AI storytelling & narrative evolution in creative industries: a Systematic Review.

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Iván Sánchez-López, Arnau Gifreu-Castells, Antoni Roig This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7318246/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Artificial Intelligence is reshaping narrative forms and content across the creative industries, transforming how stories are conceived, produced, distributed, and experienced. This study examines the evolution of storytelling under the influence of AI through a systematic review of high-impact scientific literature. The methodology follows the PRISMA 2020 protocol, combining Discourse Analysis and Grounded Theory for synthesizing results. The sample comprises the most cited articles indexed in Web of Science and Scopus between 2020 and 2025. Findings reveal a predominantly technophilic perspective in current research. The analysis identifies a comprehensive narrative transformation articulated through four key shifts. First, Hybrid Authorship: New collaborative dynamics arise between human creators and generative systems via prompting and automation. Traditional unidirectional storytelling evolves into dialogic human–machine co-creation. Second, Procedural Narratives: AI systems produce adaptive, real-time narratives that adjust according to user interaction and contextual inputs, enabling pervasive storytelling across digital and physical environments. Third: Distributed Authorship and Algorithmic Intentionality: Content generation grounded in large-scale pattern replication challenges conventional notions of originality and ownership. Authorship becomes distributed across datasets, training algorithms, and human prompts, introducing a model of split creative responsibility. Fourth, AI-Augmented Audiences: Users gain technical capacities for narrative self-management, dynamically adopting multiple roles within both the diegetic and creative spheres. These interactions foster emotional and empathetic bonds with AI systems, raising indicators of user dependency. In conclusion, according to the most impactful literature, synthetic narrative participation is emerging as a multidimensional agent of unprecedented relevance in the creative industries. By operating across diegetic, performative, and emotional dimensions—and acquiring embodied forms through robots and interfaces—AI positions itself not merely as a tool, but as a narrative and social agent. This phenomenon marks a fundamental ontological shift: AI intervenes and participates —while remaining an object— in the construction of human fictions, realities and imaginaries. Humanities/Complex networks Social science/Complex networks Physical sciences/Mathematics and computing Social science/Science technology and society Figures Figure 1 1. Introduction 1.1. AI and the Reconfiguration of Communicative Dynamics The emergence of artificial intelligence in the digital and media sphere has generated a structural transformation of communicative dynamics, evolving into a central element that reconfigures the system as a whole (Sonni et al., 2024 ; Harahap et al. 2025 ). Specifically, its capacity to analyze large volumes of data, detect patterns, synthesize information, and generate automatic content has decentralized human communicative agency, redefining traditionally consolidated principles such as authorship and enunciation processes (Fang et al., 2023 ). In this new framework, AI systems not only operate as mediators, but also produce meaning from pre-trained structures, inference systems, and probabilistic logic (Schulz, Patrício & Odijk, 2024 ). This shift introduces forms of pedagogical and narrative communication that challenge the traditional boundaries of planned and closed discourse (Belda-Medina & Goddard, 2024 ). There are, moreover, a whole series of key functions that have been automated: editorial curation, semantic analysis, and content personalization (Vidrih & Mayahi, 2023 ; Sonni et al., 2024 ). Simultaneously, new layers of opacity are generated, where recommendation and classification systems operate with invisible filters (Bender et al., 2021 ) that introduce new vectors of symbolic power in the hands of algorithmic actors. This transformation also affects the structure of audiences, with segmentation, interpretation, and anticipation of preferences. The risk of generating echo chambers also appears, along with an intensification of the attention economy (Chen, 2024 ). 1.2. Storytelling and its Central Role in Digital Communicative Practices In contemporary digital systems, storytelling not only constitutes a way of telling stories, but operates as a mediation infrastructure between subjects, technologies, and discourses. Beyond a conception associated with literature, digital narrative unfolds as a central strategy in the configuration of interactive experiences, identities, and content (Scolari, 2013 ; Jenkins, Ford & Green, 2013 ). Digital storytelling practices adopt multiple forms in digital environments (Author). From ephemeral micro-stories on social networks to immersive narratives in virtual environments, including transmedia productions that address audiences from different platforms. According to Bal ( 2017 ), digital narrative forms part of the creation of subjectivities, as it allows users to tell and reconfigure their experiences in real time, with a negotiation of meanings in public and private spaces. Recent studies (Koenitz et al., 2023 ; Bell, Enssling & Rustad, 2014) advocate for an expanded computational narratology that overcomes the form-content dichotomy, and takes into consideration aspects such as interaction, non-linear temporality, user behavior, and the material conditions of production and circulation. 1.3. The Idiosyncrasy of Digital Storytelling Alexander ( 2017 ) defines digital storytelling as an emergent narrative form that integrates multimedia elements, interactivity, and connectivity to produce immersive and participatory experiences. Its idiosyncrasy would reside in a series of structural and functional features that distinguish it from traditional narrative: non-linearity, active user participation, content modularity, and integration of diverse technological platforms (Jenkins, 2006 ). Following these authors, beyond the digitization of stories, digital storytelling would configure a complex narrative system where the experience of the story is transformed into an interactive and personalized experience. One of the central components would be interactivity, understood as a shared generation of meaning through user actions. Authors like Ryan ( 2015 ) argue that this characteristic alters the classic narrative contract, shifting the reader/spectator toward a role of active participant. Furthermore, hypertextuality allows non-sequential structures that fragment the narrative and reconfigure it based on the user's navigation path (Aarseth, 1997 ; Landow, 2009 ). Manovich ( 2001 ) points to the personalization and adaptability of narratives, with algorithms capable of modifying the story based on the user's choices or characteristics. Another distinctive feature is multimodality, understood as the simultaneous articulation of text, image, sound, video, and animation in the construction of the narrative. This syncretism enriches the experience and redefines the modes of encoding and decoding the narrative message (Robin, 2016 ; Walsh, 2010 ). The ubiquitous and real-time nature of digital communication leads authors like Bouchardon & Fülop (2021) to propose a change in temporality, with narratives organized around multiple, asynchronous, and reconfigurable temporalities, influenced by the rhythm of user use and consumption and the constant updating of content. The participatory and collective dimension in these narratives has also been considered a fundamental feature. Communities not only consume narratives. They also rewrite, remix, and amplify them through practices such as fanfiction and collaborative storytelling (Jenkins et al, 2015 ). This approach, however, finds detractors within the academic sphere. Various authors warn that audience participation, far from implying full democratization of the narrative, can mask dynamics of appropriation, precarization, and concentration of symbolic and economic value in the hands of platforms (Terranova, 2013 ). In this context, authors like Byung-Chul (2023) and López-Mondejar ( 2024 ) observe a crisis in narrative. The latter points to an atrophy of narrative capacity in the digital era, warning that the fragmentation of attention and immediacy in content consumption can affect the construction of meaningful and coherent narratives. 1.4. AI Storytelling. A New Way of Creating? The incorporation of AI into the narrative sphere represents an entry point for machines as narrator agents in digital, interactive, and adaptive environments, transforming the way stories are created, distributed, and consumed (Amato et al., 2019 ). One of its key characteristics is the capacity to generate narratives from large volumes of data and probabilistic language models, being able to structure narrative arcs and adapt to the recipient's profile (Lynch et al., 2023 ). This vector toward narrative automation raises questions. Barandiaran & Pérez-Verdugo ( 2025 ) point toward a series of possible risks and benefits in the widespread adoption of generative AI, including challenges related to authenticity, asymmetry in generative power, and the promotion or atrophy of creativity. According to Zhai et al. ( 2024 ), although systems can simulate complex discursive abilities, their production lacks communicative intention and deep understanding of social and cultural context. They are also systems strongly conditioned by the algorithmic logics of platforms and the ideological frameworks incorporated in their training. Various studies also point toward the possible reproduction of cultural biases and stereotypes present in training data (Ruiz & Domínguez-Lloria, 2024 ), and risks of disinformation, manipulation, and deepfakes (Vaccari & Chadwick, 2020 ). Storytelling, under the influence of AI, is situated in a liminal zone, between statistical imitation, oriented training, and technical creativity, which forces us to rethink notions such as authorship, the creative process, and even the role of audiences in the media system. Also, the pre-established structures and forms of narratives. The objective of this article is to define the main characteristics of the evolution of digital storytelling under the influence of artificial intelligence (AI) at present, based on the most influential scientific research in the field. To achieve this objective, a systematic literature review is implemented following the PRISMA 2020 protocol. 2. Methods The research question is registered with the following formulation: How is storytelling evolving under the influence of AI, according to research with the greatest impact in the scientific field? The secondary questions are posed as follows: What new forms of collaboration between humans and artificial intelligence are emerging in storytelling practices? How is artificial intelligence influencing narrative structures, formats, and modes of expression? How are audience roles changing in AI-mediated storytelling environments? How is the notion of authorship being redefined in the context of AI-generated storytelling? To answer the research question, a systematic literature review will be carried out, following the steps established by the Berkeley Systematic Reviews Group, as outlined in Davis et al. (2014): a) Formulate a focused review question. b) Conduct an exhaustive search and include studies. c) Evaluate the quality of studies and extract data. d) Synthesize the results of the studies. e) Interpret the results and write the report. Additionally, PRISMA 2020 guidelines have been applied to improve the transparency of the review process and the reproducibility of the research. The PRISMA 2020 checklist consists of seven sections with 27 items, some with sub-items (Page et al., 2021). Its implementation aims for a more rigorous, complete, and precise presentation of publications. The flow diagram and verification checklist of the PRISMA model are accessible through the following link (Files >PRISMA): https://osf.io/rwdvh/?view_only=449f8cef4ded49fea58acc42db1f1105 The complete protocol and research methodology were registered on the OSF platform as a preliminary step before beginning the research process, thus guaranteeing its integrity, transparency, and traceability. No modifications were made to the registered protocol. It is linked below: https://osf.io/dbjh9/?view_only=9211b80cc5eb440caecf2d2074bc2846 2.1. Review Protocol 2.1.1. Inclusion Criteria The inclusion criteria are defined based on the research objective. The following will be considered: · Published in journals indexed in Scopus or Web of Science. · Published in journals or by publishers classified between Q1 and Q3 in these rankings. · Published between 2020 and 2025 [cutoff date: 05/05/2025 (DD/MM/YYYY)]. · In any language. · Peer-reviewed or reviewed by an expert editorial committee. · Indexed in Scopus and/or Web of Science, the main global multidisciplinary databases. · Not include books (to avoid duplications). · Address the concepts of "AI" and "Storytelling". · Selected based on the greatest scientific impact — determined by the highest number of citations in multidisciplinary scientific databases. 2.1.2. Search Strategy Document collection begins on 05/05/2025 and concludes on the same day. Following the established protocol, 100 references are downloaded from the Scopus database using the following search criteria: Access the "Start Exploring" page of Scopus: https://www.scopus.com/search/form.uri?display=basic#basic Published in journals or by publishers classified between Q1 and Q3 in these rankings. 3. "Search Documents" tab. "Search Within": article title, abstract, keywords. Search: keywords + Boolean operators: "AI" AND "Storytelling". Before confirming these operators, several combinations are tested: "AI" or "Artificial Intelligence" and "storytelling" or "narrative" or "narration". "AI" or "Artificial Intelligence" and "storytelling" or "narrative". "AI" or "Artificial Intelligence" and "storytelling" and "narrative". "AI" and "storytelling" and "narrative". "AI" and "storytelling" or "narrative". The Boolean operators that offer the most precise approximation to the objective are chosen. 5. Filters: Publication year: 2020–2025. Sort by "Cited by (Highest)". 6. Cutoff date: 05/05/2025. 7. Export the first 100 entries. Collection in Web of Science continues on 05/05/2025, applying this protocol: Access the database: https://www.webofscience.com/wos/woscc/basic-search "Documents" tab. In "Search In": Web of Science Core Collection. "Editions": all. Fields: all. Search: keywords + Boolean operators: "AI" AND "Storytelling". Before confirming these operators, several combinations are tested: "AI" or "Artificial Intelligence" and "storytelling" or "narrative" or "narration". "AI" or "Artificial Intelligence" and "storytelling" or "narrative". "AI" or "Artificial Intelligence" and "storytelling" and "narrative". "AI" and "storytelling" and "narrative". "AI" and "storytelling" or "narrative". Filters: Filter by years: 2020–2025. Sort by: "Citations: highest first". Cutoff date: 05/05/2025. Export the first 100 entries. 