05B4CkriYBQmRFDJOa9cUGpiwn/uww0Ru4SJk58WUaOGgMFgnLyQ4doplMDFG1kDOgDuLW4e8iKZ9wG4iFdYyyUOQlY99/M/E7eeedd1HkrLVehEN6c/o5W3m0LBUjcDrrquBPiLsk2SwFPFLBKw/LFQ0sBuqDosmrRooVC7TIwzmrKWvYc/04CAZ4Yn5DMJghLFljMA8RFxrA2A6SpPvDFEwQDO12D8K9atYrL/QWZSn//XiAA0t/vHnehkD8N99d7730g/fr2VZz5d/DvLqhC+87b78kwYIYQMMzp9jXKSbHnofDYnCmvVEwJUAq+ERALCQ5VCjAA24AsSJhgutEY9E+WJJkLjyZY5t9EGf1kUJD1AHGcLVtWvZe7KAZWGZ8pDfRF9ZcDsbBW7Roo5V5UFWBkFyh9aJnuERiYVRqPwCQ/qkOklZ0AyH4lUSY/UzBIV0iJc1j5bBTUTqWREcJ49OiREPJ/Aq98mSxc9JdMf2uaZISApQBPmtQFJkXMDu4UDCCSO40ZiCcU77/IdmrVspmUhmX//PPD5BQyEY2rwX2XYf5mpuKmDZuBOHlJgZ9iBveV73DFG5Lq62IBCIv9d7mQXM1Udza48fqD+QT/zp3EdfSf7rvnnx8qi4HfsmjhIuygXpYBg/pLxQrlH1WWseP2gwJWafhBJHvJg0OBeMHpqeyxgeI1KHvOSsFM672OFEYGteOi9DxBmOIhbkGc9hMQ7K0RBM6dK7f0GzBQffswyxEYv4MHz4wqYtp7O/TGUHeaNGlVYL8LONl2SFutUrmyvPTSBJn4ysu6w/F2b10Aff25YJHGLlhW/iBcYuxjBvSB7zSVTqmwGKzXlGPsDi4h0M10W/aXafKMgZg0XGKuEwlz7tx52KVkkmxZsshpjP00Aur1gFefB9C6w4ePQrbW0RAFxJL8RqE+OBxgexrZFLBKI7IpbJ8fZRRgEHz79h2SC0Fnun4YuM4O+F0GEI2ANpb4n3AXfffdLLhpDkMwxpA+/QYAhbGrZAG63gtjxsKKj6UIgR07tFMBy/szZcqgSJD8N5UTkQUZ6PYk/KmwMmfJLEMQqH5z+tvy269zEafICPTHejgbgpx7CHsDa+wkEBUC0f4GwuL/8IMP5dtvZ+K6ONKvX1/JgQB1rlw5Qt5JwDIiIVK4U/m99NLLiMn8p3C0r782VQoUKKDB9GxZs6kbrW+/gXLl0hV5DgBhHPM1KKDpb72Dsy379BllkVpbvXo1jaNcR8YVscyPA5/dFiyNMhZ+IF5klcYDMU22k74oQKFNd0ypkiXlj9/nSYb0GaRWzeoK20rcdj2gGXwuwsQIaHUzPmHcOIrVDeXCv99/7x3F704KNxBjIopeifuHIuDMxn9TYbw+dcpdz3b2ke9hbKI2zo0QyvUMsphS4GBoYsC3Xr9xXWMGnoLnejodz68NXPPH4SY6DsjgxMj4So6gOLcAH3zwbsg7mzcngqDr1DavGTv2Be2COUDI8x5M1+Vu5JnOHaUmoGv5EI6Luy4qnXHjXpDT2J0kQEyEB1c1roK+Hz9xEkr1e7w3hR4mtGVE7Bo0FAhVaXAx0bca2qnch5mkt+GKuHQJ7gy4A2yLfArQsiXP+WvhmsDtq69Ncvn3g5WEiVd44l0qCFrXpvHfpmYVzyjwLINL8N+pY2WCwy5oXsYX7tRm8kQVAyvAHQ+ztYzC4rtDAxDj2BnczpEzhyoGBrRdypHKBkcOFYMc+VSIaxioYJ4AN8F07mKYAcU+8/18Vgqezwgek+lvIsR8qHBM3/gs7jTS4dT7G1Nfk1h4n1PpRv7s2zd4ogBdrHSpBkLzqTQYNMwNX2d7bNHJ9NF9fD06CEYaJE6aWJ5+urVkg6vjUaRBVNKd9E6fPr10xGnwjHC9mEN3/vTBnO9wZhx5jTn4wInxpqycgp7n8xhoD60ZxRHade6/e7uPysM0M07zt7siuh18SK9L12ckNXYVnmpWuZ7nYRw8mwS3GJvdZYR19iLueuUDrIlaNWrI+Qvn9d/RPR8+lQaZkPnkWTNn1i35o7jb4JhppXFrz6X1qJ6Mj7hl4PtJiiuOeES9OrXDjIn+KPKnL2qSHtxBFca5lPDwrqVnVHG97/dQUTA1mi0QYJ9DdU8ZjOfo1m7ROX0ceyBMVnTSICrfrTEELyemo7IfD8O7LO8+DLMYrCwC5LxlqErDbk8fDqZ70EbxKBspD9pc2f5GEQX8r/gfqR3yS2lEag/swy0FLAUsBSwFHhgKWKXxwEyV7ag/FLgDxOTP1faa8FLA0jm8lHvw7wtVaZjDUI96UIwnY92zVR786Q/MEbhSZWOEpJb620uDbuYpS8hdyIU2n2Hle3eEPX/7HNHXmX47edUXamB4+m2QFc2ZEutKjOhZvPd54UV+jIyehao0NG8baXkxY7nq3DySDauDpaqJK8BKqAHiWnxop0ID4ddZtM91RsDfthlly0+dOiM5c2ZHgcJMWq2VB92c6H0mi8ic5/D2fJ6FYCqqv+/nATqDUe5vf8N6nUHzc7+PPTWlRczZEme/TcafJ969zjIk4Gl/xxkT50KI6rfz778lPU7H50JKvvPEfVjHZK8PnQKK6XP9hgKCERQuuuWPb+Q+LLjdf++WlStXS8NG9UPAW0If5sNzBSfp7Nlz8ttvc6VQoYJStEhhm0kVidNLIU8I1vl/zNeSFlmAgxFamjOVCw8/DR48VAUY0fGyovRHbCicCzgkuBplzA8BQIkVbMuWK6NVX1nXKXeunFKiRPF7zt5wx7IT4EnLli+XBij7wYNu3pD2qChY62nGjK/kiScaSRYUG4wMWFQWQFy7foMcOLAfMK6JkAaeIATvgpVpWcWXdGBZ9YULF2vFWtLgNhTwDz/N0bRx1pgyWYDsd9CVIKD8zdCaVDyPFRqdqVhiwXBi+fhx48ZLxvQZZfKUiVqC3WYXRt6iID8uXLRATqHc/VMo4R/dtPaNEQ46HAEeAevx16hZTVKipMDNyKNNQD6Zi5WoZkuWLJUkqHBKpWFb5FGAuwLWTlqIqqvFixdT8CR/mu6IsTsYOXK4VChX1lXAD/M29fVpsg7CNgMODBLBjhViWVpk7u9zpW7tOsh/L6m7R+c5Blp2SRKjuiz+R+vO6X7xZO1fRrWAr7/+FrCo5VCnKrOYa8zidr+H31PAu2NW8CyUJ1ePOdm+YsVKWfbXX3Lq3HnZvHmLVEKZlNhQksmSJoOCyKyFBg3ynrFGqRzmL/hTUqdJrUrD9IW/kx7ffvudglTlhdJw9tu9z4Y+7GPVKpVk5Ijh8g3GzHLxtkUeBcwOcBPme+/evao0ojueFKp7ikKT1p+/29fII1/0PZljVzcHFqBtkU8BQ++w8hwFsRG6VAQ//fyLLF22TKa/OQ1VXHNqkcArsK4ptFly4xAwJSZOngIrPJGwjlO6tGnUzfM78CbWrV2vO0uW2DCNgpSgTD/++CMs/kMoLFhYWjRvrqU2yB+mAOHPv/yqln3VKpVVIS3HTv0XfMcSILy+UMH8MMaOyyZAqV69GiRr166FgiwOoV5XT2Hfc7IbbjbuXvoAVY+fFatWa0XaTz7+EMotkXaPyH3Ll68QKpZ8wAanIiEtdHdABYUP27YdO2Xjxo3StEkT/c6JCDcX42bdqVrYeazbsFGLLJ4+c1ry5s0rzYBQmDp1qrvcYJHtjot8Tntw3mDmKhB6HKrSCIRO2j5YCvhLAaelvmzZclR6zS/Zc2R3xTdolaMmExUDa4ktWrxEGjSoBzyJv9Ri7t2rp4IfpU+fDnjwF+TTzz6XAjhNnRv1nyh8d+/ZA7cM0PcSxJcSJYtrqXRiiSeAq4iK49btm/IV4FbnwbVGXHIqjIUoSc7dTpWqlQFFe0kmTZosr746SavSjh49RkqXLiV5AbP66aefozZUChRRrOw14YLKjoqLNYioWM7DLUY0QioU9o9KjyiBn33+heQFMFSKZPmVbPyNCo39nzRxitSqVR2+8ZhylQh9+C/bj3Bhzfr+B9DgWY1xHIOHgbsTVgmev2AB3HsH4f4bqDsZNhv89pcjH77rrNJ4+ObUjiiYAsSTyIky6RSMLvcV6vY4suCKFCkoPbp31ZjFTxCax1ERNgsC6EWBC05//4ED74T4+bnj/nnOr4pT8cGH70rmjBnVuqfwPAd3Gv/7+qvT5MLFC4B2fUVyQdEEYWfz1VdfQ6HEVKxxxgz+BLjTmrXrJCtQ9OLFj4tS6XWlYf26ENJHZcXKlVIZrp+wNiPAiXP+ZJOmAH/6TyvpmkYlt2zpclmzZo3udNq0bukKpgI7hLuFd999X/s9Yfx4QOHm15JBdOEVKXxBleLuPbtx71rFGWE8yLZHmwJWaTza8/9Qjz5JksQaLGaVYpebBlEKZP8w0yk+sDIKFSyo40+ZIiUyBG/KFQTTTbsa5AI3crrICJGaE8FzKgz6+Bl01xgMJDDL7fwLn3NCCGjic1NpUEkRRjUNLPbdu3er0mjSuLGkT5deA/fM8MqpVWxdgE3HTxy/7/m4CowMNme/uXvaf/AAihamVuRA04g5fgtFF/9Dv6lYTp0+FXLvTGCJL/lrqe7UGNNjmXd/qw7f9yDsAwKaAlZpBPT02M7dDwXKlC4tX339DXYMByV7NmRhQdJfuHTRURLdlc7riiHcndqbGAqHLp3kyZOFdCE7QJDmIE7C5BCi6PGuKwgm09Knz3nqa1NkI+IULwIM6b333pa0adIgmyqLxIkXR3o82831LtcLZR3iCrT2md1k+hDWGI6TNibexmQNgjaxvLtpBKdqiR1G2zZPSY9evYAVnk2eACLgTSgMVut95eWXFLfjlVcm6W8Ej5r13WyFvX28Qjl5+90PZDHcbPfTv/uZR3tvYFHAKo3Amg/bm/ugAIU3A9xszJ5qjBTYjZs3y9Dnn0fcILecOXteWiL7pFLFChoXYPYQm8GMYVD4LGINn332hWxAIHg1XDJjx47XtNxnOndyPW/9RunVq48UgrspQbz40gYl8xMAve88Mppiwg3VulULdQONGDEa8YOXpUvXzjJmzHjp03cgYiVp5STcPQP699MdjxMzhDCutOj9aeyv7qAo8dEYO2G/mc3Efi9fvkpenvCKZp9179ZVdzWXg65IjhzZpEuXZ+S1198AimAmxD3yABIWULbYUTVp3AipyQiwjxglr706We/96ONPkOq5EN+v0zNKzgC9obONbfgzYw/XNVZpPFzz+ciORs8dQDhOnjRFevZ8VlH7uEsYMWKY/D73d9m3dx+gXLPqwT+6Wfr07qXQrQwiF0SW1MCB/SVN6jT47Yakz5hBqgEprzbKswdB2KZOnVqvY/rv6BdGyh/IMiIWN3G1GbxmEPyVV16CUsigu5jnnx+CzKgtKmhLlywBxTNali5ZJtcQAyG6IHcxiRDAHvLcYMkUfK6jSZNGmt3lBH3yNJnsB7O6+D6mD3MsLtdbbEkHhL2KSSpKdZTxZ1ZWsmTJtd/dunVRVxr//cQTDTWYTaS+uHHjyUsvjoUrLacGvweABmsRb+FB3v4D+gJP/A9VToMG9VOIWdKLbRXOvUyc9KrEx1gN8uEjy3iP4MBDVRpk4mtXrz3S2RJaXhoL2vp0o2aFkOe4C/C3dA2vo/BiVtKpk6dUEDNbikIyFc4WPf1U65COm9PT5cuW1e+YkUQ400z4GMCnVgqjenfjdfxkRwC7W5fOIT/yHvJHbaSpEhWPmVkMpvPD33hPKexU+DGN17FVQrCZ/yZfFUTswPTHF5U5rgwZMqC/GfB8YtwoGpRmUTVv2sRjv6m4zLOJNlgHULLm75rBmODcvfCEd8P69dRtFQs7oY7t2971PPbVgGT1BPZ4RvRBFWqwiy1quOPRfAtRQymDAqH5VBpcDIkSJdJTuVyUj+JWlGPm2DMDiEohOwNh1h7iPpDetGqzQTjTBeIPz5nzCGVwUI/NCGL+25TXMP548zznqVr3k9DXfAhBcx93EQywm+Z8nvuJXf6t7yf8tuMe50HZsJzy9XRyW407L/12Ptv9Om/9hjrSoXk6SMaYhzl0+aiCs0XlEiSXpUU69Q1m60Xli728y6fSIEMULlxIMygUrB5/P2qNNEgOV8Xg5wZqeuKjSIOonHPSO1vWrDICJ7vJc/zb32aEsydF44/y8fc9vM4p/P25T98fCCven846rvFENwvMFkYi3sflpD9lTuuWSJMO/ndE83JYuxeqe8qcRPTXVRDWDjwI15vDUc7KoQ9Cvx/UPlI5MzYQHnpH94J6UGke1n5bOoeVYuG/Xr0dcVGVgynSAeAKDFVphGfhhp88gXtnIExW4FInYntmeS5i6Wmf9uBTgDtb/i8QWqhKIxA6aftgKWApYClgKRAYFLBKIzDmwfbCUsBSwFLggaCAVRoPxDTZTloKWApYCgQGBUJVGgba0Vut/8AYRuT1wj3lMLpr2UfeSKP/ye5pseyRpXf0z4vtQfRTwCBQhiU1O7J67RuECVksZ1B87RDgHYnsxfz5Rykg7D5RFGBxUGPIeQ4gsibmUXwuURJZAcqJLhebac7BFWofRZrYMVsKcA0QkZFlZgoCMCu6D1P6hntFZ/fs+VdY8XLI0MGSCKUHHgWl4YK1jAlI0P8BIW2rlqIw5bUHD31eqletJnWBgsb6Rjb1MGIWNZXx2++8p/TsghPX8VD24tCRozJ58qvSvv3TUqJYsTCd2YiYXtmnWApELwUMauO8+QvkIApvvjR+jMTAGolOuROqe4qgLoTffJRKaHBCiJ+QAiUoiA3epUsnLQtB3Oi/Fi2R5s2a6e83gstBRC9bPRxv5+KIh1pI//vfj9IaeA/xU6ZUhLnt27fhzEZ8W2H14ZhmO4pwUoCVis+eOxvOuyP2tlCVhsY0HkG4V2ZEFy1aVKuBrkaBtixNM8kqwGwSSpOuusDImI5YZojOp7FoRa3aNWTGl1/K/v3AfoDSYPG8fPnyKeaEqdcUnX2077YUiC4KWLjX6KJ8GN5L1xOLwBWD4li1ao00QzE4Ko/CRQor/jJ/ty3iKEB6sr5X7ly5UN57A5RzHvlnzz9SDQX1EiKWFggBwIgbrX2SpcCDS4FQdxoP7tDur+eM3dCvTgznDz/6WPbt3w+oz1PSvEUziYNS2FaI3R9977kbrj66qCpUqIA40mYphhgG3YRFixWN4BfZx1kKWArcDwWs0giFenlg8bL880cffSrp0qeTPHny3A+97b1eKGDiSGXLlZG5v/8u8xH4I+ZDIRTLtK4pyzaWAoFDAas0fMwFhVUG4DnnBi70iy+9IsOGDUFAPGO0p7wFDvtEbE/o8MuCstsJEyWUTz/7XJ7t3h3B8bh2VxexZLZPsxS4LwpYpeFLaaAkccKECaQE0NaIlla+XFlXeXTgI9gW8RSgSzAhdhfVqlZVGNIqlStFa2phxI/QPtFS4MGngFUaocwhQXyqVq0sy5YsVDeJjWVEHtObM0Dt2z0tHfBRJLww4GlEXs/sky0FLAUMBUJVGjyfwZOI0XmYJLqny4LORO0MGKCrR5nnopbi9m2BTgFCvQYFBQVEN30qDS7efPkek569egCkHmmmj7DVZwVY1PGrpXXU0dq+KbApwLXAdPRGDRrIpUuXAqIKhU+lQRD7tACbz5AunboJ+LdtlgKWApYClgJRRwEqjYIF8ukLA8E97pd7yn+U5qgjpH2TpYClgKXAo0KBQFAWfsc0HpVJseO0FLAUsBSwFAidAqHuNEJ/hL3CUsBSwFLAUuBRoUCkKQ1PgDqBQNSo7Jd5F8cdSMHdqKQBxx7V74tMPnuQx/Ig9z0y59Q+O2wUCFVpKLZErJjInPIfO8Kg/TFszgwsd4EZVjS2iGJ20y+SKCp8hAZUyBNoU3jGFFa6eWMFVsyMiR8xozioGLkRKyfNIwr9Max0CC+t3ZU9n2Pm9EEEhiJmCVtkg4iFdX7CJrIi7+rw8ElE94YVxZmlGkhGpvsYfSoNEpGnn69cuQ5Mg3h+YRoQvOjY8eOyfPlKpOkmlVKlSkqSJEkUvCkuCOJs12/dlDgxXYzsbEagG6Q8529crJ7Q3Nyfbe4xi5v9IvrVps1bgJORHIXxyqPwYOxIyQgzi2btuvX6TpZSL47Ce1SgnsZEYWoWtOm3c2EbBEHzm/N6p/KjMoiFOaOyJgKeO01jxogp165fk3Xo1/4DB7RPOXLkwGUEdYlo9hfFHDl75qysR9XaS5cuS4Xy5SRVqpQqeN2bUSie5tGMkfPlHBfLjnCBudOOz/b2PD7LvCOEN4LpZmju3gdFSgOBzp07L2vWrpWrV69K2bJlJTXG8iBlFC5bsUIOA4WzYMECWnKehgMbaeLkSyeNnPNE+pC/zD38r6GV+zPMffw+Fgp8ujAZ7zTOjyphrEvOo6my4HyeN4709DzTJ1/3O8foVPpOw8bZb/Msd16KKOPHfXxUFMTNiINCqVzzgao4fCP3YTH9TeChv5ZIi5bNJSVAiczBK08TahDvli1dLoOeGyq9enQHgxaU5EmTKhEOgmHnz1sgpwEhmzlLJqlfv54cOnlSeD3BnjJkyCDFixeT9Ejz5cRQuKzGIl22dIUK+rp160jiJIllzZq1UrxEMUmcMJEuWgqnjVAGfMf1G9dl29btKghz5syJxV0GOyUXyx45elS+/fZbOQAErDlzfpRUwGy4FQklQUypkalT35CDBw9JL5xzoYDm9/wsW7FSBektWPnlobwKFy4kK1eugoL5V39/DJgdRYoUwXhc/SYjsYDfv//8K0WKFpHaNWvIzr//lgsXLuK6wnoPrzt37pzs3LlLMUB27Nglhw4dkqRJk0gplEHJni2rKpPrl2/gmp0yecpr8lSrVjJ8xPNKK+4kI7px/v7+e7eMHjVWChbKL4VRiiVt2jTy7959UFzrVAinTp1KSpQoIVnQ52vXr8us2T9oCRH2+9SpU1ISfS+GMfNZp06fkd/nzZNjQPRLhzTwmqBDElxH3I2du3bJTdCTwiddurSu3xIlkvUbNyn/JkmcWGrVqqm1rdasXy8nT5yUKlWqSNw4seXMmTOyZetWKVK4sKQCj/+Nkuwrlq8An56RbNmz6rOS4v5Lly9refzPP58h0954XerVqyO3A9wq5JwqP16/IS+8ME7iJ4gv/Xr3luxZs8mqNWskefJk4L/CwsNjK8CXNA6LgVfXgT937NgpV4KuqsJMkCAB6FdDdu/eo8K+fPmy4LkYshR0ohJlyZcYeM9y8PHyZcslI2q01a5dS1IkTy6nT55WZXv48GE970WcGhpS5zH/q1av1jWaGHPFdUq+PgGZQJ71JDi51vm8teAfwlCzbzeuXwVkQVEpW6a0bNuxA/25JoWgGNkfI3iNIbcKsuPy5StSpkwprWvGxmvWAfBrzep1cjUIkKrg06pVKsvf//wDuOszWkKI1/7z33+qdGkIR7TBSf4+A7C7b7+ZKZUrV4bczA9ZFpjlinzvNEBQTiDBhxo90dCvnQYngYdQKlV8XIYOGSzYaOGbGCgtfgBMO1YnMmPGDCrQqlerJj/9NEcmTZysSmkjFvjMmd/LwIH9dNJXrlwtL4wZJ+khBI5j97J1yzZ5Bih6w4ePkmnTXpfSJUuEyLkpr76OXOb8CuCzatUqVTBz5/6hi5xCm0xTGX1KmSKlPD9suC6S8DZ3i4VM524V0AImpnq/fn3kySaNQ5Tg/378ST75+FN5DJbebWJ2AE42ExbYiy++pIKbDPkHFGt+KI4BoAOZ84MPPpIff/xZAaB+nvOLWtcHsFNYuHCxvP3Wm1iYyXQoO7BgJk9+Tdo+3UbG4XkFCxSQdFDAP/04R7p16yIVHy+vtbQ6dWiv88ETpux3sPEYZnLc43riXLvtWCjEc6Hg47hxYyVThvT6DqIhvjn9LRU0cbEYZ333vXR+phP+rqgYGvP/XCibN22WOrVrSzYINy6o09ixjBv3EvjgGJ6XQ0vV5wL2Rk7slF58cYIqzDIQGhexo8kF0KaaNarLpi1bZdSoF1RQnTt7Tnlh4sQJoNk7MmPG1zJ79kwcmqov/0EZ9+8/UD7+6EM5fOiwvDJxkmTFezmYffv3Smk8NxGEGpXx6JHDZQueez/8E2ZCR8ANt25jhw5eGj7seamAGmpUjKNHj1HDhAbUCSjRNm3aomROVXn3vbfkLdBoC4yv8tgd3rp5Q5KBx8qWLQ3o41Fy/fpN+X7WN4o3M3jwEAj/C7J920ZZAH4cg/WaF5Wh/5g3X9av24C1PUERLwcPHgrjoLikT59efpj9P6z3Fjr/fM8uKPy+vXth7i6hT2N17ZcpVUrXrPu6Ii+Qz4cOGS558+WVrDACLl44LylTp1E++eqrb1RWTH/zDTUmzf3cjR46ckTGjB2vxtfs72eqvCC7zvp+tvYpd57caqARqbQmcFzm/PyLekzefnuaJILS5Jgo1H+YPUvic80Fez04Pffr7uZYuR4pa/Pnzw+JmT8CZj1yHhFqTIOCgdslZ1A3tK7wHnN6PAZmhS6Rzz7/Qi5Di3/95ReKv30NVmEsmMckVB0I+NdfnSxnsPCbNG4mc376RQqDcVjplBqXv+2Exdqn7wBglu+BlZJTThw/ASt2j/wL7V8FVgEVVZ7cudWCbA0LeshzA7FLWSd9+vSHNV4IuN5VtdssicLmHA//7Wl8LoF697aafb8ES2UOLF42Y9GS+bjrcZ6a5720wvibcdu9+eZb8kznTvJ0m9Z6/1Uor1PYeVGwdYAwb1CvrlrbXJwdOrZXq/uPP+Zj0Q2QJxo2kA8++kTef/9DLPDWWLSJIUxPyyYoW1p2ZyEY06ZJCxdQKj2U+fLLL8GizCIjIRymv/W2LuY0+I3tOqx6zpOzuf/N35yWmvu1XJzff/+DlIMQ4g6RlFI6YDGZOlJm4Ru6m2dUrlRR3nvnLdDsljz1dHtY71+qEKGhUa5cOVQVniAfffIB3JfgJTxvNhb19h3b5fvvvtVdCN/DOT9/4YJawZ1B02ZNG981nk8//VwyZcwk70MI7oOSfbZ7L1mMXQdpRIExY8YMqVenjsQCfydMkFD7PHfu7zqHL780zjU/oBMVH78jfW5jTnldWNZDaOslqn43gonv4xhy5Mgpu2HZ79z5N3ZVpxQCIGmSpNgBuwpHdmjfTnr37B7SvXOgNel5Af9dsnSZ8mwWAGedS3Jed42fY41XhGFG2q3bsBGKeBB2epvVSs+eNatMn/6Geh36DRwsH374ETwNdWXo0OdkHAR508aN5c9Fi6Ggbkm3rl19Vjfm3GfPnk1eg1zICkXubNwpkR88ta1bt0mKZMmlAOZ+HXab5AHuOn744X9CCISxo0fpbZeCZUSc2HFgLCRUZcvGZ5MuKi/w93HIoKlTp0nFSo9LQ3hNfHlh/JljNUbBi57WoT/3R9U1oSqN++0ItT0tiB3bd0iVSpWCFcYNnQiKLG5JaSXSkjx85DDcDUmh8XOFWNMtYZGwZc2SBQyXBDuUw+qHZ9yE8Yk/F/wpSRE74fMyw+qIuYpC/ZLeUxpWO7fB3HYapeFpPCbw5GIFl+CjrjACj/5+ozpuBsPfJkmcRGbNmg1313dqjTWoX18t0ZhIGnBvvJfPouuITFqjZnW9hNYJFxStbS5ijo2ukjOgRV7sKigct2/fru+mD5qtVKkSsKa+UldMHLhWjh49LuMhYB9/vIJaKNxJ0OVDpUCLCYSTRo0ayiBYhEePHpO0EAyemiYtoD/OsWL/5BKUpAkFpYM+tFDjA7ub73n11dfgXkis76mA3Qxpc09QxfFS0uIyXD0H4a7gwrwWdE0FOXmFjUKJC5CugbQYC90NxAsvDeuTNLmIe/kMujToMqJz7ssvv5L/YEDwXpa+aQ2+2Y/dSFW4oNho4aaBa+wfWJnkFfbzGp77K5REHliYHDfngcpv1qzvZRqUO+eJdOdCfhgrG9Myr1atqvz661y5Dlo0gFFyHPykcw5l/tuvv4Ke53XsXH+Vq1SSlClTwFOQXo03Ck26S3fCFXoU65Hxq0aNGii96QYkzXft2ikF8xdQtzHje/yedOeumYYU105lGAv9oUjixYsLQ6kt7nO5p701187zNHaNk9VrwfmpD2OLOxRPjeOh63M9YnkFCxcEvEFm5acm8AAkjJ8Ac14cu6058ln2nFKlaiXsbrPqY7i+uKt57bWp6spetHiJSyEFCwO62SdPmqz0eQI71gc1AcArob38EOlKg++lYGHMgv5n09Ryw+RTc6/Elqxbt2d1Qbdu3UpaNH9SzsLCvnbtuiSAi4ctRkxkcWGxX7pyWbJly6a+1ZvB/utF2BZnAEAS/dbO7Sx9gkmhhOj799YY7Pp5zq8QzjvUkmCjAE8LlxhjC+uwW6GAcy0klxDlQqteo5qUg1+X8ZGf4WJr1bqNBnrHjXtB/bieGoUgfcp8j/Hi6C4EdAi6GiSff/EFLOrZcuniJewSJqhPnttl9ofXsPG/t27dhi86ufaHKHepU6WGgD0LobkXCjQZ4j8p1GIzjYxOnzbnwVOLhQD5Ebh95v72uypw8y7uAqugwi+D2KSDoQ/nLhWEecOG9aVvn17wxbaVLVDgX8z4UsaNf1H69e0jTwXvpDy9TxcjFOi4cS63EndMHbGr8uYnuwH3CJUtjQI2CrlffvlValSvqrtU8kayxEnBA+mVn+iCDIJg4i6P9Fb+wf/o6ruC3S6/506scKFCcPv9JJ07dVBFwsVfHXM7atQI+XbmLLgtftA5HfzcII2pBWpg0itzh/IDBTl3fB8AmTJjxoyqSP84eJhpEUoxzgvdm1TgKSE0YyJpJQi0425iKeIWJ0+dhMuzAlxTO4KL6d0OSXIgrRgvunIlSNctjUK6lc9DCWXPlh1xrpEhsUbyysyZ32FNVcfuoz6yu3xnarp27rEkTZo0ChFMW8PMs6chM9h+FG527jSefLKpyo+Z330HI/WI5AOoGl233GX978cf5dNPP5Mm2LH2gUubjS5mvodxVib20CgxLT3crZ98+rEUyA9XswcXdXjnJdDvixKlQQsuAbb/DCyaZjJo6LYohdjEK3ClzPnlN/lz4UJYLe016B4/QTy5AKHJRoF3Ax/6UXMhwP3br79hi5pDKsFKoY+8BKwFgvcYtwjvoZA4hW033VbemgbSIXS4Q2BqcbCEUauXzKjBa/7GLA8Ia/M3r+SO4B8EyyjUiOiXAguLN3lLRKIiugIFRCWUAsLdbHMVWjZuPGnVshWwJCrL2HEvavCWMQj60vlOCjo2Lloq0/xgVDI5r6sG4UlXEf2hFORkdLp9jAvl/PnzasUZoe9OCwoJGk+kA2lg6MD/Km0wprvowx0IFI1uo0G/gwcPIhtrvyqrnHB5cB5UwHoJljB2w0A23RNFILgzZ82sAoRKnkrc9Nv8lwKdOwxadmzcDSwEn2yA66MmgtsUcFWrVVWXHzN82F8KOipLYzBQIdBNSv5hfIOpxhUhMJfCzcKdBa81LqiGsBoZE6MvfsjQYbBCfxGWa4+EBLNolQ+KXwL+6vJMZ01Q4HjJC7fB56R9SezUqVBpfpDfT4L+QViv3LE1fqKR7h6YjELepHuPypv8yca1wu9pyHF9JAe/j0DSBWn/zrvvazLLE43qK02pkIogkJ09e3bNzqLr2ldjv5kQ0xTCvSiSSDS7za0Zl7PhoQNISNmAOBm9AUwE2YOYDr0fVBrJkN3ZqUM7xAKfkgV/LpIp2DnT5U3+zgEZQ1cw3boXYbDOhiFhQnd05bVv11bXmqc+ROvkRuLLI11pUCiTcYj5zAygZzp31L/PQxkkgMVNi5iCkW6kPr17qjvmzWnT5aUXx2pAc+P6jSKdBa6Vo3L23Fl8lwvb13S6K8mTN48KEPr4i+P53FWoAA7eMfwGi3Tvv3tl4ID+IWmo7r5oChdmcTXAx71RAJVC8M690a00E64pxhqIF04c60mTJsC/mvmewo7mfaQD3SYpkqeAD/VH+Iqf1VTRc9hR0aIngzIdlVvsF14YKb1795V6EKrMAGPbs/sfKYD7161Zp1ZvNrid6Mr7/fd5MgRxgBUrVsHi2yY9enRTi5nvpauKO5Wvv/4Gz8kBf3TGkGwz7VewFGTf0sElQAHhiQa8tno1l5vH2U5BaTJR4dChIwhEppDqCB4yuSE+FJQGBh0xEyfdKZzTpkkNt0RFSYkdExccvzPXxMX9/LfxTcfHfDIY/QH84HRL5kFgnVl5zMwyFl6cuHG0ayYllLRl7GsrdkBsdGfQnfJY3ryy9799+CaGpEMfqmJcY8e8KJchEKicmPgRFwo8GZQUoWaTQTDsQ7aXy2XpOfYVieszwh/tnAeNPUHg1atbW9+zFfxDNyQb6Uq3D5txuJJHqQxo9DCBgG3hor80KYAupdQIRm9AfI2uwWNwhR49dkwzj+ieppGYGXG3StiZ0MD49LPPELsqDT5IE2LohWUn57zW/d90ncYN7rvZNS+BcUU3JBNNOJncQdJgoNFBt1o6KELewyxHEEUTdVyu2TumgnM34YppHJeXXnoZO95q0qxZ04DNdopoJooUpeG09nmAjHnV7WCp/QurvEPHZ5ANlU59jBNeGq+TQqFpcsPbQNuPwDZ2MxZ7jx49EBgdJh07d0H65WkpXbq0lEN63oULl3SRP/ZYXs3MIZPQLUFlRGt1Btwk+/ft0y1xz549cF8ptTwpSFzunbu3v75S29xtHvWPwtKnVd2yZTO1SOIFM6h7BgXfY4JaDOZy98SA9tQ3pssmWD0UlFR2jRs3UkF/DS48tkLIeipdugwyxKbLhx+8pxbVdGQb0e/KrKHewVvnDOkzSCJYzjlyZJeDhw7qQqdCJUMfg2+6b7+BKpxjx46LoGQ/zbKir5huQba75gkL5dot/1P8XALkpsZR6FJkUJGNCsBJB7PQzOLlNRy3znlwBpuhE3eTo18YI3Q3/oP049at2iCrqqNmnz0BX/k2CLWu3XoisymzKoxmzZ5UwUa6TZr0qsyDEqf7gxlXgwf2R3C8MyB6R0jbDp2QYXMRAdAC2L09jgyYWZoNxL7WrF5dM6lmzfwGz4orGxDAZbYaLe8bMGjY1yeCMwfVPRksUMMi4CJ60Yb3eWYnZeae9HcmbvDf/M61s70mH3/yKXYE67BC4R6FO6h//776m5NveA8TEsiHTPAYh11yp85dVfnWqllT+WLxX0td841nsrWCUvkFLmG6AOkGMgkj3tyn7uPl+7mLMX13Jqzwt19gLDIdlzzHa1q1bImd6SJ1e7do3kwflxYyaCQy65i+vQBx0Xnz56t7k0YcDSzKmS+RicX3mPFyjZodP5/BlPB3334T77ghzaE0bEwjvJyJ+2jxM3eegap22PaxtHp6LMJx48fomYwTJ05IJljlZLQWLZpLnTouS4cCrQzS+qa+8Ros11SSGZbxSxPG6bOSIeuhErIUuCuhRThjxufwTWbVd7333tuaMUSG7gMfu+bPY4Ezw6IQLFIKbLqXFi9Zqil+tL7pMgtP00OKsGprIaahAhDv8ZRuR4ZlH9555z39b0swKxVXDfht6aai0riOOAOD9akh2MeOHaM7DXPoacCAfppWS7OoXfu2khPKkenEHTt11NRHXkelVbNmNd1RPF6hgnzz7Ve6M6Fb7uOP35eTWLj0S/McCH2ytCGvIDbwBdwxTHVuA2HvsjzD7nghHag42zzVKmTuPAlS0vkgrDamyxJjnXjrjRs/oXEhzp0JeJqFT6jXMlCYdCPRAsyLnQGv4TieHzpElixZprsNKloaA/ERyBw7boz8t3evKl7uXOl7jg+aMEg6HjvWNavWqBvG8M8gKG4qG5d7JqG88spL8uyzXcFPWVRZtIWBw+w8umF4XoZnXNg4jk8+/kyzjXz50MPDV5F9j6ZHgy+YtHAVZy8YixgzZjTORqUPmQOeqygDw4y7Nqa9/4tdOmNtrrhvDHXt8FyP4VN+y132q68xGHxTypYrA1q+iDM4G1Qok95a1rtQAXlt6quSATsNziXdUZMmvaLp9/ybO8pRo0fgEDCMPh8BcCMjuHuZPHmiCncn//Df7ZHxVb58eSgyurWR6YadVP78jyGVdyRcTdlCMpyKlywuU6ZM1HhFrdo1JTl275QLjJHwHAkD30/ABUc+5eFkPpveCNKH7l/+TfkyDwqHwf3IOvAX2XwRnueHutNwIfe58vlDa7yGxOOW/+uvZ6ibiAQn49AFQr9gEyx205TwcLOwUfC6gmdx9fwFBRy/o9XNDxt7wF0BA2vmGgqKYljY5hn54LLix7SQk58Qjow7DB48ULNuKITuJyPGV162cbWMHDkCKcAUPmlDwFOoOOjH58dJhyJYCGZ8pENG7Bj40Qwr0LEWlI1zTLyGSpUfXkOfMV1pHC9/oxvA2UJO7EIg0JdP905W0P5+gLUodK958Cc7x1UAKdNvwxqjpcmsLtI8W3BA27nQTJIBDy66N/ad806h0qB+nZCfzYlw+rX5cTaz48kPpcOPaS6ecu2KzHNzYafGD+8hXzRGFpizqSLHF0w+YMYNs8S4u9Nss3t6G3hfmJ3cVAhuJkxkzJRBlR7T2p2VB+i+NPxUAFlj/Dgb6W2MD7N2UqVKgXWdUmlJXiqKtcgPm+FnGhepIYTNWuRcmp2pnhiHgcW+mDnxRUHyiXkeA+bOQ6n8zcylp2c4T54ngmeiLD0QmHO6InMinuK+XoxsMnyaJVMmZF5lClEQNGB5nsOffvvDFew/Ze39rEl/3nO/14QKwsSFQs1Oi9FfxUELkkJd0zjBSKbUgrM8iHmWJ5eOUxAZYefut3Te5+3fdzE8JoRWJAWxmeTIXvA5g4WRc2FyHGZMurCClbE7HZx/k4noJnPf/jqvcc8R96TU+C4uUMaPXLWn7pRvuF9G8nQ/30e3oREIZvH5Uri+fjNuRF90cO+HJ/7xRWv38yXmeeQVWsQ880MF8iDWnsobnBBCfvR0psDppvU1D04aO2uXOXnbuV4N/7oL5RDaetmte+NJT88z1/rbb+czjLwJja9Ck1X3s4bUYIaMLYSUYMYsA7k8Tahwr1QY/HBQ/h5eCc0C9Uf5OCcgrNd7m7zQ+nU/k+7pXl+xkvCMKTz3eOrX/eywwkqjyKB5WOkQ1uudytydDwO1tIM/8+KvQPXnWb6uCQ+97/edEXF/dPabspWHJekJYfNX1kbEuMP6jFDdU8bXHNYH2+stBSwFLAUsBcJOgehUXv70NlSl4c3q8ufh9hpLAUsBSwFLAf8pEOgKgyPxS2n4P2R7paWApYClgKXAw0wBqzQe5tm1Y7MUsBSwFIhgClilEcEEtY+zFLAUsBR4mCkQqtLgoSAejPME2/owE8aOzVLAUsBVv8295L8vumhttuDinu6VF6KCnmHtb1T0KSLewYOZTPUOhOw9n0qDOf3Hjx3X07ZMu2XOvbOEQEQQwz7DUsBSIPAowDMLLOvB6sU8d8VyPaGlgZoSPQSyYkVrPdiLVNKoCu6yzxs3bdHDu9lzZFOiRtW7I3MGDfAUq02zuGto8xCZfeGzfSsNTAJr4LPgHcsHsLaTVRqRPSX2+ZYC0U8BKgCW1WDp+Lyo8ZYHB0J9NVYnPghl8eKEl2U7EP9YnqXHs91RDbdhSF05cyDSYOnw3IiWN3EUtjQHTqkA3LHfzf3u97BfvI+W+DeAc34MlR/yoFglm/NgrXmvGYc5t2Kw50OqJmDsgXJ4k3Rg1eY/UY/tAMoIlUHZoeiucRWqe8qFZXD5odDY0b8UbQ8sBR4sChC6IDRoW4PUyWrTrG77/vvvoMT4QgWyKlasiGQBqNF/KCCaEmWEEuJUPYHGKFOIacJih3/v3q11xrgzUY8GrlFESxQEZHHQI8C9SICyJywDRAFPDBZWuWbfeIqa97GGFguCEnqAUABrgCHOwpwEeyLQGbFAqAQJ1cvDrQVRtoQ1sLgTOgpvCkGnWIeKvxEMjWBTrAAQKDsVFkpkkdZAaKEqDWo1Z4XSQOi07YOlgKVA1FCAaz80+FHuFA6jWuzGTZtQkbkJBHVeLdtP6NzVQM0kEt4wVK7u17e3lEf12D+hUFYuX4Wy+qMUOOzjjz/R4p0XUGGWOOK9ABvAOMrUqW8q+iILJB4COFTbdm2kKWp/nUbFa4JwnQEyJfE5thNdD9CvrLfG/i5bvgKKaI/shaJiVduOKJpK187EVyYDsW+TFqskgNJoFEnMiMq2X874ShEgXxg1XIupEn62B/pAV1AgxBA40xpbDq5OHTUz7/0toSqN6O6gfb+lgKVA4FOAiHbE4kiePKl2lnAFdGefOnNK/z55/GRIafTLEOCnsdsgiBMrz06eOFF3DL/89huwTcZr1WuW3CekLItUTnxlAsrZf6fwyg0BSZsLrrKxL7jwvCe8PFHL3JsCiNyhcLcwCff8Oneu/PzzHK16zZL3f/wxD4h936C4Z1Jp1ryV4s4T14Y484S1ZaP7nTAM3F0ZiOfAp37U9tAqjailt32bpcBDSQEiO0oMIG8Al4VN0f9gHauLh5ayokC64JwYW6DVHCsW4Jvhnnr7nXfVTZQIKIAMvl+BG4YYFXQ3lcHOhO6q3Hlzy5dff63X8X629wHKtQ2gbSyzzmv5Hv5WpXIlF8IngvcxgDB5DO4n4q8UhOsrR3CZ+4oVKypkNBsRKs0zTQl5d7C2h3LSwjkoqzTCSTh7m6WApcAdChCRkiBWR4CwyUYf/FnEHuiaosVOwWxAlhjTuHT5ElWLzPjyK4WQ7QvX1b9AVPz2u1mKzcFYAiEBGMMwzSThUPX89vsfsnjxEqBcjpJMGTKEBMIp7Il3oYqLWiS4lDUzP8+evQM3zd1EYgTr2QiFfOqkCwDtatA1YJ9zd2T3Gd742yoNu/ItBSwF7osCLHmfGnGHqlWqyh+AHy4I19J24G+nBfxrYVj3bAXw3a+//qoAT7/DTWQs+xtwUVG4nzl9RlatWKkxDAMtz10IcVjYqHAYyKYi2bJtO5TFWMDU1pFzCKCvAt44AZmI18MAOwPkbHRTXQQYE59Xvlw5+fTTz+STTz9X0LONGzfIc4MH63X5gdezasVqmYu+b926TQPvgRIAv6+JiaSbrdKIJMLax1oKPCoUoIDlpz0CzkFBV4Cq95oGpydNfEUR8BjUJvrke+++j/T9bxVxMQN2BwxIP9W6NdAt35WXXn5FypYpo2m6KXAP3URly5WVXECi5P3MtKoLJUENQ+VB6N59SEGd/vbbkjRJMiBadtB38RkZufPAdcyOqgTAMWZX5ciWTfr36weI2Zk453BDnn66jdSpW0uvqwQI4L937pJPP/8csZECQP9rq4BhgYxpEZ285ZfSsFo3OqfIvttSIPooYBRCaD1wQfImkiHPDZKevXsAO/t9ZEb9h/MOdDUBFrZIYXmHeNoEAgveSvCebAiET5r4suKxx2ZcBM2creje9Rn9m+cnCgBtcRzgafnvsohzMAvL2YBXqW6tLp07htzDmMaAfn1CIJlbNG8qTZ9soum1PAPC9yuOBQLpzwHHgmc9XD1w9SG6D9E5x+fvPIQ2TxHxe6hKg35E42OMiBfaZ1gKWAo8OBRg4NnfA70GcCtRgoTSp28vPYxGOFY9XQ5hb4LLFMim0YXE7/nxhajoBA7zBSLmCfHSvMscJnR/l44xuA/sWyAayaRtoMDA+lQa1MQ8bDNo8ADF5Q0kzfvgLDvbU0uBB48CFFAMKHeBtU8sbJ6Q9rdRQTBmkRuWvlOIexPGUWlFe1OAUdkHf+nI6xQxFbRv9mRTje/w39Gt1HwqDXbOgLgbfOewDNheaylgKfBgUoBrn2mx+fLmDReWvC8c7weTItHXayo6nmdhPldUQfb6Gm2o7ik7+dHHLPbNlgLRTYFAEFLRTYNAeH+gnEwnLUJVGoFAMNsHSwFLAUsBS4HAoIBVGoExD7YXlgKWApYCDwQFIk1pxIwZQ1PtojtoExWzoKWKefT0TlJIVLz2gX6Ht/LOzvINjwLvPNCTaDv/SFIgVKURHuQ+U++e+RYRAcJiUvL8Tf2L6pk0Od+qOBzNVKWMyFQ5g4zmTDv09F1U0yCs7+OcElzG3Ver3wfXKLLJF2GlasRfzywoKu+w8HAMGIxqQzlSayO+ZxH/RGOw3G+/DW4HpUGgIp6GZ14NxX0qDRKRNWR4LD8xKlZSCIZGUELDHjh4EGWR/8CpytRStWplzcAyB3rMi0lQk5pnDtQ42cAE4AjuEtLwbCeoiie2uev64As8gb04+2GqzLi/058gIN+3YuVqnFhNI9lQDM15ipQlDdhY+iCiWtCVIC2pkBjVPzkXmnN+9Zpcu35NUyNDmx/2Q4vF4b7oApqhktuGUhBk3Dx5coeAyvD7HTt2ybKlyyRzlszgnSpI+4zn9zmBiKKxfY6LAuQl4lxwnhImTBDqCWlP4Ej+rCFf9Dbr+X6f4+sdngCfwvM+PoeGLTFFWFsrSZKkkgwVdf2Rm+yft7Ga9RpRRhTnlZgk7C9RDsN68t2n0qAluH33PzJ//nwtA0Al4OtgjWJvYOGvWLFKXn5logwZPEhu4XDPLeR8z1+4SDYBOpLlk3PnyiWVUIkyTeqUsv/AQfnhf//TA4QGi7xWzRp6PoQW5yaAprAu/7mz51EjJp80aFBP4Sfddx3GStiId7BeP3OaM2bMKI8/Xl7y4H1nUfp4EdCvWN6ABtB1KMN8KBlQGkhYq1evVoCXYsWKKm8tXbZcbqKf5cuX84mSxf6ePH1a3pj2pvTt0wslD3LoKVPjevnppzkosXxVOrRvF3LGxfwWVquG1lssAMksXbJM1gFghidY+S7O0cp1K5VGL7447i7L3ZOrh30+d+681tfJnDmTMg3Hapov95CvPnu7z9P3nFcurG++nSlTJk+U5CmSqyXLay+j3tBfS5bIwYOHpCTwDBJRWIXhjIAV+BFDAQoq1n5iOfKs2bJI3dq1fKZ7kq/IR7+gftPixYslU6ZM0qxZU12D5gCf06Bxd0964i0+8x+cKmfl3OxIOTWKzJ1XvRlK/qwxrbwLeTBvwZ+yYP6fWpeqWbMnJTsMQE/n0tzf5XwH/82xLl+O9fj775IhfTrp2bOHpEuX1uO6NM8yz9gDlFSWlM+cObMqbE17Rv/OAjPkIAzxvAShQu0u53oIqzuX88qNwJyffwG6YiJp1rRxmNN4fe80MDtnUBlyMwRxWOrLX0OnKleqpGAqbGeg1d5//yPUrT8ndWrVkmUQyitWAoQFICgkCioqyw+obc+a9s0AmsJGwbJ+40Z5BcAp2VA3hhPAsgRHAPaSMnlyueYmSAzxZn//g8ycNUs6tW8v+wDCMn/efCiwl7RU85Qpr0k6gK+ULlFCgiCkYkEQ79q1SwYNGqJKY84vP0oSVMOcMeNLuRJ0FUXOyir6l7etOfv+zTczJWuWrFIYZRKosY31T5dV3ty5JQhKw2hyTxaNP9a+E6by0KFDshy0C1k4+EcmMFkJCFin8He3+swOTXeCKAr36SefSy+Ue0iWK4k61ciIxqIxzzbQm+7f83en1WPgMs195jd3eE0zVvaFNYEII0wF0fiJRiEukLJlSsnQoc8BXhh4CQ+YeyNixHVgPIW8Sr7ftWsn1kDoFV/JVzSSprz6ulSsWEEWwkjcgaKFr706SeJjTdGb4A7Xav52egeMB8IYoD/99DN20tdl6JBBShgnJKuTUk5+dPc2GD72RFnKmT/+WCBjx78oFWAkrl69BrjoW2XaG6/pLsFT89YHjodrpUzpkoC+PagGHnFG0kNpsLmvSTNWDQGA3v/74UdJind269JZ1yQ9MaQr5di0adNl2tTXJAF23teNQkHZlVgo/e6+XkPjIK713UBLTA45Gp7mV0yDAClOjRbai3itKYPMaylIOQEEQ2FtmFWr10rffv0VUzg/UL6I6EUGPXz4sLwwcnjI41ngLBMslckTJ+h3nHxaP2QQb43Epltj6NDBcg4T1qTJk7J27TrsKEoJyzf36tFDqsNlZtrCRX9Jjhw5VPsuxA7lCYC8xFcMAAa30bysF040LZHFi//C7qe+JIHWJjPR8j8KK/rjTz6VkydPSf369RTjNwa+55bwgw8+VstjyZKlkidvHnkG9EiNwmrexkSBzB3ae+994CoXjblIhEXIRoafNv1tQFhuwY6qgr7nNjGF8T0hLz/77HOAz2zSMfR41oVENnPW91B038INtFOOHDuipau7du2iu6TlqDL6Ke7hvdmy55D27Z5WcJttWPzs76WLl2Trtq1SAztBIqgR54Bzzf59+slncujIYcmVA/V+BvaXdLDYWI30E9Dh8OEjKFJXGu/pDDdHQlVQ8cD8JaC8/1ywSJ6A0nBanixIZ1tgUIBFBU1FWm89ooHE3eu3334HAKVaMhpreCt4plevvrIe/JcGrtt5qGzb+qlWintx/MRJ+Q4l0GvUqK4ofz/8+DME9x8KzNSgQQNp81RrIOgdl7ffegcW+x9qO/y3d69CtPaEIUoB+8f8BXjG9+o6b9iwoTRv/iT43lWSnbujOHFiK/DSnj3/SCcUM6xVq2aI9W7GwbVKRD/ueKkwJk96WfYdOCCdOnZRg5VeArrZiSr4GPq5detWaVC/vjRqWF9l0Zxf58oPP/xPDeoWzZurfCO9ssC1Sq/Fpo2bQ0hGZULo2m9n/yC//jYXruSE0gbFGqtWqSQ7gfXxxRcz5Ldff9MwwJo1a6R6tWrSvEUz+fDDTwBO9StQETfLgEGDUU04lfSGVyMzdnJnsAP5EgYukQrTA4GwS5dnUKMrn19xZH/m1dt8h6o0IoJ1ic9LpUCQ+n379sv69eulfr16kjVrFrWOqU0vY/KDYN1fgGBKgsJnp+FL3Q3XGAnEdgL177mPZMVKX357CtRfUSp5wsuTZAPKH+fBidYKFSooA7AP77z9jgpACvCqVSoDtzilTlTdurVlNhigPippcoIh5n0OnQL7AKx+Mm3GjBlCruV4kiVLJo0aNUBVzw/0XbVrVlcFxNgDdzFcLG2wgF5//Q2tyNm+bRuP7yJTs58fffSJCtma1avLyxMng2EyupQoVhPLQ1+8eEEXUEe4wVhDh/Thzm3d2g0yYsQwXYBJQTcK68oAn7mCuAhpwW14wYIFFRuZ9aPjxo0nTzZtqkrte9w/ftxLUDwfqyvpLSxgKrh6desC/WyWMm010I+wmsOGjZRScPP1b9kM2+jDOp//YKs9fvxLUggLvTZ2l99BWX315dfSvXtXVbakLhcX0dS4paei8yceExH8aJ8RsRSgwj+J9cky5BTsbIzxpU+XTnYDfa9IkULy85w5Wpm2erUq6mngumgK18jOXX8rb9HrkAyWLw0GVsqlC7p9u7Z47kkV2l1gfXNdUWHMgxvpzTenq7GRJHES7HB+gjJKDj6roQrmZ+x4uCPv0KE9lEEFwMWmUh5zh0ulkUUvCHE2KletpP2m9c14Gu8nDvkZyCHy+ny8sybW7dzffpdS2EnsRFXcaXBLd+zYAZgfseSDDz/EOkoN74SrkCKV113uOHz3xRdfQAbOkW7dushBKCeCT+XIkV2f37JlCzl+/Lga123aPIUKvan0fsqlWHFioY9ntTIv1yqr+fL504HBvm3bNunarausWbtWJk6arEUhWf4lMtdSlCgNziQZi9qwaNEiqHl/Ufbu36sCMQG+89QuYkdB6EZu1+jOmAVB9ROgG3v16iENYb17C1RRGKbAxBeBu+gmKmxuRkyEDJAWxEYXUHc/pxRDH7htpNA6D1xiClPGLw4ivjL7fz+C6AnUAgmt0boiIxKdzDROVvwE8aVo4cK6g+FCMjlVfA+VXuMnGko17IaWLlsh21C//yoC27Tm3N1gZGouKgr954c9L8UKF5LVsEJ24TujNPIA+rJI4SKyEsF4No6RWwu+k4F4XstYUI7s2fW39HDzcfeweGFKxI0K6U6Puxz2LWeuHBJ0NUgOgKEp+E+cPCmHjx7RMaaDEGjZqoUC3tD624HYEJXGwkWLlM7PY2dHhW2qmM6HJUjLrFqVKsrwNAQoLFjLiLzAbiYGLS4BpY0LgDsQ2x5cCly/cQ2dvx2Czsd1QP/8ZQSEabAQKW/Bn39KxUqPw+e/QhVJZngRNp10xTn//nuP4oNz3RqhxySJjBkzKX+Uwm9s5GvicTDGGQ9GDpXBdVjwq1atVqXh4n+RkqVKwMjpoP82ritPgpR4HjS+jEKhG5tr0Qj94sWKaZ9oxFatVlmV4Glgf8yZ84vuKvhuuohuoXLuRuxOSsO9arL/nLNJYKd5cJWzEvDN4CKN3KHQTd+6ZXPIi0LqVUkOb0gJvNO4pxh3pLJNmjSZlCheXKFq2XZDoc1fsACyrKgmK3DXfxQAWLt2/g06FovUOGCUKA0SlJYkt2zNUZqYnycaPwkrcz6s47bBws5V6ZKCki1VyhSKD3wELixaFzXhEvnhfz/Bst2NX+t5XV1UNNkhIBsiYM7PsJGjZebM71TZxAS8ZLXq1RDUqxly//wFC5XxmOFF/OHPYQ0wjkHF4atxUrnDYKCfQtHZyJzXg6tSGgGpcQPQgShhWbJk0cvJQHQF0ZIiA3pq/J3Wk2EWKl5CVzqbCTTyOwYNeUamVauWukX/fvZsOfPhGQjrzthau+jGHR13REZJsY9MVvgUADVr16yTarAGaenxdzI2W+bMWRTHmY0LjM9g4+JNgwQJKgz16dJHwO9hEMSDxZMcdOWMlgHOAftOtyXHSnocBwwnkyuoMCLTMvI1j/a3iKEAg6rkIwOAxOq2/HdSZBCxNW78hIwZMw6Cc54KtzZtWuv3hGAdOWq47mxng1crV64iz/bopkYGG+OjmkwDhtFMXiwGromk2HUkwjVcO9yxPJbvMeUpvpe8WwyCl82XK5u/0+BT5QYjlY1p81dgbJl+u6BgY2HdgmeDjV+uC/J3cvSBWWVcfx2xq8mDGCYTemLHxa5ZYW8hz7BW2K5ivdC9mx2GJBt3NO3aPg2DLr8qLe6+6b7SADh+dwbhmdTDdzqTkKjU+D0D9yz1zjXYr09vxQFxxjYjZnbvfkqkKQ33GIhG/IMVAn14J6B5jUBydYlBZJRRDq6pTyFEtC36/8hwtKjTwm3iK3uLT+F7zHmJY8dPyAG4wyiYaNu6ygvf0LeZ2vkmB52TUg5by19/+U0++vgDeeaZrj7pzUllQD5t+rQaqKJgJyYyrSZjaSgW8k0X01Bw8l3sg8l+IIO7mMt7484oTuw4sn3rDsmO+MPSpUtDMjGMdUQhbOhN/zJ3LoTQbA+m5Kf/wOfUyqFfly6vFCmRSABlQLQ0Nvb3NBbiihUrAEDTTuM6HwHljFtzjZOQqbEwTL91DMFBama5zULiwSYED4vCemQciXOXExVOuaAKwF1RtLALve0kfNYmP5yz9N/e/6QAFrsqLUdig8GSjgyGt8+MeApQSKXFGmPGFOOVbVq3kn179yF2cUKKBmck5sM8M6FlwoRJajwWLVJEDauL4BfuWPn5EW6ldxDHfLJZY0mWJJd2NCVcS3vgAr2OHUE88Dn5MTdkwTG4cmrAXUtjimuZhhtXkjE+QnYOPpIp2G96JWhkroGx1B2xvUMwUnlkgLueDdg5mPVqzpvxb/JroYKFZOWqVdK4yROaKnsVAp9p7yZpIAMMSnpUToAGueCCousqE3YN5O0noeTYXOs0yIWhju9TwFA+dPigygquY+NN4XqKid3MKbjqiJDIlgr/zQJDjgZXC8Rz2M5dALIhDLvIzjaMFKXBTlMTktCaGQAC0JJ99933EPhcCAvkEjIMSsmTIDgVCH1xKxGE5S7hyLFjGrStWb2qWhwvvfQK0n07wipPIoeRJtqpY3ufXJ8MVgZ3C81bttYJoR+0K3yIrmyQG7BqXoBS+FQuQEgSGawQtn60oGkBJ4BlXLN2Dfga3w4VItgwZ1MoNDI7sZEzIjOLMYXfkHZIQUrIy1s3b6uLiJYI/f7uytRXggEtd6b+latQFjGaV+QL4CkzVZZpjGz0Z37+2ReyAUGyvVikHTo9I9Wxk2rRojmsth+QmbRUXQMnwGxdESSLj3HymUxB5tgnT3lVvvr6axnQvz/cV1nhb64u777zPnzCPyPIdh5CIEOIdeXsp/PfVeALbtioERIbBuji405jzNjR8jjcfYyPDBgwCN9n1bmtUqky+tFJg/XcZTBQ+dxzA0MUKsdkeMeZSBHxYs4+MSIpQOEbH2eRunXtKmPHjkcGZCvEJi5pOW/GtPg7BWv9+nXlewSpBw0aoIBL/J7wqkzySAzhdx7xDMYCyd90c1JB0NCh0mjTph1Q+UqDXwZJOyRo0OXcqlUbTRsPAm91QPyD7qmwNO0XFFHnTh1l1OgxUFYtNbZav15dTQwxBqp6C4INXpMK2w5xyG3bt4PHW6jL9wLiioMGDtAUfq6x/FCSFSs+LuPGvagVaidPniT9+vWVF8dPkCZPtlAFET9efM0wJY0oN+rUrg1ZOElaYFz10Ienn35Kh5MLSpJHB4aPGA03e2oZNvx5jYP0hPdkyuRXkYK/Xr0KKVOklNGjRyqoVFgOY4aFZrw2UpQGA09MZZ3w8kRY7J015Wzc+DFyDIKCFi7dMvT3J4eAjwkrujsCOYR5pIKhsKBFwm1lOsQhXnhhpDIWYw+5sf3LkcP1m3sz2rVDx3ZStXoVVRhkZOY8M12XKbTTp0/TYJMBluEWMQNTcMGMDNzxuUwFXbNutQpbk3bojajcbdSpU1v+gBXPFDY+ixZ4YVjWKWHNu1xOMXQbyYN/tAo+/PA9hbrku9q3f1oZkxPuaZI5JtKE5zyKFCqs1hYFM7fLvJ+LqzUyTVqqKyoOGP4ymCqtKuk6CJDnUMa/qTnuxFBm39R/iwXcCQulCqw7Wkdp0mInhnvad2grxYsXU7dCFiQp0JVGyMx06dLLiy+N020/30vG5G/8N914gwb1x+KuISewsyMd6VpkmiXntCKgNJlNljRpEiQlYPuOMdGKoi+3MGIqjKuwT+qaxFgX/7VEpk59E+4JHCZ1HuwMK2fb66OUAuSFYsWLytQ3psjWLdvhNkmD+KUjDR1zfAlKgfG1GjWqqcLgvNNfz+SI84gPpk2bTvLlz6sxDQOMxKwlCknG2RgP5JrLgAD7qFEj8J5tcg7GH634x5Dwwj6QL0eMHKa73NBcUyQQr8mXPx+SUqYg1XYL3F7JsQaK6nv6IquT64r9yYEdf3IIY2ZlUm4wW/Kll8Zr3I5uJ2aI5UVfDd4FjSdC0D6O9GOuQZ6zKgRX1JRXJ+Ke7SrnqPCIXGj6SdrwmdztMBlFs1DRD467G2hUC2dlKAP4fsoGHpxmXPbvXbvVJU0ZQ5ddtO80KNQYUPXH56y+fAyyKnziP/74gwo3aj0yB10r/Jim2UR4NoVJOQhtZzN51fydKXpVIMhNC+1cA3173Co7m6bCQjDnhuDkx/1dvD4kZxzWeIHHHtNLdHvow3tkAmgvvDBKD96ZkhgU5gzyeRpTbjCfCcylA+6xqZHvjb5kAJ70rowAovuYyMQlIeTdG/udFcyUDR9PdOOCZbyoOBIC2MzY6d812R937uOhu5hQ8ElC+s3nmkAdF7fe54Df5IIj38RPEA/WYam75pw04vhphdXAroi7PKMw+V8GHZn6yIw2VVKcA9uihQLkSRoQd7uRPXfFuHlzZMsOPO7sehF3C+Rfxh2+R+bkN0jJbYUsoWQQgmYNkIcrIV3cNOeZChNDyAxXDz9mTZK/mUHEdFXTzDkkCtqsEMa+zmY4R2D6nRXGJT9s5l6uYVcsEkcGqLC4boIBqdgHppWnq1b1nj7o2HFtQqTVMqjt7Def6ZQNTnnGvjvlJN9hdjb0mJQIdvWZe2LgHXmxC+HHNH9PjdOQpTIMT/O50zCA7hUwqXyBP8fNOUgKEW7vXIEpZObgO2/ZTmQq94N6zoEYoeHM4/c1UG9Chv3ypzRAaP1xfzfHR81v4hX83V84Sn8ForfnMSYC6nokR2h08/RM79gpN/GeO82djs5tvFP50cJic587XpMCKZKednJcHDQUNBhoFUZ41nSE3OPKfoqNU/kl1Rfv79o3B98MH3D3SL6ikGrTppUGrZ2CzfCct/Xtbd364m9/1rm74tB7NPDomc8N/ztllXmPt77fBP+7+0R83eNLRnlaC+Z6p+sstMnnPZwTZrHqmSkfMR9vz/KpNGgxMoDFbRUn2l8/mTPYG9og/P3dn52Ov8+K6Ov8pUtEv9ef50Ul3by9y9P33rbQkcE7/tDJXnM3BcjTNBQZP2BiiTEA/KGTc745n6wf1rFDO73VmyUcXj4N730ex+E7J8Xr0MPTh/Dc460DYXkW54PGQHPEPdnCY5iFGtPQ7SXTwMKhkfxhMHuNpYClQOBSwFP9pbD21t9dflifa68PPwXux9ANVWmwW1ZhhH9y7J2WAg8yBezaf5Bnz3vf72de/VIaDyfZ7KgsBSwFLAUsBcJKAas0wkoxe72lgKWApcAjTAGrNB7hybdDj1gKMIvFAO5oZQIbB7wvAht68iHmRPZ9PdDeHCEUsEojQshoH/LIUwAKg5koJ1D2mzVlmIbNysSmlpqTPpr7D6Vi6nSZv01GmYFYdt5jYEONIDU1mPWQXHAJFvf7zD1UZM5+mHRm5/XuffA2n3w/S9G4N/MuZua4943ntZjPajJ1DDwxFYGOJ7hemXkmx0SFy6oS51D+OxbPeyEV25SgeeR5LZoJEKrSCEsOcDSPxb7eUiDaKBAHgm87ysUQQ6I4DmH1QUl/nsQ/BWRHCkBC/vJczXWcwOeBRp7yPctKzhCM/JsHGXn62HWY9opWYSZKG2uZUSnwd5a8oMBmTTCWraEQzYQy+byfgphr9QhO35+E4uKhOZ7OZ8E8Hs4lDo1mQun5izh6ToaH9vgb+8D+JUMlVT7fW8YUlQH7dxLVjzkWagfivZhT00zR5XtY0obPZ4FK9unSRRwOxv8MlgpPQ/PjoslN7QMLYLJSB5UXSwHxEN0OlNefPGmKjmn69KmuU9+P6NmdQJLDfikNFsu6EVzoL9pWpX2xpUAAU4CLmsXpeFiK9bQoMDdu3CSTgBZ5+vQpFJs7rYdeqSx4SrgKSkA899zzwJRBmRuUlcmXL7+eiciIkjd/rV6iSG0EHqIyYKmVwYD3LY7T8quBFfP6a2/I5SAIYtQ1awwwLJZ/YW3hr776BmiI30KJxFFFMmBAf5TIeEyGDx+pAFsZ0CfWVqLwZdkMouJNQf0xYjfwAF+JYsVRt6yZFgb1lJJJhXIYyJnPDRmq5cFZHO8EgMZYiP/rr2Zo2Re+S0vr49puKABIIKHBg4cARGgT6k7NVCW3ELDLBB0aM2a0Yuuw3FBaVEdgo4IdgbIhrAKRF6XR+/btg/c9r+8LHT8wgBnkPrtminjeT6rsfXYh5HafSoMT/y+0/VEwCqs+crsd2XVNImpg9jmWAlFNASoOnmbPHFz6vghO3X75BRAcUdG5T5/+AAMrJ31791JFMBOF+6hYvpzxhe5GevbsrZWJCUZUuXIlKYeyLP36DdT6YsOGD1UlQCCyMWNf1MJ/hAQ9jcqupyC0uSaXr1wp773/gbw84UWgJJZSVExWL2ZJbhaH7NGju3RH0UpWF2b5fP5GJZcXCuS1117VQph90UeClQ1GQUFP2JhaDw4K5cMP3gupFfbcc8N098EaSCNHvqB9+W7m18B6WIjCoJ+gDlsh9PMMQIv+BgbFr/Js9y7YVVzG+w6HVFp+DGV7vv3qC61tQKXB513j4ULsRAjUlgKuKV+FPaN6nqPyfaZYIosSkjZaENFD7b2o7JNXpWFwnwm2swiWAQvZkdFu4XMHlfZOV01VSk/WwHWA9MRBbSb3pqWM8fH0PNZX8QRmEp57+F4DDuTeB4MB4anf3u7h91pgz8NMcVHCUXDPLzc5Uvyf2CCe+uBprFxEeoLTzefL+01pd080JW6HEzvYXOPtHv7ujd68hz32OK9gXhYfDOu8Ki68202hzaune3zNq7d+a2kb0Ce2A1vZSR9vY/VFH+epWletJReoFj+kT6JEiXWXQfcMXTJs5B8W7mRxR35YGJKlNtg/vQ8fwg7znoTBNYKWLl2mz27evKkqkfSwzvlh336fO08K5M+PApGuOk4sx83GnQDdRyytT0F+Bah4LChaAVWOaRSyxlcGKAIiwiVHPy4CNMn0z1MgnwqPtdDIx4Rp3Yvd0DtvT0dV5LMKzEWgLtZUq44q1UTCJKIc3VZPPdVS/ly4UFpi58H+aDl/FNnjf8+cOiO/o+inK3lAUP+srNak0/pmGK97qQt3THozf+Hi4QiUTb7WJdc/8UDdm6817pQ/xB4idERZGASsGBzWUin3vPg+vrhHaWgdFVgixIX+799/FV/7ABDt/oeCY9T63Epz50HIUFZWVIEDyyA/KkWScQiwEhtgR0pAMEFy+DZZ/54VJAkyos/nosEnParCcsu7b78LKU6FAO4h5CFhEPfvO4Bt6ekQVC1uzVgZkozLMum0mFw4FRDGCJYRDewAnkVsbmOZ8B4ClXBRbEcpY9M3Lghup1mW+Diqsx5EDX2DhczfkoBp06GaJrEyiM5l+q31/LFtph94738u94HpNxmd97Bs8xX4pc1vZHyWY+HfxCzWqq7sN+nA2vyFCshulIh3+XWDscnxYxa4LkiLzZu23EEWo08axRcJ5MTKoLQWDXYAx5oR5cxTpUqNqrt7YGVyjoDxEfwe+th5zylYvk76sExzLhRS3A7ccALQOPtNHPOrgKk9sH9/CH04r0Tdy5cvr2KNs8qnuYfvIkpiAlQZJaZCyHt4D4RNLpRlp1XLasPOfpMXKFxY4dMgm5mqDkXhlmGFZPKW83mkDS1cVg0NCSoH10wiZCehQ2llO9/DSsTkBdKHQjqEPug3K47yHuWf4EVF3z3jA0SRo1AkoqOTPqQpK7F6akboOrEYzHVUIiyt3X/gYDmNHQShUTsBTpe8Ye7jf0lrIzxIN4LsUAntB7/Onv2jFqZkJdWz584qngXbYpTEX79+g1qlBCdif9l3rsVzWKOcUyoNCuz1KE/fo3df5Yt8efIqxCrf5yvziwqDML8fAuK0M/qcDXy6C3+z71QobKpcoPAYf6GFUxjokicw598AEC1PHhbYQ7lxTDCvO3HyhCz8c5GClJFZCwOjhTT31ChrtmO+iafhnFdW1SUM8j///KOQ0U5+yIpCqVexgyIvGNljCoGysiyrxBLYycyr7pxwDyvmcl0agDTKxXgowklIW3pgOG9O3A5CRzMB4j/IBdLPYOjQUCCfstozY1XO97AyLedlx45dIXyv8gRruRB2af/953rPHiAG8pm/AbOcaKb0/ERXYoBHpYHQmwrfZctXwpKAewoYF6tXr1G0KgpQwnaeAaCOIRgDdyzJ+y8gCNfA52qqJ5L4FBIUIsTROA+CmZREVk4lvgTr0vz558IQVDgG51j+m75fQjiyxLqxzii8KqHaaywopVUAQGE9HCouU98mG5QalRxxLEzfKNy5Raar4OeffoHF56rsaKqzsmw6IVdZkpvllM1vLKleomRx9HuRYnArJgjLegeXSuYi/QO4GYahqBjInMUBtbhs6XIVzEYJMThIq5EKcu7c30NwRlRx4bv0AHKin9fQlN/zUxmly1kKfs7Pv6hr0PSN1mcluDD2Q6FtAZztHfpcVdhaooEtBE3PIvOEmSvmPTWAfrgPliHxjQ3jkj50URCwfvGixRrkdPa7adMmuqD+xKI29OG80m2SCZVH/1q8RIW5ovEFp5gWhpBnuer58xaEzAPpQ3cMGZ5b7U3w95sxsQ+lS5fW4CmhQPm3CvPg52WBobB582adW+LNc7NGA4SGRTzEBn6B24N+fDbOUQKUayes6KHDh13vCbbu+dyyQBAsDONi4cJFSm+z8KiMatasqcYDjQtDH/Kj8nC6NLJ48V9qYDjpUxN4D8ROCGsjvgzR4QqiNPhS8AsFJ+lDo4RC21PjujIVZxnMpiBZBOudJbkTQ5FcvRakt3EsxOCmsTcE+BMU0HWBz9ARsQ+uIQbYDa0IYlQwfwEF4CIed3qUwdc+eBkQx34FdHxz+lsw0ooI8WTYiHAXA1rA+Nw5BI6Rwfubt7EjxX+Jg/3yxIl6DefLFDSlQh48eIC6XTjnFLz8t8csLfDe+g0bZMP6jXfxD6sjU8gTQ+bI4SMh65LvIsYMQZoYYzL0YeVdrrtMmTOC55bLfsg7M6+UZ9wpMfmAcR+jCLU4KXiUWBarV63BOtoZwlvkO64jYl/MAxytyQwzBU3pcly3bh3469Bd76lRo7oqUd5DI8YptwhZQFlGRXgI95E2BH6iwUggK8q+m4hrRXW7R2lw8GQ6Wi8VypeVH4GFu3zpCumH2vJpNFgFvAgsOmdsg4PhNpcauHadWg7AEmRXwCpOlDCR9OzdUzNBnDs0Lma+qywQ85zZAVwQFFCtsaWlsHH+xoXj6l/5u2jF76honmzWFEAuDUPew75xK8/PyFHD7vhGQWtuj9lvWrkElg/xm+rOJbYyIev9393vGGAiVIfEWEqUKKECjI2LhEzHe4hmRws15DdYKFSqfD4xJdwb+03AKcJaOunDsXIrPnzk8/f0m7+VBsB9I+CNu9OHC7QnwF24a3N6w2jdlgJ2shPgiPTh9VRE3eBvdu83yzvTwiI/3HkPx+pybXSBb50L3Pke0prMXxL0MeNR+uA7Po8wuQ0a1rur3+QFKoQyKJNvkBcNnYhtwh0cF5jzeS4XTnz1+Tvfz36SPjQqGgDy10mfeAC+ocDq0bP7XWPlu0ifGzdKAlSqfsgUOenTBYFdlzAInnPQhTsqZvSwL2Fp18DXpEMHgHO1afMUaN8DYETvy3MEKArevXJeyE/Gpcnd/Ecff6wuocIFC0obYKlMQaCdjbuHn36eo3xZGWXnNwOYiztHI8RJP8ZbnI3Kn4YOdzgtWjZD0Lm/fPTRx9IHa1V3vG7KS1EcwaM/AJqVhtCLL44PEXIMZMcFZvd+KCo2llQnch8t9htAm+TukUF5YqjM+OJL9RiQjLprxdqg0eJs3twv5JMmqJRbD3Ed57yS5zjnzzzTyYVfYbaK4DsabLfghqrlgHk2/JgYEAHtgFfjgkw2axnzCsOD8oEGJ/9rGo0J8n2r1pRN1+6sS1zA+aLsIp7OHQbCbgoKlX0rhO+dfeO6Iv8QmbN//74uejv6QLlAg4Xy55WJU9Q9NXBAP4W+pazRDLZoaF45nUKEzJsNWu30Y6c0u4G4vfQbkmju3jmtH6+whHf/YqwJWp7u9xhfJVPsnM3cw0Xs7z28n8JLhY9bHxQAFpSme8S98R5OKPF+3ftAhiYYkHszwpjCzNM93ujDa731wdtY2Yf0oKvHPoCZ3aMDxr3BufA0R1wA7vcYertgce9urrHGUDeZpzlKBXeJ5zm6rUBWnu4hve+Zo2ABlQwuPk9zRLxxT7ylbgZv9EG/Pb2HbhCv9PFwzx36eB7rHevaVU6cCIm8xxhWFAZ0S9CSNI27Hlq/QVeDJCl4jDC7o0e9II0AtZsA41kCQbt6zWrsJlICVzq7PF6hAtAuS2MnWR6K4nVpDuNoExQD55Nz06BBffntt99lNJDziG1CtxNdcXTbXrx0QX6eMwdWf2w5e/4cAsvJgQxXR40H7iKpAAh41qx5M5n2xpua2VUEwt1dcFPp/w336ksTJkrZsqXlD1jH5y+cV8TLGtWqScOGDWUmMDOIYElURsoM7nzfwDMv4h105BKR7tXXpoa4gyisN2AH8BkUCeeF65QIfdw9ka/o8nW6YVy8mMzzvHKX6UNmeJNNhE71xMOcN288nDIljFc3RjVznhSKyF+5oMBo6HfahGk8rnETy+NujEqDstJgh9xzQxR94VVpmOq2tKapbePC4jFM5CuDytu21lcJ3oi8xzsmxJ1FfI8AZtl3LwT3PVbPmt7XPd5+80Ufr7npeH2Y++3jnoifI8/08TVH3sYTnnnllHrlrVvewZ3Cw498F63QrYDgnfHl10JXIKE5qXS5W2iC1FiiJ/JvtgJwIbYGlrZxexLOsy1Sbi8A55kClm66YkWLqSG0Cq5h7niJgDly+DD58KNPNJ6RIGF8IDB2UOFM/OwJQFfkb7Nm/SDp4Hp5CjsRWquNGzdWlzKDzbSOM2bICPdmRew6S8ElmEL7R+FVHYL/8MHDGq/0xKf8jmm2hPjlv6k0mIqfBFDMhbDzefrp1nINypDu1LTY2YwcMQwB/hRQRk3hwsuv9xB985WXX1I3IAHaisM3X6liRbiVljDKof1g7JT0OQz34rczZ6kbyLgLdU59rNeI5uHwrFdv9/iSC97WuLnnySebKN+EBkIXFXrD556ampbbp4QxEvqNpREVnbbvsBQINApQGTB1tMezXfWw2g3ActIqptCn9dsFLiA2Y3iVQTyPH6axUhBw99UHmM+mlYIwdTaqGgqWJMmSyOCB/e76jc/kJx+s8ylAPXRmyfH5nTu214+z8Z2VHk+jqHnaB3wYU+rfr/dd/XS/hwkGb7/5xj3kp5XNZ3Tv1kU/prFfvQD7a8ZOt2anYGwN9oE7HHfkTlUM+C0IcSseTuwKtyATDozCDbS5j4r+UPmTnwL+nAaJ4Us7RgWx7DssBR4EClCg0R00ZDADzy6L0OTT0/hyd/W4/8115gvB0tDAoMGZRAFn3MGFmsdAONDyHBlQ3uID7u/zZcGHvN+HlW8Ug3vfnO93p4WvMWdFZlE/nKw39HyUlUYgyeGwRe8ehNVr+2gpEE0UoNC9FRxP85WyGhHd8yZEGBqi7zs6W0QJOGZfXY8iekYnvR60d1ul8aDNmO1vQFMgspVFQA8+Ejpn6RkJRL3PR1qlcZ8EtLdbClgKWAo8ShSwSuNRmm07VksBSwFLgfukgFUa90lAe7ulgKWApcCjRAGrNB6l2bZjtRSwFLAUuE8KWKVxnwS0t1sKWApYCjxKFLBK41GabTtWSwFLAUuB+6SAVRr3SUB7u6WApYClwKNEAas0HqXZtmO1FLAUsBS4TwpYpXGfBLS3WwpYClgKPEoU+D9ZpZRjXRFwMgAAAABJRU5ErkJggg==\" alt=\"image\" width=\"265\" height=\"142\" /\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUser Path:\u003c/strong\u003e Linear path down a busy street. Picking up the takeaway triggers a brief, 2-second visual/sound flash of a Quanzhou night market.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScene 3: The Decision (Transition)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"image\" width=\"263\" height=\"153\" /\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUser Path:\u003c/strong\u003e A static, narrative moment. The user reads the letter, triggering the audio and an abrupt scene change.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 290px;\" valign=\"top\"\u003e\n\u003cp\u003e\u003cstrong\u003eScene 2: The Flat \u0026amp; The Memory (London)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"image\" width=\"257\" height=\"296\" /\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUser Path:\u003c/strong\u003e Free movement in a small room. Interaction with the bowl begins a seamless transition, blending the London flat into Grandma's kitchen.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 312px;\" valign=\"top\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScene 5: The Ancestral Temple (Quanzhou)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"image\" width=\"272\" height=\"145\" /\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUser Path:\u003c/strong\u003e A slow, prescribed walk to the altar. The user performs two embodied interactions: a bowing gesture and placing an offering.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 290px;\" valign=\"top\"\u003e\n\u003cp\u003e\u003cstrong\u003eScene 4: The Return (Quanzhou)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"image\" width=\"265\" height=\"151\" /\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUser Path:\u003c/strong\u003e A short, room-scale walk from the arrival point into the vibrant market. The environment is designed for immediate sensory overload.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 601px;\" colspan=\"2\" valign=\"top\"\u003e\n\u003cp\u003e\u003cstrong\u003eComic-Style Storyboard\u003c/strong\u003e\u003cstrong\u003e：\u0026nbsp;\u003c/strong\u003eThis storyboard captures the key emotional beats and visual composition of each scene.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel 1 (Scene 1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eVisual:\u003c/strong\u003e A low-angle, slightly distorted view looking up at a towering red London bus. The user's hand is visible, holding a greasy takeaway bag. The colors are cool and muted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eCaption:\u003c/strong\u003e \"The Hustle. Twenty years of routine...\"\u003c/p\u003e\n\u003cstrong\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull; \u0026nbsp;\u003c/span\u003e\u003c/strong\u003eSound Effect:\u003c/strong\u003e \"ROAR... HONK...\u0026nbsp;\u003cem\u003eclatter of rain\u003c/em\u003e\"\u003cbr /\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003e\u003c/strong\u003ePanel 2 (Scene 1 - Memory Flash)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull; \u0026nbsp;\u003c/span\u003e\u003c/strong\u003eVisual:\u003c/strong\u003e A transparent, ghostly overlay of a sizzling Quanzhou oyster omelette pan superimposes itself over the London street for just a moment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003e\u003c/strong\u003eCaption:\u003c/strong\u003e \"...punctuated by flashes of a different life.\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003e\u003c/strong\u003eSound Effect:\u003c/strong\u003e \"SIZZLE...\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel 3 (Scene 2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003e\u003c/strong\u003eVisual:\u003c/strong\u003e Inside a modest London flat. The user's hands are on a bowl of ingredients. A faded photo of Grandma is on the wall. The light is warm but lonely.