2.1.3. Selection Process A selection process is established according to the research objective, with the following key validation conditions: · The article or document considers the relationship between AI and storytelling. · It is relevant and up-to-date: published between 2020 and 2025 (limit 05/05/2025). · It is scientific research indexed in global multidisciplinary databases. · It is among the most cited in the 2020–2025 period. · Only individual studies are included, not compilations or collective books (to avoid duplications). 2.1.4. Evaluation of Study Quality Quality is ensured through: - Indexing in Web of Science or Scopus. - Peer review and validation by recognized scientific editorial committees (considering journal quality indices). - Journal membership in the top three quartiles of WoS or Scopus rankings. 2.1.5. Data Extraction and Results Synthesis For information extraction, a standardized database is created and the following are recorded: title, authors, journal, DOI, abstract, and number of citations. A document is recorded for each of the consulted platforms (WoS and Scopus). They can be consulted here (FILES> REFERENCES DATABASE): https://osf.io/rwdvh/?view_only=449f8cef4ded49fea58acc42db1f1105 For content analysis, Discourse Analysis is used, combining Grounded Theory with the Constant Comparative Method (CCM). The former involves several phases of data collection, refinement, and categorization (Kolb, 2012). The CCM develops coding systems that identify constructs and iteratively reviews assigned texts to ensure their representativeness (Olson et al., 2016). Their combination is useful for identifying emerging patterns and conceptual frameworks in a developing field, allowing for a rigorous qualitative synthesis of findings. The coding protocol is organized into four phases: Initial coding: reading and extraction of key data; annotation of relevant fragments with initial codes. Constant comparison: contrast codes, identify patterns, adjust the codebook. Refinement and categorization (axial coding): group codes and define relationships. Narrative synthesis: present results in a codebook and write a narrative description; develop conclusions. The coding work can be consulted at the following link (FILES>ATLAS TI CODIFICATION): https://osf.io/rwdvh/?view_only=449f8cef4ded49fea58acc42db1f1105 This protocol is implemented by three researchers with assigned tasks: Researcher 1 1.1. Initial coding of articles 1–18. Each researcher adds descriptive codes in Atlas.TI to define what they consider relevant in relation to the objective. Reflections generated during analysis are saved as memos. 1.2. Constant comparison of articles 19–36 and joint discussion. The sample documents are exchanged between researchers 1 and 2, and the analysis is performed again, adding new codes and annotating reflections/comments through memos. 1.4. Joint discussion, final categorization, and narrative synthesis. At the end of the comparative exercise, researchers 1-2-3 meet to discuss and define the final groups of codes and memos. The groups will allow identifying causal and semantic relationships. 1.5. Researcher 1 compiles the results in summary tables. 1.6. Researcher 1 extracts preliminary conclusions based on the results. Narrative and causal relationships are established, developing a substantive theory about the discursive phenomenon. Researchers 2 and 3 review, comment/discuss, and contribute relevant reflections aimed at shaping the research conclusions and discussions. Researcher 2 1.1. Initial coding of articles 19–36. 1.2. Constant comparison of articles 1–18 and joint discussion. 1.4. Joint discussion, final categorization, and narrative synthesis. 1.6. Researcher 1 extracts preliminary conclusions based on the results. Narrative and causal relationships are established, developing a substantive theory about the discursive phenomenon. Researchers 2 and 3 review, comment/discuss, and contribute relevant reflections aimed at shaping the research conclusions and discussions. Researcher 3 1.3. Refinement. Fine-tune categories, codes, and theoretical concepts. 1.4. Joint discussion, final categorization, and narrative synthesis. 1.6. Researcher 1 extracts preliminary conclusions based on the results. Narrative and causal relationships are established, developing a substantive theory about the discursive phenomenon. Researchers 2 and 3 review, comment/discuss, and contribute relevant reflections aimed at shaping the research conclusions and discussions. The software used for analysis is Atlas.TI. According to Herbst et al. (2024), it contributes to preserving data integrity and generating scientific knowledge. It facilitates the identification of patterns and relationships through advanced query tools, supports collaborative work with version control, and allows for export and report creation. 2.1.6. PRISMA Flow Diagram and Sample Below is the flow diagram (Fig.1) following the PRISMA 2020 protocol (Page et al., 2021). The final composition of the sample can be consulted in the following databases (FILES>REFERENCES DATABASE): https://osf.io/rwdvh/?view_only=449f8cef4ded49fea58acc42db1f1105 No automation tools were used for any of the sampling phases. Nor for the coding and analysis of the sample. 3. Results The results are presented as a narrative synthesis based on the coding and categorization of the analysis. In brackets, the main codes that collect the category in the sample. 3.1. AI and Storytelling Evolution: Key Points 3.1.1. Performative AI and Embodiment AI is shown as a performative and, occasionally, embodied and semi-intentional entity. AI systems are represented in narrative and social environments. Main characteristics: Physical incarnation/embodiment [Physical impersonation, embodiment]: AI acquires a tangible presence, often through robots, voice-activated toys, or embedded interfaces, such as stuffed animals or mobile devices. Narrative agency [Enacting diegesis keypoints, Characters interaction with users]: AI positions itself as a narrative actor capable of performing functions relevant to the story, triggering plot development, and modifying diegetic worlds. Social interaction and relational presence [Social embodiment, Interaction process]: Personification is not limited to appearance or function, but extends to the way AI interacts socially. These systems can be designed to "speak," "respond," and "relate" as if they were animated beings, integrated into a shared communicative space. 3.1.2. Automation and Procedural Narrative Generation AI systems have the capacity to generate narratives through automated, procedural, and scalable processes. Main characteristics: Procedural generation, multiplicity and variability [Real time variability / Procedural, Real time interaction, re-telling]: Narratives are not fixed. Stories can be regenerated, reinterpreted, or restructured dynamically. Procedural adaptability [Real time interaction, Real time variability / Procedural, Interaction process]: Narratives can change in real time depending on user interaction, suggesting a form of procedural and reactive narration, rather than a fixed linear script. Scalability and replicability [Content generators, Story continuations, Infinite ramifications]: They can be designed for massive implementation, with the capacity to maintain open and branched structures with minimal human intervention. This could consolidate continuous narration or perpetual narration. 3.1.3 Multimodal, Crossmodal and Multilayer Storytelling (Real-time Change) Potential for combined and simultaneous use of multiple media (text, image, audio, video, etc.) that interact with each other and integrate into overlapping narrative layers. These layers can be reorganized dynamically in real time according to interaction or context. Main characteristics: Multimodality [Multimodality, visual storytelling, Verbal, Orality/Voice]: Capacity to process, generate, and combine different types of data—such as text, image, audio, or video—in a single architecture or model, allowing fluid interaction between modalities. Semantic crossmodality [crossmodal telling]: Ability of a model to understand an input in one modality and generate a coherent output in another, establishing semantic links between different modes of representation. Multilayer/multidimensional [Multidimensional: diegetic, Multidimensional: extradiegetic / multilayer]: Capacity to generate narratives in multiple interrelated layers, where each layer represents a distinct dimension of the story—such as the main plot, subplots, emotional context, cultural references, or symbolic levels—and can be activated, explored, or modified dynamically. 3.1.4. Data, Algorithmic Intervention and Adaptability These systems do not generate stories neutrally. They are programmed to operate within limits defined by data and algorithms that carry implicit intentions, patterns, and biases. There is algorithmic intention. Main characteristics: Data training and orientation [AI trained for specific uses, Data mining, Training ends]: Systems are based on previously defined data and training objectives, which limits their capacity to respond meaningfully in unknown or complex narrative contexts. Biases and lack of transparency [Bias & misrepresentation, Lack of Transparency]: Algorithmic narration reproduces existing cultural prejudices and blind spots of dominant social systems. A reproduction of already existing prejudices occurs. An inability to capture contextual nuances in marginal narratives is observed. Personalization based on user profile [Profile adaptation, Personalization (audience multirole)]: Story content can be tailored to user profiles, adjusting language, pace, or content based on demographic or interactional data. Emotional and experiential responsiveness [Feelings adaptation: mood, hedonic, emotion, Emotion RT adaptation]: AI narration can include detection of user reactions through real-time data (voice, gestures, emotional signals), enabling a reactive form of narration that incorporates affective and experiential layers. Goal-driven algorithmic design [Goal Driven coding, edit, design customization]: AI proposals are deliberately shaped by design intentions, editing constraints, and possible customization of results. 3.2. AI and Storytelling: Emerging Forms of Creative Processes Narrative creation is conceptualized primarily as a hybrid act, in which meaning, structure, and style are constructed jointly between human intention and artificial intelligence generation. Main characteristics: Process automation [Automation, efficiency]: Optimizes creative workflows, reducing the cognitive or procedural burden on human participants. Dialogic and interactive storytelling [Interaction process, Interactive, Conversation, Turns tale]: Narration is not a unidirectional act, but an exchange between AI and human participants, often shaped by turns, real-time inputs, and iterative responses. Goal-based guidance and structure [Goal Driven coding/edit/design customization, Assisted agency]: Co-creation is determined by objectives, whether stylistic, thematic, or purposeful. Enhanced creativity and expression [Enhancement, Multimodal creativity democratization, Automating processes]: The efficiency and capacity to improve users' natural expressive potential is highlighted. AI is equated to an instrument, employed in the narrative field, that enables, restructures, or accelerates creative processes. Democratized creative access [democratization/ multimodal creativity democratization]: Some multimodal AI tools facilitate access to generativity of forms and content for unqualified profiles or those without specific skills through simplified interaction models and interfaces. Persistent AI gaps [Lack of semantic understanding]: AI limitations persist, such as its dependence on pattern prediction, its lack of deep contextual or cultural understanding in some cases. 3.3. Changing Roles of Audiences and Narrative Uses Predominantly, a distinctive role of the audience in AI-driven narrative is described: from consumers to agents who collaborate in constructing interaction, story, and emotional process. Main characteristics: Narrative independence of the audience [Real time interaction, Real time variability / Procedural, AI support or tool or assistant]: The user/audience has the capacity to construct, adapt, or even create their own narrative experience, beyond the control of the author or traditional narrative framework. Displacement and redistribution of authorial power [Deplacing (human as mediation)]: The boundary between author and audience becomes destabilized. As users and AI agents become co-authors, different authorial functions are redistributed. Multirole, multi-identity [Multimodality, Real time variability, Real Time interaction]: In the same narrative, users can fulfill different roles and assume different identities, both within the diegetic process and in the creative process itself. User self-managed storytelling [Profile adaptation, Personalization (audience multirole)]: The user or recipient can be placed at the center of the story's design and experience. It's not just about telling a story, but about self-managing narrative conditions adapted to interests, emotions, decisions, and trajectories through their data. Affective and emotional bonding [User attachment / Affectiveness, Likeability, AI fosters engagement]: Interaction is both functional and emotional. AI systems foster user attachment, creating empathetic and affective bonds. 3.4. Authorship, Plagiarism and Synthetic Perspectives Authorship tends to be reflected as a distributed assemblage between immediate design, algorithmic reproduction, and iterative refinement. Main characteristics: Prompt-based authorship [Prompting creation]: Content generation is triggered from elaborate instructions, which shifts authorship from mediated creation to strategic design of input and iteration instructions. Plagiarized re-generated originality [Plagiarism]: The way LLMs (Large Language Models) work based on pre-existing patterns and data raises questions about originality, repetition, and possible accusations of algorithmic plagiarism. Displacement of the author figure [Deplacing (human as mediation)]: The human author sometimes appears as a mediator or subsidiary figure, raising questions about legitimacy, ownership, and authority in synthetic writing. Synthetic perspective [Object + relationship + temporal dimension for synthetic understanding]: The synthetic perspective assumes greater prominence in the authorial process, ceasing to be assistive to become an agent. Split authorship [Author roles: creator, assistant, optimizer, reviewer, Opaque Authorship]: The author's role is divided between the code that generates content, the trainer who shapes the model's behavior, and the narrator who delivers the result. It is a stratified system of agents. Humanness [Humanness]: A synthetic simulation of the human is pursued, becoming a qualification category for synthetic operations. 4. Discussion and Conclusions This research delves into the evolution of storytelling under the influence of artificial intelligence in creative industries, through a systematic review of articles with the greatest scientific impact that address this topic. The results point to an initiatory stage within the area, where changes in AI technology itself are perceptible even within the five-year difference of the sample period. In Ghajargar, Bardzell & Lagerkvit (2022), there is direct reference to a "lack of semantic understanding," while Li, Wang & Qu ( 2024 ) already affirm that language models like ChatGPT can form linguistically precise associations taking into account relationships between words in a sentence. Based on the criteria established for sample selection, AI adopts different forms in the researched articles, although there is a predominant focus on generative models, both linguistic and multimodal. From the analyzed texts emerges a technophilic vision of AI's impact on narrative, in a line that presents certain continuity with previous academic currents that addressed recent technological developments early on. These techno-optimist discourses achieved a dominant character at moments like the creation of the Web, and the idea of decentralization and global participation (Berners-Lee & Fischetti, 2000 ; Kelly, 1998 ), or emancipation through interaction between users and media co-creation (Jenkins et al, 2015 ; Scolari, 2013 ). The results identify what can be characterized as force vectors: distinct directional tendencies that define artificial intelligence's evolving influence on narrative. AI is not presented solely as an algorithmic system. It incorporates a performative and agentic capacity within narrative systems, being able to acquire an embodied or materialized entity, especially through robots (Hubbard et al.,2021; Ligthart, Neerincx & Hindriks, 2020 ; Elgarf et al., 2022 ). This coincides with what Feng et al. ( 2025 ) collected, when affirming that this performativity extends, being able to perceive, reason, and interact with its environments. It is capable of automating diegetic and extradiegetic processes, offering procedural, replicable, and scalable narratives, with dynamic variations. Some authors have proposed models to develop some of these possibilities (Kumaran et al., 2023 ; Buongiorno et al., 2024 ). Technically, continuous narrative, or perpetual narrative, could be generated. The sample also alludes to multimodal, crossmodal, and multilayer potential, making specific mention of the option to change between modes and dimensions in real time. Text positions itself, again, as the main backbone of stories, but this time, above all, from the logic of the prompt (instruction) as trigger. In line with other studies, the prompt is assigned a nuclear value in AI generativity (Oppenlaender, Linder & Silvennoinen, 2024 ; Park & Choo, 2024 ). The creative process is cataloged primarily as a collaborative human-machine act (Bender, 2023 ; Zhang et al., 2022 ). Process automation occurs, with dialogic exchanges that transform the notion of unidirectional narrative act into an exchange between AI and human participants, shaped by turns, real-time inputs, and iterative work. The sample also emphasizes the idea of democratization (Pellas, 2023 ), with access to multimodal generativity for unqualified profiles (O'Meara & Murphy, 2023), and that of augmented creativity, with improvement of users' expressive potential. This perspective finds detractors within academia. For Lee ( 2022 ), AI dissociates creativity from human agency, under the protection of a conception of creative industries that dehumanizes the creative process by hiding working conditions and treating it as human capital generating IPs (intellectual properties). Runco ( 2024 ), for his part, maintains that the processes used by generative artificial intelligence suggest discovery, not creativity. In any case, the creative process is not neutral, and is conditioned by the data and training objectives with which AI systems are built. This fact questions a supposed neutrality of narratives created with AI, with biases and lack of transparency. Above all, regarding minoritized or vulnerabilized groups underrepresented in data and training methods. This is one of the aspects to which the scientific community has paid most attention (Varona & Suárez, 2022 ; Hou, Tseng & Yuan, 2024 ; Hofmann, 2024). The invasiveness of these systems, along with the complicity of their users, can lead to absolute personalization of stories, attending to biographical and biometric data, allowing the creation of narratives with response capacity that detect user reactions in real time, modulating affective and experiential layers. In this sense, Gorenc ( 2025 ) points to deep filtering of information that the user receives and measured influence on their decisions. He also verifies in his research that users dissociate between their self-perception of knowledge of AI-driven platforms and their biases, and their real behavior on such platforms. The audience framework shifts toward a notion closer to that of the user, in line with Bruns' (2008) produser. It points toward a path of narrative self-management, an independence or possession at will of the author figure, in collaboration with the machine, but this is not predominant in the sample. There is insistence on narrative co-creation (Li, Wang, Qu, 2024 ; Yan et al., 2023 ), in which the user is simultaneously the one who creates and the one who experiences the story. AI systems enable the real-time adoption of variable roles in narrative development as well as different diegetic identities. In the human-machine relationship, various authors mention the likeability, affectiveness, and engagement that these systems arouse, consolidating Reeves & Naas's (1996) argument about the human tendency to respond to media and technologies as if they were real human beings. It is noteworthy that one of the criteria for evaluating these systems is precisely that of humanness (Nichols, Gao, Gomez, 2020 ). In authorship, the synthetic perspective assumes greater prominence, raising doubts about the originality and legitimacy of works, as posed by Fenwick & Jurcys ( 2023 ). The way Large Language Models work, with massive absorption of third-party data and pattern reproduction, poses creation rooted in massive plagiarism. In these systems, authorship is multiple: the data that gives rise to content, the trainer who shapes the model's behavior, the narrator who tells the story... More than a death of the author, in the Barthesian sense of the term (Barthes, 1968 ), we would find here a severed authorship, where co-authors have no connection to each other. The systematic review of scientific literature does not lead to the consolidation of a phenomenon that we could call AI Storytelling in the present, comparable to the acclaimed Transmedia storytelling (Jenkins, 2006 ; Scolari, 2013 ) in academic circles. Nor does it establish new specific narrative formats, beyond the dynamic uses of ChatGPT, that are standardizable by cultural industries. It does offer us a series of keys to understand how artificial intelligence is affecting storytelling and what forces are beginning to take shape under the premise of joint narrative creation between humans and intelligent synthetic systems. More than a narrative evolution in a strictly technical or formal sense, what seems to be consolidating is an increasingly widespread tendency toward transhumanist horizons, as defined by Hayles ( 1999 ). Synthetic participation is gaining greater relevance, not only as an assistant, but, above all, as a multidimensional agent. It is capable of operating across multiple narrative levels—diegetic, extradiegetic, hermeneutic, procedural, performative, and symbolic—within various narrative environments, including physical reality itself. It represents an ontological mutation of the traditional narrative agent: AI both intervenes in and participates —while remaining an object— in the construction of human fictions, realities, and imaginaries. This research presents certain limitations. First, the sampling criteria exclude pre-2020 publications that may contain foundational contributions to the field. Second, the methodology focuses exclusively on academic sources, overlooking non-academic perspectives that could provide valuable insights into the phenomenon. The focus on creative processes of the sample analyzed in this research suggests, as a possible future line, the need to review specific literature on artificial intelligence and creativity. Likewise, to broaden perspectives, it would be revealing to delve into the phenomenon of AI narrative from the perspective of professionals and sector specialists, using both quantitative (surveys) and qualitative (in-depth interviews, focus groups) methodologies. Also, incorporating perspectives and agencies of minoritized and vulnerabilized groups, affected by the reproduction of artificial intelligence prejudices and biases, through PAR (Participatory Action Research) and ABR (Arts Based Research) methodologies. Declarations Competing interests The authors declare no competing interests. Ethical statements This article does not contain any studies with human participants performed by any of the authors. Author Contribution Conceptualization: ISLData curation: AGCFormal Analysis: ISL, AGCFunding Acquisition: ISLInvestigation: ISL, AGC, ARMethodology: ISL, AGCProject administration: ARResources: ARSoftware: ISL, AGCSupervision: ARValidation: ARVisualization: ISL, AGCWriting – original draft: ISL, AGCWriting – review and editing: AR Acknowledgement This research is funded by the Ramón y Cajal Grant from the Ministry of Science, Innovation and Universities/State Research Agency of Spain.Grant RYC2023-044777-I, funded by MICIU/AEI/10.13039/501100011033 and by ESF+. Data Availability Protocol and methodology of this study have been registered in the Open Science Framework Platform:https://osf.io/dbjh9/?view_only=9211b80cc5eb440caecf2d2074bc2846Methodology, PRISMA Documents, Sample Databases, Atlas.TI coding file (FILES) have been deposited in the Open Science Framework Platform:https://osf.io/rwdvh/?view_only=449f8cef4ded49fea58acc42db1f1105 References Aarseth E (1997) Cybertext: Perspectives on ergodic literature. Johns Hopkins University Alexander B (2017) The new digital storytelling: Creating narratives with new media, 2nd edn. Praeger Amato G et al (2019) AI in the media and creative industries. arXiv. https://doi.org/10.48550/arxiv.1905.04175 Bal M (2017) Narratology: Introduction to the theory of narrative, 4th edn. University of Toronto Barandiaran XE, Pérez-Verdugo M (2025) Generative midtended cognition and Artificial Intelligence: thinging with thinging things. Synthese 205(4). https://doi.org/10.1007/s11229-025-04961-4 Barthes R (1968) La mort de l’auteur. Manteia Belda-Medina J, Goddard C (2024) AI-driven digital storytelling: A strategy for creating English as a foreign language learning environments. Int J Linguistics Stud 4(1):40–49. https://doi.org/10.32996/ijls.2024.4.1.4 Bell A, Ensslin A, Rustad H (2014) Analysing digital fiction. Routledge Bender EM, Gebru T, McMillan-Major A, Shmitchell S (2021) On the dangers of stochastic parrots: Can language models be too big? In: FAccT ’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, pp 610–623. https://doi.org/10.1145/3442188.3445922 Bender SM (2023) Coexistence and creativity: screen media education in the age of artificial intelligence content generators. Media Pract Educ 24(4):351–366. https://doi.org/10.1080/25741136.2023.2204203 Berners-Lee T, Fischetti M (2000) Weaving the web: The original design and ultimate destiny of the World Wide Web by its inventor. Harper San Francisco Bouchardon S, Fülöp E (2021) Digital Narrative and Temporality. In: Mitchell A, Vosmeer M (eds) Interactive Storytelling. ICIDS 2021. Lecture Notes in Computer Science, vol 13138. Springer, Cham. https://doi.org/10.1007/978-3-030-92300-6_29 Bruns A (2008) Blogs, Wikipedia, Second Life, and beyond: From production to produsage. Peter Lang, New York Buongiorno S, Klinkert LJ, Chawla T, Zhuang Z, Clark C (2024) PANGeA: Procedural artificial narrative using generative AI for turn-based video games. arXiv. https://arxiv.org/abs/2404.19721 Chen G (2024) The impacts of AI-driven storytelling applications on language acquisition and literacy development in early childhood education: A systematic review. Asian J Adv Res Rep 18(11):1–12. https://doi.org/10.9734/ajarr/2024/v18i11770 Davis J, Mengersen K, Bennett S, Mazerolle L (2014) Viewing systematic reviews and meta-analysis in social research through different lenses. SpringerPlus 3:511. https://doi.org/10.1186/2193-1801-3-511 Elgarf M, Zojaji S, Skantze G, Peters C (2022) CreativeBot: A creative storyteller robot to stimulate creativity in children. In: Proceedings of the 2022 International Conference on Multimodal Interaction (ICMI ’22). Association for Computing Machinery, pp 540–548. https://doi.org/10.1145/3536221.3556578 Fang X, Ng DTK, Leung JKL, Chu SKW (2023) A systematic review of artificial intelligence technologies used for story writing. Educ Inform Technol 28:14361–14397. https://doi.org/10.1007/s10639-023-11741-5 Feng Z et al (2025) Multi-agent embodied AI: Advances and future directions. arXiv. https://arxiv.org/abs/2505.05108 Fenwick M, Jurcys P (2023) Originality and the future of copyright in an age of generative AI. Comput Law Secur Rev 51:105892. https://doi.org/10.1016/j.clsr.2023.105892 Ghajargar M, Bardzell J, Lagerkvist L (2022) A redhead walks into a bar: Experiences of writing fiction with artificial intelligence. In: Proceedings of the 25th International Academic Mindtrek Conference. Association for Computing Machinery, pp 1–12. https://doi.org/10.1145/3569219.3569418 Gorenc N (2025) AI embedded bias on social platforms. Int Rev Sociol 1–20. https://doi.org/10.1080/03906701.2025.2489056 Harahap K, Muhtar S, Sakarina S, Shofwani SA (2025) Digital narratives and AI in modern media: A systematic literature review. Tech Fusion in Business and Society. Springer, pp 197–205. https://doi.org/10.1007/978-3-031-84636-6_16 Hayles NK (1999) How we became posthuman: Virtual bodies in cybernetics, literature, and informatics. University of Chicago Press Herbst RS, Frizzarini ST, Herbst GM (2024) ATLAS.ti® in qualitative research: Expanding horizons in oral history analysis. Interconnections Knowledge: Multidisciplinary Approaches 974–994. https://doi.org/10.56238/SEVENED2024.010-062 Hofmann V, Kalluri PR, Jurafsky D, Goel S (2024) AI generates covertly racist decisions about people based on their dialect. Nature 633:147–154. https://doi.org/10.1038/s41586-024-07856-5 Hou T-Y, Tseng Y-C, Yuan CW, T. (2024) Is this AI sexist? The effects of a biased AI’s anthropomorphic appearance and explainability on users’ bias perceptions and trust. Int J Inf Manag 76:102775. https://doi.org/10.1016/j.ijinfomgt.2024.102775 Hubbard LJ, Chen Y, Colunga E, Kim P, Yeh T (2021) Child-robot interaction to integrate reflective storytelling into creative play. In: Proceedings of the 13th Conference on Creativity and Cognition (C&C ’21). Association for Computing Machinery, Article 16, pp 1–8. https://doi.org/10.1145/3450741.3465254 Jenkins H (2006) Convergence culture: Where old and new media collide. NYU Jenkins H, Ford S, Green J (2013) Spreadable media: Creating value and meaning in a networked culture. NYU Jenkins H, Shresthova S, Gamber-Thompson L, Kligler-Vilenchik N, Zimmerman AM (2015) By any media necessary: The new youth activism. NYU Kelly K (1998) New rules for the new economy: 10 radical strategies for a connected world. Viking Koenitz H, Gjedde L, Haahr M, Sezen D, Ferri G (eds) (2023) Understanding interactive digital narratives: Immersive expressions for a complex time. Routledge Kolb SM (2012) Grounded theory and the constant comparative method: Valid research strategies for educators. J Emerg Trends Educational Res Policy Stud 3(1):83–86 Kumaran V, Rowe J, Mott B, Lester J (2023) SceneCraft: Automating interactive narrative scene generation in digital games with large language models. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 19(1):86–96. https://doi.org/10.1609/aiide.v19i1.27504 Landow GP (2009) Hypertext 3.0: Critical theory and new media in an era of globalization. Johns Hopkins University Lee H-K (2022) Rethinking creativity: creative industries, AI and everyday creativity. Media Cult Soc 44(3):601–612. https://doi.org/10.1177/01634437221077009 Li H, Wang Y, Qu H (2024) Where are we so far? Understanding data storytelling tools from the perspective of human-AI collaboration. In: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI ’24). Association for Computing Machinery, Article 845, pp 1–19. https://doi.org/10.1145/3613904.3642726 Ligthart MEU, Neerincx MA, Hindriks KV (2020) Design patterns for an interactive storytelling robot to support children's engagement and agency. In: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’20). Association for Computing Machinery, pp 409–418. https://doi.org/10.1145/3319502.3374826 Lynch CJ, Jensen EJ, Zamponi V, O’Brien K, Frydenlund E, Gore R (2023) A structured narrative prompt for prompting narratives from large language models: Sentiment assessment of ChatGPT-generated narratives and real tweets. Future Internet 15(12):375. https://doi.org/10.3390/fi15120375 López-Mondejar J (2024) Sin relato: Atrofia de la capacidad narrativa y crisis de la subjetividad. Anagrama Manovich L (2001) The language of new media. MIT Press O’Meara J, Murphy C (2023) Aberrant AI creations: co-creating surrealist body horror using the DALL-E Mini text-to-image generator. Convergence 29(4):1070–1096. https://doi.org/10.1177/13548565231185865 Nichols E, Gao L, Gomez R (2020) Collaborative storytelling with large-scale neural language models. In: Proceedings of the 13th ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG ’20). Association for Computing Machinery, Article 17, pp 1–10. https://doi.org/10.1145/3424636.3426903 Olson JD, McAllister C, Grinnell LD, Gehrke Walters K, Appunn F (2016) Applying constant comparative method with multiple investigators and inter-coder reliability. Qualitative Rep 21(1):26–42. https://doi.org/10.46743/2160-3715/2016.2447 Oppenlaender J, Linder R, Silvennoinen J (2024) Prompting AI art: An investigation into the creative skill of prompt engineering. Int J Human–Computer Interact. https://doi.org/10.1080/10447318.2024.2431761 Page MJ et al (2021) The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 372:n71. https://doi.org/10.1136/bmj.n71 Park J, Choo S (2024) Generative AI prompt engineering for educators: Practical strategies. J Special Educ Technol. https://doi.org/10.1177/01626434241298954 Pellas N (2023) The effects of generative AI platforms on undergraduates’ narrative intelligence and writing self-efficacy. Educ Sci 13(11):1155. https://doi.org/10.3390/educsci13111155 Reeves B, Nass CI (1996) The media equation: How people treat computers, television, and new media like real people and places. Center for the Study of Language and Information; Cambridge University Press Robin B (2016) The power of digital storytelling to support teaching and learning. Digit Educ Rev 30:17–29 Ruiz PG, Domínguez-Lloria S (2024) La inteligencia artificial generativa de imágenes como colaboradora para la creatividad: un estudio de caso grupal. Arte Individuo y Sociedad. https://doi.org/10.5209/aris.99030 Runco MA (2024) The discovery and innovation of AI does not qualify as creativity. J Cogn Psychol 1–10. https://doi.org/10.1080/20445911.2024.2436362 Ryan M-L (2015) Narrative as virtual reality 2: Revisiting immersion and interactivity in literature and electronic media. Johns Hopkins University Schulz L, Patrício M, Odijk D (2024) Narrative information theory. arXiv. https://arxiv.org/abs/2411.12907 Scolari CA (2013) Narrativas transmedia: Cuando todos los medios cuentan. Deusto Sonni AF, Hafied H, Irwanto I, Latuheru R (2024) Digital newsroom transformation: A systematic review of the impact of artificial intelligence on journalistic practices, news narratives, and ethical challenges. Journalism Media 5(4):1554–1570. https://doi.org/10.3390/journalmedia5040097 Terranova T (2013) Free labor. In: Scholz T (ed) Digital labor: The Internet as playground and factory. Routledge, pp 33–57 Vaccari C, Chadwick A (2020) Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Social Media + Soc 6(1). https://doi.org/10.1177/2056305120903408 Varona D, Suárez JL (2022) Discrimination, bias, fairness, and trustworthy AI. Appl Sci 12(12):5826. https://doi.org/10.3390/app12125826 Vidrih M, Mayahi S (2023) Generative AI-driven storytelling: A new era for marketing. https://doi.org/10.48550/arXiv.2309.09048 . arXiv Walsh M (2010) Multimodal literacy: What does it mean for classroom practice? Australian J Lang Lit 33(3):211–239. https://doi.org/10.1007/BF03651836 Yan Z, Yang C, Liang Q, Chen XA (2023) XCreation: A graph-based crossmodal generative creativity support tool. In: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST ’23). Association for Computing Machinery, Article 48, pp 1–15. https://doi.org/10.1145/3586183.3606826 Zhai X, Nyaaba M, Ma W (2024) Can generative AI and ChatGPT outperform humans on cognitive-demanding problem-solving tasks in science? Sci Educ. https://doi.org/10.1007/s11191-024-00496-1 Zhang Z, Xu Y, Wang Y, Yao B, Ritchie D, Wu T, Yu M, Wang D, Li TJ-J (2022) StoryBuddy: A human-AI collaborative chatbot for parent-child interactive storytelling with flexible parental involvement. In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). Association for Computing Machinery, Article 218, pp 1–21. https://doi.org/10.1145/3491102.3517479 Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":129297,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of studies via databases and registers. Flow diagram following the PRISMA protocol. Source: own elaboration.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7318246/v1/8d12000e0ec7c1f46b37479f.png"},{"id":102496761,"identity":"3ad390ea-cd51-443a-961c-51513eeaa435","added_by":"auto","created_at":"2026-02-12 09:43:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":772030,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7318246/v1/658502bc-16d2-4b8b-973c-3946c5f851bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI storytelling \u0026 narrative evolution in creative industries: a Systematic Review.","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1. AI and the Reconfiguration of Communicative Dynamics\u003c/h2\u003e\u003cp\u003eThe emergence of artificial intelligence in the digital and media sphere has generated a structural transformation of communicative dynamics, evolving into a central element that reconfigures the system as a whole (Sonni et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Harahap et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Specifically, its capacity to analyze large volumes of data, detect patterns, synthesize information, and generate automatic content has decentralized human communicative agency, redefining traditionally consolidated principles such as authorship and enunciation processes (Fang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this new framework, AI systems not only operate as mediators, but also produce meaning from pre-trained structures, inference systems, and probabilistic logic (Schulz, Patr\u0026iacute;cio \u0026amp; Odijk, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This shift introduces forms of pedagogical and narrative communication that challenge the traditional boundaries of planned and closed discourse (Belda-Medina \u0026amp; Goddard, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThere are, moreover, a whole series of key functions that have been automated: editorial curation, semantic analysis, and content personalization (Vidrih \u0026amp; Mayahi, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sonni et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Simultaneously, new layers of opacity are generated, where recommendation and classification systems operate with invisible filters (Bender et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) that introduce new vectors of symbolic power in the hands of algorithmic actors.\u003c/p\u003e\u003cp\u003eThis transformation also affects the structure of audiences, with segmentation, interpretation, and anticipation of preferences. The risk of generating echo chambers also appears, along with an intensification of the attention economy (Chen, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2. Storytelling and its Central Role in Digital Communicative Practices\u003c/h2\u003e\u003cp\u003eIn contemporary digital systems, storytelling not only constitutes a way of telling stories, but operates as a mediation infrastructure between subjects, technologies, and discourses. Beyond a conception associated with literature, digital narrative unfolds as a central strategy in the configuration of interactive experiences, identities, and content (Scolari, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jenkins, Ford \u0026amp; Green, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDigital storytelling practices adopt multiple forms in digital environments (Author). From ephemeral micro-stories on social networks to immersive narratives in virtual environments, including transmedia productions that address audiences from different platforms. According to Bal (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), digital narrative forms part of the creation of subjectivities, as it allows users to tell and reconfigure their experiences in real time, with a negotiation of meanings in public and private spaces. Recent studies (Koenitz et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bell, Enssling \u0026amp; Rustad, 2014) advocate for an expanded computational narratology that overcomes the form-content dichotomy, and takes into consideration aspects such as interaction, non-linear temporality, user behavior, and the material conditions of production and circulation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3. The Idiosyncrasy of Digital Storytelling\u003c/h2\u003e\u003cp\u003eAlexander (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) defines digital storytelling as an emergent narrative form that integrates multimedia elements, interactivity, and connectivity to produce immersive and participatory experiences. Its idiosyncrasy would reside in a series of structural and functional features that distinguish it from traditional narrative: non-linearity, active user participation, content modularity, and integration of diverse technological platforms (Jenkins, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Following these authors, beyond the digitization of stories, digital storytelling would configure a complex narrative system where the experience of the story is transformed into an interactive and personalized experience.\u003c/p\u003e\u003cp\u003eOne of the central components would be interactivity, understood as a shared generation of meaning through user actions. Authors like Ryan (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) argue that this characteristic alters the classic narrative contract, shifting the reader/spectator toward a role of active participant. Furthermore, hypertextuality allows non-sequential structures that fragment the narrative and reconfigure it based on the user's navigation path (Aarseth, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Landow, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Manovich (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) points to the personalization and adaptability of narratives, with algorithms capable of modifying the story based on the user's choices or characteristics.\u003c/p\u003e\u003cp\u003eAnother distinctive feature is multimodality, understood as the simultaneous articulation of text, image, sound, video, and animation in the construction of the narrative. This syncretism enriches the experience and redefines the modes of encoding and decoding the narrative message (Robin, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Walsh, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe ubiquitous and real-time nature of digital communication leads authors like Bouchardon \u0026amp; F\u0026uuml;lop (2021) to propose a change in temporality, with narratives organized around multiple, asynchronous, and reconfigurable temporalities, influenced by the rhythm of user use and consumption and the constant updating of content.\u003c/p\u003e\u003cp\u003eThe participatory and collective dimension in these narratives has also been considered a fundamental feature. Communities not only consume narratives. They also rewrite, remix, and amplify them through practices such as fanfiction and collaborative storytelling (Jenkins et al, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This approach, however, finds detractors within the academic sphere. Various authors warn that audience participation, far from implying full democratization of the narrative, can mask dynamics of appropriation, precarization, and concentration of symbolic and economic value in the hands of platforms (Terranova, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this context, authors like Byung-Chul (2023) and L\u0026oacute;pez-Mondejar (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) observe a crisis in narrative. The latter points to an atrophy of narrative capacity in the digital era, warning that the fragmentation of attention and immediacy in content consumption can affect the construction of meaningful and coherent narratives.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.4. AI Storytelling. A New Way of Creating?\u003c/h2\u003e\u003cp\u003eThe incorporation of AI into the narrative sphere represents an entry point for machines as narrator agents in digital, interactive, and adaptive environments, transforming the way stories are created, distributed, and consumed (Amato et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). One of its key characteristics is the capacity to generate narratives from large volumes of data and probabilistic language models, being able to structure narrative arcs and adapt to the recipient's profile (Lynch et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis vector toward narrative automation raises questions. Barandiaran \u0026amp; P\u0026eacute;rez-Verdugo (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) point toward a series of possible risks and benefits in the widespread adoption of generative AI, including challenges related to authenticity, asymmetry in generative power, and the promotion or atrophy of creativity.\u003c/p\u003e\u003cp\u003eAccording to Zhai et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), although systems can simulate complex discursive abilities, their production lacks communicative intention and deep understanding of social and cultural context. They are also systems strongly conditioned by the algorithmic logics of platforms and the ideological frameworks incorporated in their training. Various studies also point toward the possible reproduction of cultural biases and stereotypes present in training data (Ruiz \u0026amp; Dom\u0026iacute;nguez-Lloria, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and risks of disinformation, manipulation, and deepfakes (Vaccari \u0026amp; Chadwick, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStorytelling, under the influence of AI, is situated in a liminal zone, between statistical imitation, oriented training, and technical creativity, which forces us to rethink notions such as authorship, the creative process, and even the role of audiences in the media system. Also, the pre-established structures and forms of narratives.\u003c/p\u003e\u003cp\u003eThe objective of this article is to define the main characteristics of the evolution of digital storytelling under the influence of artificial intelligence (AI) at present, based on the most influential scientific research in the field. To achieve this objective, a systematic literature review is implemented following the PRISMA 2020 protocol.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThe research question is registered with the following formulation: How is storytelling evolving under the influence of AI, according to research with the greatest impact in the scientific field?\u003c/p\u003e\n\u003cp\u003eThe secondary questions are posed as follows:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eWhat new forms of collaboration between humans and artificial intelligence are emerging in storytelling practices?\u003c/li\u003e\n \u003cli\u003eHow is artificial intelligence influencing narrative structures, formats, and modes of expression?\u003c/li\u003e\n \u003cli\u003eHow are audience roles changing in AI-mediated storytelling environments?\u003c/li\u003e\n \u003cli\u003eHow is the notion of authorship being redefined in the context of AI-generated storytelling?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo answer the research question, a systematic literature review will be carried out, following the steps established by the Berkeley Systematic Reviews Group, as outlined in Davis et al. (2014):\u003c/p\u003e\n\u003cp\u003ea) Formulate a focused review question.\u003c/p\u003e\n\u003cp\u003eb) Conduct an exhaustive search and include studies.\u003c/p\u003e\n\u003cp\u003ec) Evaluate the quality of studies and extract data.\u003c/p\u003e\n\u003cp\u003ed) Synthesize the results of the studies.\u003c/p\u003e\n\u003cp\u003ee) Interpret the results and write the report.\u003c/p\u003e\n\u003cp\u003eAdditionally, PRISMA 2020 guidelines have been applied to improve the transparency of the review process and the reproducibility of the research. The PRISMA 2020 checklist consists of seven sections with 27 items, some with sub-items (Page et al., 2021). Its implementation aims for a more rigorous, complete, and precise presentation of publications. The flow diagram and verification checklist of the PRISMA model are accessible through the following link (Files \u0026gt;PRISMA):\u003c/p\u003e\n\u003cp\u003ehttps://osf.io/rwdvh/?view_only=449f8cef4ded49fea58acc42db1f1105\u003c/p\u003e\n\u003cp\u003eThe complete protocol and research methodology were registered on the OSF platform as a preliminary step before beginning the research process, thus guaranteeing its integrity, transparency, and traceability. No modifications were made to the registered protocol. It is linked below:\u003c/p\u003e\n\u003cp\u003ehttps://osf.io/dbjh9/?view_only=9211b80cc5eb440caecf2d2074bc2846\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.1. Review Protocol\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.1.1. Inclusion Criteria\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria are defined based on the research objective. The following will be considered:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Published in journals indexed in Scopus or Web of Science.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Published in journals or by publishers classified between Q1 and Q3 in these rankings.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Published between 2020 and 2025 [cutoff date: 05/05/2025 (DD/MM/YYYY)].\u003c/p\u003e\n\u003cp\u003e\u0026middot; In any language.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Peer-reviewed or reviewed by an expert editorial committee.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Indexed in Scopus and/or Web of Science, the main global multidisciplinary databases.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Not include books (to avoid duplications).\u003c/p\u003e\n\u003cp\u003e\u0026middot; Address the concepts of \u0026quot;AI\u0026quot; and \u0026quot;Storytelling\u0026quot;.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Selected based on the greatest scientific impact \u0026mdash; determined by the highest number of citations in multidisciplinary scientific databases.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.1.2. Search Strategy\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDocument collection begins on 05/05/2025 and concludes on the same day. Following the established protocol, 100 references are downloaded from the Scopus database using the following search criteria:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eAccess the \u0026quot;Start Exploring\u0026quot; page of Scopus: https://www.scopus.com/search/form.uri?display=basic#basic\u003c/li\u003e\n \u003cli\u003ePublished in journals or by publishers classified between Q1 and Q3 in these rankings.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e3. \u0026nbsp; \u0026quot;Search Documents\u0026quot; tab.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003e\u0026quot;Search Within\u0026quot;: article title, abstract, keywords.\u003c/li\u003e\n \u003cli\u003eSearch: keywords + Boolean operators: \u0026quot;AI\u0026quot; AND \u0026quot;Storytelling\u0026quot;. Before confirming these operators, several combinations are tested:\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003e\u0026quot;AI\u0026quot; or \u0026quot;Artificial Intelligence\u0026quot; and \u0026quot;storytelling\u0026quot; or \u0026quot;narrative\u0026quot; or \u0026quot;narration\u0026quot;.\u003c/li\u003e\n \u003cli\u003e\u0026quot;AI\u0026quot; or \u0026quot;Artificial Intelligence\u0026quot; and \u0026quot;storytelling\u0026quot; or \u0026quot;narrative\u0026quot;.\u003c/li\u003e\n \u003cli\u003e\u0026quot;AI\u0026quot; or \u0026quot;Artificial Intelligence\u0026quot; and \u0026quot;storytelling\u0026quot; and \u0026quot;narrative\u0026quot;.\u003c/li\u003e\n \u003cli\u003e\u0026quot;AI\u0026quot; and \u0026quot;storytelling\u0026quot; and \u0026quot;narrative\u0026quot;.\u003c/li\u003e\n \u003cli\u003e\u0026quot;AI\u0026quot; and \u0026quot;storytelling\u0026quot; or \u0026quot;narrative\u0026quot;.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe Boolean operators that offer the most precise approximation to the objective are chosen.\u003c/p\u003e\n\u003cp\u003e5. Filters:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003ePublication year: 2020\u0026ndash;2025.\u003c/li\u003e\n \u003cli\u003eSort by \u0026quot;Cited by (Highest)\u0026quot;.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e6. Cutoff date: 05/05/2025.\u003c/p\u003e\n\u003cp\u003e7. Export the first 100 entries.\u003c/p\u003e\n\u003cp\u003eCollection in Web of Science continues on 05/05/2025, applying this protocol:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eAccess the database: https://www.webofscience.com/wos/woscc/basic-search\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u0026quot;Documents\u0026quot; tab.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003eIn \u0026quot;Search In\u0026quot;: Web of Science Core Collection. \u0026quot;Editions\u0026quot;: all.\u003c/li\u003e\n \u003cli\u003eFields: all.\u003c/li\u003e\n \u003cli\u003eSearch: keywords + Boolean operators: \u0026quot;AI\u0026quot; AND \u0026quot;Storytelling\u0026quot;. Before confirming these operators, several combinations are tested:\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003e\u0026quot;AI\u0026quot; or \u0026quot;Artificial Intelligence\u0026quot; and \u0026quot;storytelling\u0026quot; or \u0026quot;narrative\u0026quot; or \u0026quot;narration\u0026quot;.\u003c/li\u003e\n \u003cli\u003e\u0026quot;AI\u0026quot; or \u0026quot;Artificial Intelligence\u0026quot; and \u0026quot;storytelling\u0026quot; or \u0026quot;narrative\u0026quot;.\u003c/li\u003e\n \u003cli\u003e\u0026quot;AI\u0026quot; or \u0026quot;Artificial Intelligence\u0026quot; and \u0026quot;storytelling\u0026quot; and \u0026quot;narrative\u0026quot;.\u003c/li\u003e\n \u003cli\u003e\u0026quot;AI\u0026quot; and \u0026quot;storytelling\u0026quot; and \u0026quot;narrative\u0026quot;.\u003c/li\u003e\n \u003cli\u003e\u0026quot;AI\u0026quot; and \u0026quot;storytelling\u0026quot; or \u0026quot;narrative\u0026quot;.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/li\u003e\n \u003cli\u003eFilters:\u003c/li\u003e\n \u003cli\u003eFilter by years: 2020\u0026ndash;2025.\u003c/li\u003e\n \u003cli\u003eSort by: \u0026quot;Citations: highest first\u0026quot;.\u003c/li\u003e\n \u003cli\u003eCutoff date: 05/05/2025.\u003c/li\u003e\n \u003cli\u003eExport the first 100 entries.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003e2.1.3. Selection Process\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA selection process is established according to the research objective, with the following key validation conditions:\u003c/p\u003e\n\u003cp\u003e\u0026middot; The article or document considers the relationship between AI and storytelling.\u003c/p\u003e\n\u003cp\u003e\u0026middot; It is relevant and up-to-date: published between 2020 and 2025 (limit 05/05/2025).\u003c/p\u003e\n\u003cp\u003e\u0026middot; It is scientific research indexed in global multidisciplinary databases.\u003c/p\u003e\n\u003cp\u003e\u0026middot; It is among the most cited in the 2020\u0026ndash;2025 period.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Only individual studies are included, not compilations or collective books (to avoid duplications).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.1.4. Evaluation of Study Quality\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality is ensured through:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e- \u003cstrong\u003eIndexing\u003c/strong\u003e in Web of Science or Scopus.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Peer review \u003cstrong\u003eand\u003c/strong\u003e validation by recognized scientific editorial committees (considering journal quality indices).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Journal membership in the top three quartiles of \u003cstrong\u003eWoS or Scopus\u003c/strong\u003e rankings.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.1.5. Data Extraction and Results Synthesis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor information extraction, a standardized database is created and the following are recorded: title, authors, journal, DOI, abstract, and number of citations. A document is recorded for each of the consulted platforms (WoS and Scopus). They can be consulted here (FILES\u0026gt; REFERENCES DATABASE):\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttps://osf.io/rwdvh/?view_only=449f8cef4ded49fea58acc42db1f1105\u003c/p\u003e\n\u003cp\u003eFor content analysis, Discourse Analysis is used, combining Grounded Theory with the Constant Comparative Method (CCM). The former involves several phases of data collection, refinement, and categorization (Kolb, 2012). The CCM develops coding systems that identify constructs and iteratively reviews assigned texts to ensure their representativeness (Olson et al., 2016). Their combination is useful for identifying emerging patterns and conceptual frameworks in a developing field, allowing for a rigorous qualitative synthesis of findings.\u003c/p\u003e\n\u003cp\u003eThe coding protocol is organized into four phases:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eInitial coding: reading and extraction of key data; annotation of relevant fragments with initial codes.\u003c/li\u003e\n \u003cli\u003eConstant comparison: contrast codes, identify patterns, adjust the codebook.\u003c/li\u003e\n \u003cli\u003eRefinement and categorization (axial coding): group codes and define relationships.\u003c/li\u003e\n \u003cli\u003eNarrative synthesis: present results in a codebook and write a narrative description; develop conclusions.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe coding work can be consulted at the following link (FILES\u0026gt;ATLAS TI CODIFICATION):\u003c/p\u003e\n\u003cp\u003ehttps://osf.io/rwdvh/?view_only=449f8cef4ded49fea58acc42db1f1105\u003c/p\u003e\n\u003cp\u003eThis protocol is implemented by three researchers with assigned tasks:\u003c/p\u003e\n\u003cp\u003eResearcher 1\u003c/p\u003e\n\u003cp\u003e1.1. Initial coding of articles 1\u0026ndash;18. Each researcher adds descriptive codes in Atlas.TI to define what they consider relevant in relation to the objective. Reflections generated during analysis are saved as memos.\u003c/p\u003e\n\u003cp\u003e1.2. Constant comparison of articles 19\u0026ndash;36 and joint discussion. The sample documents are exchanged between researchers 1 and 2, and the analysis is performed again, adding new codes and annotating reflections/comments through memos.\u003c/p\u003e\n\u003cp\u003e1.4. Joint discussion, final categorization, and narrative synthesis. At the end of the comparative exercise, researchers 1-2-3 meet to discuss and define the final groups of codes and memos. The groups will allow identifying causal and semantic relationships.\u003c/p\u003e\n\u003cp\u003e1.5. Researcher 1 compiles the results in summary tables.\u003c/p\u003e\n\u003cp\u003e1.6. Researcher 1 extracts preliminary conclusions based on the results. Narrative and causal relationships are established, developing a substantive theory about the discursive phenomenon. Researchers 2 and 3 review, comment/discuss, and contribute relevant reflections aimed at shaping the research conclusions and discussions.\u003c/p\u003e\n\u003cp\u003eResearcher 2\u003c/p\u003e\n\u003cp\u003e1.1. Initial coding of articles 19\u0026ndash;36.\u003c/p\u003e\n\u003cp\u003e1.2. Constant comparison of articles 1\u0026ndash;18 and joint discussion.\u003c/p\u003e\n\u003cp\u003e1.4. Joint discussion, final categorization, and narrative synthesis.\u003c/p\u003e\n\u003cp\u003e1.6. Researcher 1 extracts preliminary conclusions based on the results. Narrative and causal relationships are established, developing a substantive theory about the discursive phenomenon. Researchers 2 and 3 review, comment/discuss, and contribute relevant reflections aimed at shaping the research conclusions and discussions.\u003c/p\u003e\n\u003cp\u003eResearcher 3\u003c/p\u003e\n\u003cp\u003e1.3. Refinement. Fine-tune categories, codes, and theoretical concepts.\u003c/p\u003e\n\u003cp\u003e1.4. Joint discussion, final categorization, and narrative synthesis.\u003c/p\u003e\n\u003cp\u003e1.6. Researcher 1 extracts preliminary conclusions based on the results. Narrative and causal relationships are established, developing a substantive theory about the discursive phenomenon. Researchers 2 and 3 review, comment/discuss, and contribute relevant reflections aimed at shaping the research conclusions and discussions.\u003c/p\u003e\n\u003cp\u003eThe software used for analysis is Atlas.TI. According to Herbst et al. (2024), it contributes to preserving data integrity and generating scientific knowledge. It facilitates the identification of patterns and relationships through advanced query tools, supports collaborative work with version control, and allows for export and report creation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.1.6. PRISMA Flow Diagram and Sample\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBelow is the flow diagram (Fig.1) following the PRISMA 2020 protocol (Page et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe final composition of the sample can be consulted in the following databases (FILES\u0026gt;REFERENCES DATABASE):\u003c/p\u003e\n\u003cp\u003ehttps://osf.io/rwdvh/?view_only=449f8cef4ded49fea58acc42db1f1105\u003c/p\u003e\n\u003cp\u003eNo automation tools were used for any of the sampling phases. Nor for the coding and analysis of the sample.