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003e\u003c/strong\u003eCaption:\u003c/strong\u003e \"A quiet moment to prepare... to remember.\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel 4 (Scene 2 - Transition)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003e\u003c/strong\u003eVisual:\u003c/strong\u003e The kitchen walls around the user's hands dissolve, morphing into the earthy, warm tones of a traditional kitchen. The London bowl becomes a worn wooden one.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eCaption:\u003c/strong\u003e \"\u0026hellip;and the memory consumes you.\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eVisual Effect:\u003c/strong\u003e \u003cstrong\u003eMORPH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel 5 (Scene 2 - Memory)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eVisual:\u003c/strong\u003e A stylized, kindly Grandma (AI agent) gestures with a smile, guiding the user's hands as they stir a pot in her kitchen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eDialogue (Grandma):\u003c/strong\u003e \"A little more soy sauce, my child.\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eCaption:\u003c/strong\u003e \"The taste of home.\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel 6 (Scene 3)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eVisual:\u003c/strong\u003e A close-up on a handwritten letter, resting on the London table. A single raindrop hits the paper, blurring the ink. The background is dark and out of focus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eCaption:\u003c/strong\u003e \"The call to return.\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eSound Effect:\u003c/strong\u003e \"\u003cem\u003ecrack of thunder\u003c/em\u003e\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel 7 (Scene 4)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eVisual:\u003c/strong\u003e A wide, vibrant shot. The user steps from a shaded area into a sun-drenched Quanzhou market street. Stall awnings are red, steam rises from food, the crowd is a blur of life.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eCaption:\u003c/strong\u003e \"The Return. Overwhelming. Familiar.\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eScent Icon:\u003c/strong\u003e 🍜\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eSound Effect:\u003c/strong\u003e \"CHATTER... SIZZLE... \u003cem\u003eunfamiliar dialect\u003c/em\u003e\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel 8 (Scene 5)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eVisual:\u003c/strong\u003e A low-angle, reverent shot from behind the user as they bow deeply before an ancestral altar, smoke from incense curling around them.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eCaption:\u003c/strong\u003e \"The final ritual. A quiet reconciliation.\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003eSound Effect:\u003c/strong\u003e \"\u003cem\u003esilence\u003c/em\u003e\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePanel 9 (Scene 5 - Finale)\u003c/strong\u003e\u003c/p\u003e\n\u003cstrong\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003e\u003c/strong\u003eVisual:\u003c/strong\u003e A first-person view of the user's hands gently placing a piece of fruit on the altar next to an old portrait. The scene slowly begins to fade to white.\u003cbr /\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003e\u003c/strong\u003eCaption:\u003c/strong\u003e \"Home is not a place, but a feeling you carry. And then, you return it.\"\u003cbr /\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cspan style=\"text-align: start; color: #000000; background-color: #ffffff; font-size: 11px; font-family: Verdana, Arial, Helvetica, sans-serif;\"\u003e\u0026bull;\u0026nbsp;\u003c/span\u003e\u003c/strong\u003eNarrative V.O.:\u003c/strong\u003e \"And I was home.\"\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"5. Discussions","content":"\u003cp\u003eHere, we provide a summary of findings and recommendations, structured by fundamental differences, experience levels and usage, model pairings and workflows, and limitations.\u003c/p\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Fundamental Differences\u003c/h2\u003e \u003cp\u003eThe three models differ fundamentally in their core approach.\u003c/p\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e5.1.1 ChatGPT is a \u0026ldquo;Preset-to-Prototype\u0026rdquo; model\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eChatGPT employs a prescriptive methodology, generating production-ready specifications organised within a DL \u0026rarr; MOD \u0026rarr; EFF \u0026rarr; IF scaffold.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe typical output consists of concrete defaults, including parameters for locomotion, audio mixes, environmental palettes, runtime maps, and frameworks for safety, privacy, and cognitive load.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThis model is most effective for applications requiring rapid implementation, such as fast prototyping, project scoping, and the deployment of accessibility and safety policies.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e5.1.2 Gemini is a \u0026ldquo;Facilitator\u0026rdquo; Model\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eGemini utilises a facilitative style, employing clear framing and focused inquiries to surface project intent, constraints, and the core emotional thesis.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe model characteristically produces structured prompts concerning user embodiment, sensory inputs, environmental ambience, temporal transitions, and the intended emotional arc.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIt is optimally suited for initial project phases, such as kickoffs and workshops, where it facilitates stakeholder alignment and defines the experiential \"feel\" prior to finalising technical specifications.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e5.1.3 DeepSeek provides \u0026ldquo;Classification and Blended Approach\u0026rdquo;\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDeepSeek is distinguished by its use of formal taxonomies and reasoned hybrid plans that connect specific design choices to narrative value, such as identity and memory.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIts output typically delineates principled trade-offs, such as the use of natural versus artificial locomotion, diegetic sound versus musical scores, and linear versus non-linear narrative structures.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe model is most advantageous during design review and polish phases, where it assists in making defensible trade-offs and documenting the rationale for narrative-driven choices.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Application of Models Across Designer Expertise Levels\u003c/h2\u003e \u003cp\u003eIn what ways will each level of designers be helped with co-ideation, co-creation, and design using the models?\u003c/p\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e5.2.1 Beginners\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFor building and co-creation, ChatGPT is the primary tool, offering plug-and-play defaults that facilitate the creation of a functional prototype from the outset.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor co-ideation, Gemini serves a secondary role by posing questions that clarify goals and constraints in a manageable way.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDeepSeek should be used sparingly, as its complexity can be burdensome for novice users, though it is useful for demonstrating hybrid exemplars.