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe results are presented as a narrative synthesis based on the coding and categorization of the analysis. In brackets, the main codes that collect the category in the sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1. AI and Storytelling Evolution: Key Points\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.1.1. Performative AI and Embodiment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAI is shown as a performative and, occasionally, embodied and semi-intentional entity. AI systems are represented in narrative and social environments.\u003c/p\u003e\n\u003cp\u003eMain characteristics:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003ePhysical incarnation/embodiment [Physical impersonation, embodiment]: AI acquires a tangible presence, often through robots, voice-activated toys, or embedded interfaces, such as stuffed animals or mobile devices.\u003c/li\u003e\n \u003cli\u003eNarrative agency [Enacting diegesis keypoints, Characters interaction with users]: AI positions itself as a narrative actor capable of performing functions relevant to the story, triggering plot development, and modifying diegetic worlds.\u003c/li\u003e\n \u003cli\u003eSocial interaction and relational presence [Social embodiment, Interaction process]: Personification is not limited to appearance or function, but extends to the way AI interacts socially. These systems can be designed to \u0026quot;speak,\u0026quot; \u0026quot;respond,\u0026quot; and \u0026quot;relate\u0026quot; as if they were animated beings, integrated into a shared communicative space.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003e3.1.2. Automation and Procedural Narrative Generation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAI systems have the capacity to generate narratives through automated, procedural, and scalable processes.\u003c/p\u003e\n\u003cp\u003eMain characteristics:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eProcedural generation, multiplicity and variability [Real time variability / Procedural, Real time interaction, re-telling]: Narratives are not fixed. Stories can be regenerated, reinterpreted, or restructured dynamically.\u003c/li\u003e\n \u003cli\u003eProcedural adaptability [Real time interaction, Real time variability / Procedural, Interaction process]: Narratives can change in real time depending on user interaction, suggesting a form of procedural and reactive narration, rather than a fixed linear script.\u003c/li\u003e\n \u003cli\u003eScalability and replicability [Content generators, Story continuations, Infinite ramifications]: They can be designed for massive implementation, with the capacity to maintain open and branched structures with minimal human intervention. This could consolidate continuous narration or perpetual narration.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003e3.1.3 Multimodal, Crossmodal and Multilayer Storytelling (Real-time Change)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePotential for combined and simultaneous use of multiple media (text, image, audio, video, etc.) that interact with each other and integrate into overlapping narrative layers. These layers can be reorganized dynamically in real time according to interaction or context.\u003c/p\u003e\n\u003cp\u003eMain characteristics:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eMultimodality [Multimodality, visual storytelling, Verbal, Orality/Voice]: Capacity to process, generate, and combine different types of data\u0026mdash;such as text, image, audio, or video\u0026mdash;in a single architecture or model, allowing fluid interaction between modalities.\u003c/li\u003e\n \u003cli\u003eSemantic crossmodality [crossmodal telling]: Ability of a model to understand an input in one modality and generate a coherent output in another, establishing semantic links between different modes of representation.\u003c/li\u003e\n \u003cli\u003eMultilayer/multidimensional [Multidimensional: diegetic, Multidimensional: extradiegetic / multilayer]: Capacity to generate narratives in multiple interrelated layers, where each layer represents a distinct dimension of the story\u0026mdash;such as the main plot, subplots, emotional context, cultural references, or symbolic levels\u0026mdash;and can be activated, explored, or modified dynamically.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003e3.1.4. Data, Algorithmic Intervention and Adaptability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThese systems do not generate stories neutrally. They are programmed to operate within limits defined by data and algorithms that carry implicit intentions, patterns, and biases. There is algorithmic intention.\u003c/p\u003e\n\u003cp\u003eMain characteristics:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eData training and orientation [AI trained for specific uses, Data mining, Training ends]: Systems are based on previously defined data and training objectives, which limits their capacity to respond meaningfully in unknown or complex narrative contexts.\u003c/li\u003e\n \u003cli\u003eBiases and lack of transparency [Bias \u0026amp; misrepresentation, Lack of Transparency]: Algorithmic narration reproduces existing cultural prejudices and blind spots of dominant social systems. A reproduction of already existing prejudices occurs. An inability to capture contextual nuances in marginal narratives is observed.\u003c/li\u003e\n \u003cli\u003ePersonalization based on user profile [Profile adaptation, Personalization (audience multirole)]: Story content can be tailored to user profiles, adjusting language, pace, or content based on demographic or interactional data.\u003c/li\u003e\n \u003cli\u003eEmotional and experiential responsiveness [Feelings adaptation: mood, hedonic, emotion, Emotion RT adaptation]: AI narration can include detection of user reactions through real-time data (voice, gestures, emotional signals), enabling a reactive form of narration that incorporates affective and experiential layers.\u003c/li\u003e\n \u003cli\u003eGoal-driven algorithmic design [Goal Driven coding, edit, design customization]: AI proposals are deliberately shaped by design intentions, editing constraints, and possible customization of results.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2. AI and Storytelling: Emerging Forms of Creative Processes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNarrative creation is conceptualized primarily as a hybrid act, in which meaning, structure, and style are constructed jointly between human intention and artificial intelligence generation.\u003c/p\u003e\n\u003cp\u003eMain characteristics:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eProcess automation [Automation, efficiency]: Optimizes creative workflows, reducing the cognitive or procedural burden on human participants.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDialogic and interactive storytelling [Interaction process, Interactive, Conversation, Turns tale]: Narration is not a unidirectional act, but an exchange between AI and human participants, often shaped by turns, real-time inputs, and iterative responses.\u003c/li\u003e\n \u003cli\u003eGoal-based guidance and structure [Goal Driven coding/edit/design customization, Assisted agency]: Co-creation is determined by objectives, whether stylistic, thematic, or purposeful.\u003c/li\u003e\n \u003cli\u003eEnhanced creativity and expression [Enhancement, Multimodal creativity democratization, Automating processes]: The efficiency and capacity to improve users\u0026apos; natural expressive potential is highlighted. AI is equated to an instrument, employed in the narrative field, that enables, restructures, or accelerates creative processes.\u003c/li\u003e\n \u003cli\u003eDemocratized creative access [democratization/ multimodal creativity democratization]: Some multimodal AI tools facilitate access to generativity of forms and content for unqualified profiles or those without specific skills through simplified interaction models and interfaces.\u003c/li\u003e\n \u003cli\u003ePersistent AI gaps [Lack of semantic understanding]: AI limitations persist, such as its dependence on pattern prediction, its lack of deep contextual or cultural understanding in some cases.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3. Changing Roles of Audiences and Narrative Uses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePredominantly, a distinctive role of the audience in AI-driven narrative is described: from consumers to agents who collaborate in constructing interaction, story, and emotional process.\u003c/p\u003e\n\u003cp\u003eMain characteristics:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eNarrative independence of the audience [Real time interaction, Real time variability / Procedural, AI support or tool or assistant]: The user/audience has the capacity to construct, adapt, or even create their own narrative experience, beyond the control of the author or traditional narrative framework.\u003c/li\u003e\n \u003cli\u003eDisplacement and redistribution of authorial power [Deplacing (human as mediation)]: The boundary between author and audience becomes destabilized. As users and AI agents become co-authors, different authorial functions are redistributed.\u003c/li\u003e\n \u003cli\u003eMultirole, multi-identity [Multimodality, Real time variability, Real Time interaction]: In the same narrative, users can fulfill different roles and assume different identities, both within the diegetic process and in the creative process itself.\u003c/li\u003e\n \u003cli\u003eUser self-managed storytelling [Profile adaptation, Personalization (audience multirole)]: The user or recipient can be placed at the center of the story\u0026apos;s design and experience. It\u0026apos;s not just about telling a story, but about self-managing narrative conditions adapted to interests, emotions, decisions, and trajectories through their data.\u003c/li\u003e\n \u003cli\u003eAffective and emotional bonding [User attachment / Affectiveness, Likeability, AI fosters engagement]: Interaction is both functional and emotional. AI systems foster user attachment, creating empathetic and affective bonds.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.4. Authorship, Plagiarism and Synthetic Perspectives\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthorship tends to be reflected as a distributed assemblage between immediate design, algorithmic reproduction, and iterative refinement.\u003c/p\u003e\n\u003cp\u003eMain characteristics:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003ePrompt-based authorship [Prompting creation]: Content generation is triggered from elaborate instructions, which shifts authorship from mediated creation to strategic design of input and iteration instructions.\u003c/li\u003e\n \u003cli\u003ePlagiarized re-generated originality [Plagiarism]: The way LLMs (Large Language Models) work based on pre-existing patterns and data raises questions about originality, repetition, and possible accusations of algorithmic plagiarism.\u003c/li\u003e\n \u003cli\u003eDisplacement of the author figure [Deplacing (human as mediation)]: The human author sometimes appears as a mediator or subsidiary figure, raising questions about legitimacy, ownership, and authority in synthetic writing.\u003c/li\u003e\n \u003cli\u003eSynthetic perspective [Object + relationship + temporal dimension for synthetic understanding]: The synthetic perspective assumes greater prominence in the authorial process, ceasing to be assistive to become an agent.\u003c/li\u003e\n \u003cli\u003eSplit authorship [Author roles: creator, assistant, optimizer, reviewer, Opaque Authorship]: The author\u0026apos;s role is divided between the code that generates content, the trainer who shapes the model\u0026apos;s behavior, and the narrator who delivers the result. It is a stratified system of agents.\u003c/li\u003e\n \u003cli\u003eHumanness [Humanness]: A synthetic simulation of the human is pursued, becoming a qualification category for synthetic operations.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"4. Discussion and Conclusions","content":"\u003cp\u003eThis research delves into the evolution of storytelling under the influence of artificial intelligence in creative industries, through a systematic review of articles with the greatest scientific impact that address this topic.\u003c/p\u003e\u003cp\u003eThe results point to an initiatory stage within the area, where changes in AI technology itself are perceptible even within the five-year difference of the sample period. In Ghajargar, Bardzell \u0026amp; Lagerkvit (2022), there is direct reference to a \"lack of semantic understanding,\" while Li, Wang \u0026amp; Qu (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) already affirm that language models like ChatGPT can form linguistically precise associations taking into account relationships between words in a sentence.\u003c/p\u003e\u003cp\u003eBased on the criteria established for sample selection, AI adopts different forms in the researched articles, although there is a predominant focus on generative models, both linguistic and multimodal.\u003c/p\u003e\u003cp\u003eFrom the analyzed texts emerges a technophilic vision of AI's impact on narrative, in a line that presents certain continuity with previous academic currents that addressed recent technological developments early on. These techno-optimist discourses achieved a dominant character at moments like the creation of the Web, and the idea of decentralization and global participation (Berners-Lee \u0026amp; Fischetti, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Kelly, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), or emancipation through interaction between users and media co-creation (Jenkins et al, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Scolari, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe results identify what can be characterized as force vectors: distinct directional tendencies that define artificial intelligence's evolving influence on narrative.\u003c/p\u003e\u003cp\u003eAI is not presented solely as an algorithmic system. It incorporates a performative and agentic capacity within narrative systems, being able to acquire an embodied or materialized entity, especially through robots (Hubbard et al.,2021; Ligthart, Neerincx \u0026amp; Hindriks, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Elgarf et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This coincides with what Feng et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) collected, when affirming that this performativity extends, being able to perceive, reason, and interact with its environments. It is capable of automating diegetic and extradiegetic processes, offering procedural, replicable, and scalable narratives, with dynamic variations. Some authors have proposed models to develop some of these possibilities (Kumaran et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Buongiorno et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Technically, continuous narrative, or perpetual narrative, could be generated. The sample also alludes to multimodal, crossmodal, and multilayer potential, making specific mention of the option to change between modes and dimensions in real time. Text positions itself, again, as the main backbone of stories, but this time, above all, from the logic of the prompt (instruction) as trigger. In line with other studies, the prompt is assigned a nuclear value in AI generativity (Oppenlaender, Linder \u0026amp; Silvennoinen, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Park \u0026amp; Choo, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe creative process is cataloged primarily as a collaborative human-machine act (Bender, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Process automation occurs, with dialogic exchanges that transform the notion of unidirectional narrative act into an exchange between AI and human participants, shaped by turns, real-time inputs, and iterative work. The sample also emphasizes the idea of democratization (Pellas, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with access to multimodal generativity for unqualified profiles (O'Meara \u0026amp; Murphy, 2023), and that of augmented creativity, with improvement of users' expressive potential. This perspective finds detractors within academia. For Lee (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), AI dissociates creativity from human agency, under the protection of a conception of creative industries that dehumanizes the creative process by hiding working conditions and treating it as human capital generating IPs (intellectual properties). Runco (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), for his part, maintains that the processes used by generative artificial intelligence suggest discovery, not creativity.\u003c/p\u003e\u003cp\u003eIn any case, the creative process is not neutral, and is conditioned by the data and training objectives with which AI systems are built. This fact questions a supposed neutrality of narratives created with AI, with biases and lack of transparency. Above all, regarding minoritized or vulnerabilized groups underrepresented in data and training methods. This is one of the aspects to which the scientific community has paid most attention (Varona \u0026amp; Su\u0026aacute;rez, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hou, Tseng \u0026amp; Yuan, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hofmann, 2024).\u003c/p\u003e\u003cp\u003eThe invasiveness of these systems, along with the complicity of their users, can lead to absolute personalization of stories, attending to biographical and biometric data, allowing the creation of narratives with response capacity that detect user reactions in real time, modulating affective and experiential layers. In this sense, Gorenc (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) points to deep filtering of information that the user receives and measured influence on their decisions. He also verifies in his research that users dissociate between their self-perception of knowledge of AI-driven platforms and their biases, and their real behavior on such platforms.\u003c/p\u003e\u003cp\u003eThe audience framework shifts toward a notion closer to that of the user, in line with Bruns' (2008) produser. It points toward a path of narrative self-management, an independence or possession at will of the author figure, in collaboration with the machine, but this is not predominant in the sample. There is insistence on narrative co-creation (Li, Wang, Qu, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), in which the user is simultaneously the one who creates and the one who experiences the story. AI systems enable the real-time adoption of variable roles in narrative development as well as different diegetic identities. In the human-machine relationship, various authors mention the likeability, affectiveness, and engagement that these systems arouse, consolidating Reeves \u0026amp; Naas's (1996) argument about the human tendency to respond to media and technologies as if they were real human beings. It is noteworthy that one of the criteria for evaluating these systems is precisely that of humanness (Nichols, Gao, Gomez, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn authorship, the synthetic perspective assumes greater prominence, raising doubts about the originality and legitimacy of works, as posed by Fenwick \u0026amp; Jurcys (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The way Large Language Models work, with massive absorption of third-party data and pattern reproduction, poses creation rooted in massive plagiarism. In these systems, authorship is multiple: the data that gives rise to content, the trainer who shapes the model's behavior, the narrator who tells the story... More than a death of the author, in the Barthesian sense of the term (Barthes, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1968\u003c/span\u003e), we would find here a severed authorship, where co-authors have no connection to each other.\u003c/p\u003e\u003cp\u003eThe systematic review of scientific literature does not lead to the consolidation of a phenomenon that we could call AI Storytelling in the present, comparable to the acclaimed Transmedia storytelling (Jenkins, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Scolari, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) in academic circles. Nor does it establish new specific narrative formats, beyond the dynamic uses of ChatGPT, that are standardizable by cultural industries. It does offer us a series of keys to understand how artificial intelligence is affecting storytelling and what forces are beginning to take shape under the premise of joint narrative creation between humans and intelligent synthetic systems. More than a narrative evolution in a strictly technical or formal sense, what seems to be consolidating is an increasingly widespread tendency toward transhumanist horizons, as defined by Hayles (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Synthetic participation is gaining greater relevance, not only as an assistant, but, above all, as a multidimensional agent. It is capable of operating across multiple narrative levels\u0026mdash;diegetic, extradiegetic, hermeneutic, procedural, performative, and symbolic\u0026mdash;within various narrative environments, including physical reality itself. It represents an ontological mutation of the traditional narrative agent: AI both intervenes in and participates \u0026mdash;while remaining an object\u0026mdash; in the construction of human fictions, realities, and imaginaries.\u003c/p\u003e\u003cp\u003eThis research presents certain limitations. First, the sampling criteria exclude pre-2020 publications that may contain foundational contributions to the field. Second, the methodology focuses exclusively on academic sources, overlooking non-academic perspectives that could provide valuable insights into the phenomenon.\u003c/p\u003e\u003cp\u003eThe focus on creative processes of the sample analyzed in this research suggests, as a possible future line, the need to review specific literature on artificial intelligence and creativity. Likewise, to broaden perspectives, it would be revealing to delve into the phenomenon of AI narrative from the perspective of professionals and sector specialists, using both quantitative (surveys) and qualitative (in-depth interviews, focus groups) methodologies. Also, incorporating perspectives and agencies of minoritized and vulnerabilized groups, affected by the reproduction of artificial intelligence prejudices and biases, through PAR (Participatory Action Research) and ABR (Arts Based Research) methodologies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization: ISLData curation: AGCFormal Analysis: ISL, AGCFunding Acquisition: ISLInvestigation: ISL, AGC, ARMethodology: ISL, AGCProject administration: ARResources: ARSoftware: ISL, AGCSupervision: ARValidation: ARVisualization: ISL, AGCWriting \u0026ndash; original draft: ISL, AGCWriting \u0026ndash; review and editing: AR\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThis research is funded by the Ram\u0026oacute;n y Cajal Grant from the Ministry of Science, Innovation and Universities/State Research Agency of Spain.Grant RYC2023-044777-I, funded by MICIU/AEI/10.13039/501100011033 and by ESF+.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eProtocol and methodology of this study have been registered in the Open Science Framework Platform:https://osf.io/dbjh9/?view_only=9211b80cc5eb440caecf2d2074bc2846Methodology, PRISMA Documents, Sample Databases, Atlas.TI coding file (FILES) have been deposited in the Open Science Framework Platform:https://osf.io/rwdvh/?view_only=449f8cef4ded49fea58acc42db1f1105\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAarseth E (1997) Cybertext: Perspectives on ergodic literature. Johns Hopkins University\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlexander B (2017) The new digital storytelling: Creating narratives with new media, 2nd edn. Praeger\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmato G et al (2019) AI in the media and creative industries. arXiv. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/arxiv.1905.04175\u003c/span\u003e\u003cspan address=\"10.48550/arxiv.1905.04175\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBal M (2017) Narratology: Introduction to the theory of narrative, 4th edn. University of Toronto\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarandiaran XE, P\u0026eacute;rez-Verdugo M (2025) Generative midtended cognition and Artificial Intelligence: thinging with thinging things. Synthese 205(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11229-025-04961-4\u003c/span\u003e\u003cspan address=\"10.1007/s11229-025-04961-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarthes R (1968) La mort de l\u0026rsquo;auteur. Manteia\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBelda-Medina J, Goddard C (2024) AI-driven digital storytelling: A strategy for creating English as a foreign language learning environments. Int J Linguistics Stud 4(1):40\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.32996/ijls.2024.4.1.4\u003c/span\u003e\u003cspan address=\"10.32996/ijls.2024.4.1.4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBell A, Ensslin A, Rustad H (2014) Analysing digital fiction. Routledge\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBender EM, Gebru T, McMillan-Major A, Shmitchell S (2021) On the dangers of stochastic parrots: Can language models be too big? In: FAccT \u0026rsquo;21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, pp 610\u0026ndash;623. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3442188.3445922\u003c/span\u003e\u003cspan address=\"10.1145/3442188.3445922\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBender SM (2023) Coexistence and creativity: screen media education in the age of artificial intelligence content generators. Media Pract Educ 24(4):351\u0026ndash;366. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/25741136.2023.2204203\u003c/span\u003e\u003cspan address=\"10.1080/25741136.2023.2204203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerners-Lee T, Fischetti M (2000) Weaving the web: The original design and ultimate destiny of the World Wide Web by its inventor. Harper San Francisco\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBouchardon S, F\u0026uuml;l\u0026ouml;p E (2021) Digital Narrative and Temporality. In: Mitchell A, Vosmeer M (eds) Interactive Storytelling. ICIDS 2021. Lecture Notes in Computer Science, vol 13138. Springer, Cham. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-92300-6_29\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-92300-6_29\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBruns A (2008) Blogs, Wikipedia, Second Life, and beyond: From production to produsage. Peter Lang, New York\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuongiorno S, Klinkert LJ, Chawla T, Zhuang Z, Clark C (2024) PANGeA: Procedural artificial narrative using generative AI for turn-based video games. arXiv. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arxiv.org/abs/2404.19721\u003c/span\u003e\u003cspan address=\"https://arxiv.org/abs/2404.19721\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen G (2024) The impacts of AI-driven storytelling applications on language acquisition and literacy development in early childhood education: A systematic review. Asian J Adv Res Rep 18(11):1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.9734/ajarr/2024/v18i11770\u003c/span\u003e\u003cspan address=\"10.9734/ajarr/2024/v18i11770\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavis J, Mengersen K, Bennett S, Mazerolle L (2014) Viewing systematic reviews and meta-analysis in social research through different lenses. SpringerPlus 3:511. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/2193-1801-3-511\u003c/span\u003e\u003cspan address=\"10.1186/2193-1801-3-511\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElgarf M, Zojaji S, Skantze G, Peters C (2022) CreativeBot: A creative storyteller robot to stimulate creativity in children. In: Proceedings of the 2022 International Conference on Multimodal Interaction (ICMI \u0026rsquo;22). Association for Computing Machinery, pp 540\u0026ndash;548. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3536221.3556578\u003c/span\u003e\u003cspan address=\"10.1145/3536221.3556578\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang X, Ng DTK, Leung JKL, Chu SKW (2023) A systematic review of artificial intelligence technologies used for story writing. Educ Inform Technol 28:14361\u0026ndash;14397. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10639-023-11741-5\u003c/span\u003e\u003cspan address=\"10.1007/s10639-023-11741-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeng Z et al (2025) Multi-agent embodied AI: Advances and future directions. arXiv. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arxiv.org/abs/2505.05108\u003c/span\u003e\u003cspan address=\"https://arxiv.org/abs/2505.05108\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFenwick M, Jurcys P (2023) Originality and the future of copyright in an age of generative AI. Comput Law Secur Rev 51:105892. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.clsr.2023.105892\u003c/span\u003e\u003cspan address=\"10.1016/j.clsr.2023.105892\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhajargar M, Bardzell J, Lagerkvist L (2022) A redhead walks into a bar: Experiences of writing fiction with artificial intelligence. In: Proceedings of the 25th International Academic Mindtrek Conference. Association for Computing Machinery, pp 1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3569219.3569418\u003c/span\u003e\u003cspan address=\"10.1145/3569219.3569418\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGorenc N (2025) AI embedded bias on social platforms. Int Rev Sociol 1\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/03906701.2025.2489056\u003c/span\u003e\u003cspan address=\"10.1080/03906701.2025.2489056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarahap K, Muhtar S, Sakarina S, Shofwani SA (2025) Digital narratives and AI in modern media: A systematic literature review. Tech Fusion in Business and Society. Springer, pp 197\u0026ndash;205. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-031-84636-6_16\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-84636-6_16\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHayles NK (1999) How we became posthuman: Virtual bodies in cybernetics, literature, and informatics. University of Chicago Press\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerbst RS, Frizzarini ST, Herbst GM (2024) ATLAS.ti\u0026reg; in qualitative research: Expanding horizons in oral history analysis. Interconnections Knowledge: Multidisciplinary Approaches 974\u0026ndash;994. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.56238/SEVENED2024.010-062\u003c/span\u003e\u003cspan address=\"10.56238/SEVENED2024.010-062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHofmann V, Kalluri PR, Jurafsky D, Goel S (2024) AI generates covertly racist decisions about people based on their dialect. Nature 633:147\u0026ndash;154. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-024-07856-5\u003c/span\u003e\u003cspan address=\"10.1038/s41586-024-07856-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHou T-Y, Tseng Y-C, Yuan CW, T. (2024) Is this AI sexist? The effects of a biased AI\u0026rsquo;s anthropomorphic appearance and explainability on users\u0026rsquo; bias perceptions and trust. Int J Inf Manag 76:102775. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijinfomgt.2024.102775\u003c/span\u003e\u003cspan address=\"10.1016/j.ijinfomgt.2024.102775\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHubbard LJ, Chen Y, Colunga E, Kim P, Yeh T (2021) Child-robot interaction to integrate reflective storytelling into creative play. In: Proceedings of the 13th Conference on Creativity and Cognition (C\u0026amp;C \u0026rsquo;21). Association for Computing Machinery, Article 16, pp 1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3450741.3465254\u003c/span\u003e\u003cspan address=\"10.1145/3450741.3465254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJenkins H (2006) Convergence culture: Where old and new media collide. NYU\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJenkins H, Ford S, Green J (2013) Spreadable media: Creating value and meaning in a networked culture. NYU\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJenkins H, Shresthova S, Gamber-Thompson L, Kligler-Vilenchik N, Zimmerman AM (2015) By any media necessary: The new youth activism. NYU\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKelly K (1998) New rules for the new economy: 10 radical strategies for a connected world. Viking\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoenitz H, Gjedde L, Haahr M, Sezen D, Ferri G (eds) (2023) Understanding interactive digital narratives: Immersive expressions for a complex time. Routledge\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKolb SM (2012) Grounded theory and the constant comparative method: Valid research strategies for educators. J Emerg Trends Educational Res Policy Stud 3(1):83\u0026ndash;86\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumaran V, Rowe J, Mott B, Lester J (2023) SceneCraft: Automating interactive narrative scene generation in digital games with large language models. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 19(1):86\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1609/aiide.v19i1.27504\u003c/span\u003e\u003cspan address=\"10.1609/aiide.v19i1.27504\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLandow GP (2009) Hypertext 3.0: Critical theory and new media in an era of globalization. Johns Hopkins University\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee H-K (2022) Rethinking creativity: creative industries, AI and everyday creativity. Media Cult Soc 44(3):601\u0026ndash;612. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/01634437221077009\u003c/span\u003e\u003cspan address=\"10.1177/01634437221077009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi H, Wang Y, Qu H (2024) Where are we so far? Understanding data storytelling tools from the perspective of human-AI collaboration. In: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI \u0026rsquo;24). Association for Computing Machinery, Article 845, pp 1\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3613904.3642726\u003c/span\u003e\u003cspan address=\"10.1145/3613904.3642726\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLigthart MEU, Neerincx MA, Hindriks KV (2020) Design patterns for an interactive storytelling robot to support children's engagement and agency. In: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI \u0026rsquo;20). Association for Computing Machinery, pp 409\u0026ndash;418. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3319502.3374826\u003c/span\u003e\u003cspan address=\"10.1145/3319502.3374826\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLynch CJ, Jensen EJ, Zamponi V, O\u0026rsquo;Brien K, Frydenlund E, Gore R (2023) A structured narrative prompt for prompting narratives from large language models: Sentiment assessment of ChatGPT-generated narratives and real tweets. Future Internet 15(12):375. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/fi15120375\u003c/span\u003e\u003cspan address=\"10.3390/fi15120375\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-Mondejar J (2024) Sin relato: Atrofia de la capacidad narrativa y crisis de la subjetividad. Anagrama\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManovich L (2001) The language of new media. MIT Press\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Meara J, Murphy C (2023) Aberrant AI creations: co-creating surrealist body horror using the DALL-E Mini text-to-image generator. Convergence 29(4):1070\u0026ndash;1096. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/13548565231185865\u003c/span\u003e\u003cspan address=\"10.1177/13548565231185865\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNichols E, Gao L, Gomez R (2020) Collaborative storytelling with large-scale neural language models. In: Proceedings of the 13th ACM SIGGRAPH Conference on Motion, Interaction and Games (MIG \u0026rsquo;20). Association for Computing Machinery, Article 17, pp 1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3424636.3426903\u003c/span\u003e\u003cspan address=\"10.1145/3424636.3426903\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlson JD, McAllister C, Grinnell LD, Gehrke Walters K, Appunn F (2016) Applying constant comparative method with multiple investigators and inter-coder reliability. Qualitative Rep 21(1):26\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.46743/2160-3715/2016.2447\u003c/span\u003e\u003cspan address=\"10.46743/2160-3715/2016.2447\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOppenlaender J, Linder R, Silvennoinen J (2024) Prompting AI art: An investigation into the creative skill of prompt engineering. Int J Human\u0026ndash;Computer Interact. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10447318.2024.2431761\u003c/span\u003e\u003cspan address=\"10.1080/10447318.2024.2431761\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePage MJ et al (2021) The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 372:n71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmj.n71\u003c/span\u003e\u003cspan address=\"10.1136/bmj.n71\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark J, Choo S (2024) Generative AI prompt engineering for educators: Practical strategies. J Special Educ Technol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/01626434241298954\u003c/span\u003e\u003cspan address=\"10.1177/01626434241298954\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePellas N (2023) The effects of generative AI platforms on undergraduates\u0026rsquo; narrative intelligence and writing self-efficacy. Educ Sci 13(11):1155. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/educsci13111155\u003c/span\u003e\u003cspan address=\"10.3390/educsci13111155\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReeves B, Nass CI (1996) The media equation: How people treat computers, television, and new media like real people and places. Center for the Study of Language and Information; Cambridge University Press\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRobin B (2016) The power of digital storytelling to support teaching and learning. Digit Educ Rev 30:17\u0026ndash;29\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuiz PG, Dom\u0026iacute;nguez-Lloria S (2024) La inteligencia artificial generativa de im\u0026aacute;genes como colaboradora para la creatividad: un estudio de caso grupal. Arte Individuo y Sociedad. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5209/aris.99030\u003c/span\u003e\u003cspan address=\"10.5209/aris.99030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRunco MA (2024) The discovery and innovation of AI does not qualify as creativity. J Cogn Psychol 1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/20445911.2024.2436362\u003c/span\u003e\u003cspan address=\"10.1080/20445911.2024.2436362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRyan M-L (2015) Narrative as virtual reality 2: Revisiting immersion and interactivity in literature and electronic media. Johns Hopkins University\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchulz L, Patr\u0026iacute;cio M, Odijk D (2024) Narrative information theory. arXiv. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arxiv.org/abs/2411.12907\u003c/span\u003e\u003cspan address=\"https://arxiv.org/abs/2411.12907\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScolari CA (2013) Narrativas transmedia: Cuando todos los medios cuentan. Deusto\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSonni AF, Hafied H, Irwanto I, Latuheru R (2024) Digital newsroom transformation: A systematic review of the impact of artificial intelligence on journalistic practices, news narratives, and ethical challenges. Journalism Media 5(4):1554\u0026ndash;1570. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/journalmedia5040097\u003c/span\u003e\u003cspan address=\"10.3390/journalmedia5040097\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTerranova T (2013) Free labor. In: Scholz T (ed) Digital labor: The Internet as playground and factory. Routledge, pp 33\u0026ndash;57\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVaccari C, Chadwick A (2020) Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Social Media + Soc 6(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2056305120903408\u003c/span\u003e\u003cspan address=\"10.1177/2056305120903408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVarona D, Su\u0026aacute;rez JL (2022) Discrimination, bias, fairness, and trustworthy AI. Appl Sci 12(12):5826. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app12125826\u003c/span\u003e\u003cspan address=\"10.3390/app12125826\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVidrih M, Mayahi S (2023) Generative AI-driven storytelling: A new era for marketing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/arXiv.2309.09048\u003c/span\u003e\u003cspan address=\"10.48550/arXiv.2309.09048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. arXiv\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalsh M (2010) Multimodal literacy: What does it mean for classroom practice? Australian J Lang Lit 33(3):211\u0026ndash;239. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF03651836\u003c/span\u003e\u003cspan address=\"10.1007/BF03651836\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan Z, Yang C, Liang Q, Chen XA (2023) XCreation: A graph-based crossmodal generative creativity support tool. In: Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST \u0026rsquo;23). Association for Computing Machinery, Article 48, pp 1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3586183.3606826\u003c/span\u003e\u003cspan address=\"10.1145/3586183.3606826\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhai X, Nyaaba M, Ma W (2024) Can generative AI and ChatGPT outperform humans on cognitive-demanding problem-solving tasks in science? Sci Educ. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11191-024-00496-1\u003c/span\u003e\u003cspan address=\"10.1007/s11191-024-00496-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Z, Xu Y, Wang Y, Yao B, Ritchie D, Wu T, Yu M, Wang D, Li TJ-J (2022) StoryBuddy: A human-AI collaborative chatbot for parent-child interactive storytelling with flexible parental involvement. In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI \u0026rsquo;22). Association for Computing Machinery, Article 218, pp 1\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3491102.3517479\u003c/span\u003e\u003cspan address=\"10.1145/3491102.3517479\" targettype=\"DOI\" 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":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7318246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7318246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial Intelligence is reshaping narrative forms and content across the creative industries, transforming how stories are conceived, produced, distributed, and experienced. This study examines the evolution of storytelling under the influence of AI through a systematic review of high-impact scientific literature. The methodology follows the PRISMA 2020 protocol, combining Discourse Analysis and Grounded Theory for synthesizing results. The sample comprises the most cited articles indexed in Web of Science and Scopus between 2020 and 2025. Findings reveal a predominantly technophilic perspective in current research. The analysis identifies a comprehensive narrative transformation articulated through four key shifts. First, Hybrid Authorship: New collaborative dynamics arise between human creators and generative systems via prompting and automation. Traditional unidirectional storytelling evolves into dialogic human\u0026ndash;machine co-creation. Second, Procedural Narratives: AI systems produce adaptive, real-time narratives that adjust according to user interaction and contextual inputs, enabling pervasive storytelling across digital and physical environments. Third: Distributed Authorship and Algorithmic Intentionality: Content generation grounded in large-scale pattern replication challenges conventional notions of originality and ownership. Authorship becomes distributed across datasets, training algorithms, and human prompts, introducing a model of split creative responsibility. Fourth, AI-Augmented Audiences: Users gain technical capacities for narrative self-management, dynamically adopting multiple roles within both the diegetic and creative spheres. These interactions foster emotional and empathetic bonds with AI systems, raising indicators of user dependency. In conclusion, according to the most impactful literature, synthetic narrative participation is emerging as a multidimensional agent of unprecedented relevance in the creative industries. By operating across diegetic, performative, and emotional dimensions\u0026mdash;and acquiring embodied forms through robots and interfaces\u0026mdash;AI positions itself not merely as a tool, but as a narrative and social agent. This phenomenon marks a fundamental ontological shift: AI intervenes and participates \u0026mdash;while remaining an object\u0026mdash; in the construction of human fictions, realities and imaginaries.\u003c/p\u003e","manuscriptTitle":"AI storytelling \u0026amp; narrative evolution in creative industries: a Systematic Review.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-13 13:10:32","doi":"10.21203/rs.3.rs-7318246/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f50fff4b-5154-48e0-adaa-a9c1c767adc9","owner":[],"postedDate":"October 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56129367,"name":"Humanities/Complex networks"},{"id":56129368,"name":"Social science/Complex networks"},{"id":56129369,"name":"Physical sciences/Mathematics and computing"},{"id":56129370,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-02-12T09:43:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-13 13:10:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7318246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7318246","identity":"rs-7318246","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}