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e \u003ch2\u003e5.2.2 Intermediate Designers\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDeepSeek is the primary model for co-ideation and co-creation, as its taxonomies help transform intuitive design instincts into principled decisions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eChatGPT functions as a secondary tool for implementation and iteration, providing concrete parameters that enable rapid A/B testing.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGemini remains valuable for aligning cross-functional teams, such as curators and educators, during initial project phases.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e \u003ch2\u003e5.2.3 Professional Teams\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFor strategy and critique, DeepSeek is the principal tool, providing a defensible rationale suitable for design documentation and formal reviews.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eChatGPT serves a secondary function in operationalisation, finalising details such as runtime, safety protocols, and localisation requirements.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGemini is employed for stakeholder management, ensuring alignment with the project's emotional and pedagogical goals prior to development.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Guidance in the use of LLMs\u003c/h2\u003e \u003cp\u003eHere is a summary of key guidance in the use of each model across the dimensions:\u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003e5.3.1 Spatial, Sensory, Placeness, \u0026amp; Temporal Dimensions\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eChatGPT provides actionable defaults, including parameters for movement, comfort, sensory inputs, named micro-places, and temporal beat maps.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGemini facilitates guided choices to clarify the experiential \"feel,\" such as balancing immersion with comfort or choosing between diegetic and non-diegetic sound.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDeepSeek constructs principled hybrids, such as combining room-scale interaction with teleportation for scale or integrating a linear narrative with non-linear memory elements.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section3\"\u003e \u003ch2\u003e5.3.2 Cultural, Social, Cognitive, \u0026amp; Psychological Dimensions\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eChatGPT focuses on ethical and operational components, including consent, privacy, safety protocols, and cognitive load management through tension budgets.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGemini poses critical questions concerning cultural specificity, social norms, avatar identity, and user motivation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDeepSeek formulates value-aligned positions, advocating for high cultural specificity, embedded social etiquette, and varied cognitive load arcs to foster intrinsic motivation.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Potential limitations for each model\u003c/h2\u003e \u003cp\u003eChatGPT\u0026rsquo;s potential weakness is its tendency to over-prescribe, as its defaults may inadvertently bias creative exploration if not critically examined. The model provides lighter formal justification for its outputs, which may be insufficient for rigorous academic reviews.\u003c/p\u003e \u003cp\u003eGemini is non-committal by design, providing few quantitative metrics or policies, which requires a decisive project lead to translate its output into technical specifications. This can introduce a risk of decision drift and slower execution if teams remain in the ideation phase for an excessive duration.\u003c/p\u003e \u003cp\u003eDeepSeek\u0026rsquo;s reliance on a taxonomy-heavy framework presents an onboarding cost for junior designers. Its recommendations can appear abstract if not paired with concrete implementation presets, a domain where ChatGPT excels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Best Use Case\u003c/h2\u003e \u003cp\u003eBased on the analysis above, a recommended workflow and model pairings is provided for project goals that differ, matched with designer experience:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eProjects requiring rapid deployment should first use ChatGPT, cross-analyse with DeepSeek, and use Gemini to align the ideation process and design plans with stakeholders.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor projects requiring a flagship experience, the recommended pipeline is to use Gemini for co-ideation, DeepSeek to architect and justify the design, and ChatGPT to operationalise, test, and document the ideation and co-creation process.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhich models are suited to designers with different experiences? We think that beginners should use ChatGPT. Intermediate designers should use DeepSeek, supplemented with ChatGPT. Professional teams should use DeepSeek and ChatGPT, with Gemini for external alignment.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Discussions","content":"\u003cp\u003eWhen guided by the same Dimensionality Framework, the three LLMs exhibited distinct and stable behavioural patterns (RQ1). ChatGPT behaved as a production-oriented \u0026ldquo;preset-to-prototype\u0026rdquo; model, generating concrete design decisions, implementation-ready specifications, and complete workflows across the dimensions. Gemini acted as a facilitative \u0026ldquo;alignment engine,\u0026rdquo; using the framework to surface intent, pose clarifying questions, and shape emotional or narrative direction rather than commit to specific solutions. DeepSeek seems to adopt an analytical mode, applying the framework as a reasoning structure that justified each design choice through taxonomies, hybrid strategies, and theoretically grounded explanations. Thus, even under identical scaffolding, each model enacted the framework through a different creative stance, practical (ChatGPT), facilitative (Gemini), and analytical (DeepSeek).\u003c/p\u003e \u003cp\u003eApparent qualitative differences emerged across the models in theoretical grounding, cultural precision, and implementability (RQ2). DeepSeek demonstrated the strongest theoretical grounding, explicitly referencing framework dials and offering principled hybrid models. ChatGPT demonstrated moderate theoretical grounding but excelled in concrete implementability, producing timelines, interaction rules, and production-ready design documents. Finally, Gemini is conceptually aligned but less analytical, favouring narrative and emotional clarity over technical specificity. In cultural precision, DeepSeek generated the richest details for Chinese and Minnan contexts, whereas ChatGPT offered a broader and more globally distributed cultural accuracy; Gemini was correct but less granular. Overall, ChatGPT was the most implementable, DeepSeek the most theoretically rigorous and culturally specific, and Gemini the strongest for early conceptual framing.\u003c/p\u003e \u003cp\u003eHuman-AI co-ideation and co-creation can be helpful for different levels of VR designers, all in terms of the speed of the process, as a catalyst for creative thinking, the filling in of details based on culturally contextual knowledge embedded in LLM models, and a tailored approach that carries the VR experience designers' style.\u003c/p\u003e \u003cp\u003eThe study presented in this article demonstrates the effective integration of frameworks and LLMs into the VR design experience process. The human-AI co-ideation and co-creation system is based on structured, theoretically-grounded frameworks that guide collaboration. The Dimensionality Framework, integrated within LLMs via a customised system prompt, provides the necessary conceptual grounding, moving human-AI co-ideation beyond a simple toolset for performance enhancements toward a multidimensional synthesis of experiential dynamics within the VR process.\u003c/p\u003e \u003cp\u003eThe study evaluated three models - ChatGPT (GPT-5), Gemini (2.5), and DeepSeek (DeepThinking) revealed distinct and complementary operational styles, even when guided by a system prompt created for the purpose. It was found that ChatGPT excels at rapid operationalisation with its \u003cb\u003e\u0026ldquo;\u003c/b\u003ePreset-to-Prototype\u003cb\u003e\u0026rdquo;\u003c/b\u003e approach. Gemini's strength, on the other hand, lies in its \u0026ldquo;Facilitator\u0026rdquo; model for initial stakeholder alignment; and DeepSeek provides a \u0026ldquo;Classification and Blended Approach\u0026rdquo; for architecting designs that are grounded and principled by the framework.\u003c/p\u003e \u003cp\u003eThese findings lead to a recommended workflow where models can be used strategically, sequenced as such: using Gemini for co-ideation, DeepSeek for justification, and ChatGPT for concretising and deployment. The workflow allows the leveraging of the unique strengths of each model across the VR experience design lifecycle.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis article advocates for AI transformation in the VR experience design process, where humans and AI collaborate in the design thinking process. In the transformation, theoretical frameworks and concepts provide the boundary and groundings, while AI augments creativity within structured processes. In the human-AI process, while humans contribute to essential decisions through personal choices, emotional depth, contextual sensitivity, and experience, AI acts as an enhancement by handling the efficiency and feasibility of theoretically grounded work. The human-AI partnership serves as a vital bridge, establishing a shared vocabulary that links abstract theory with concrete design practice in this evolving landscape.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe principal author (Eugene Ch'ng), also the sole author, conducted the work below:1. The design, research, implementation and testing of the AI system prompt, which works with ChatGPT, Gemini, and DeepSeek needed for the study.2. The running of the System Prompt in ChatGPT, Gemini, and DeepSeek, from the initiation of the LLM chat, to the final synthesis.3. The analysis of the differences between the outputs of each LLM and the interpretation of the data in relation to the cultural differences, methodology, and synthesis between each LLM, and how they can be useful to different groups of users.4. The writing up of the entire article, and the polishing and submission of it.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work is supported in part by Guangdong Higher Education Upgrading Plan (2021-2025) with project No. of UICR0400010- 25, Guangdong and Hong Kong Universities \u0026ldquo;1+1+1\u0026rdquo; Joint Research Collaboration Scheme with project (2025A0505000008, UICR0800009-24 and UICR0800009-24A).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data generated by all three LLMs (ChatGPT, Gemini, DeepSeek) using a System Prompt that I programmed that integrates with published article on a comprehensive VR theory: Dimensionality Framework (8Df). The data are available in the Appendix. The Chatbots themselves that generated the data are available:ChatGPT:https://chatgpt.com/g/g-68e37b8b9fc48191831d1b7259437899-vr-dimensionality-framework-8dfGemini:https://gemini.google.com/gem/1AlAkOAy8J22ervxGA4v999riudajEZdP?usp=sharingThe DeepSeek chatbot prompt can be obtained from my GitHub page. It can be cut and pasted directly into the chat window:GitHub (System Prompt): https://github.com/drecuk/VR8DfDeepSeek: https://www.deepseek.com\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlef Y, Shafriri Y (2025) AI Assistant for Context-Based Significant Assessment. 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Large Language Models Are Improving Exponentially In a few years, AI could handle complex tasks with ease. IEEE Spectr. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://spectrum.ieee.org/large-language-model-performance\u003c/span\u003e\u003cspan address=\"https://spectrum.ieee.org/large-language-model-performance\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"presence, VR, framework, human-AI co-ideation, AI co-creation","lastPublishedDoi":"10.21203/rs.3.rs-8274845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8274845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLarge Language Models (LLMs) are transforming creative fields; however, designers often lack structured frameworks that allow for creativity and theoretical grounding, particularly for complex media such as Virtual Reality (VR). This paper integrates the VR Dimensionality Framework (8Df) into a human-AI co-ideation system that builds on the theoretical foundations of VR, by structuring experience design across eight interrelated dimensions: spatial, sensory, placeness, temporal, cultural, social, cognitive, and psychological. Through a dialogic system implemented across three leading LLMs (ChatGPT, Gemini, and DeepSeek), this study evaluates their distinct approaches to co-creating a VR narrative. The findings reported here revealed fundamental differences in the styles of co-creation: ChatGPT operates as a \"Preset-to-Prototype\" model, providing actionable specifications for rapid implementation; Gemini functions as a \"Facilitator\", a catalyst for stakeholder alignment through guided inquiry; and DeepSeek offers a \"Classification and Blended Approach\" for creating grounded, principled designs. The evaluation also highlights variations in cultural data representation and suggests a practical workflow that matches each model's strengths to specific design phases and expertise levels.\u003c/p\u003e","manuscriptTitle":"Human-AI Co-Ideation in VR Experience Design: Evaluating the Dimensionality Framework (8Df) with LLMs (ChatGPT, Gemini, DeepSeek)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 17:16:37","doi":"10.21203/rs.3.rs-8274845/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-17T13:23:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T13:17:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142695418377546283739502018666232015669","date":"2026-03-24T15:20:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-28T17:33:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2211091891756335522113274010434387040","date":"2025-12-19T12:26:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76656384548858617717961899039943944565","date":"2025-12-18T11:19:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308810714989871246054504963311772450949","date":"2025-12-17T11:42:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-17T09:46:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-16T10:42:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-16T10:37:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-14T04:30:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-12-14T04:24:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"223e5ce0-8372-4d33-b229-12657257d748","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59906880,"name":"Humanities/Cultural and media studies"},{"id":59906881,"name":"Social science/Cultural and media studies"},{"id":59906882,"name":"Biological sciences/Psychology"},{"id":59906883,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-04T14:53:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 17:16:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8274845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8274845","identity":"rs-8274845","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}