Human AI Collaborative Subject Design and Assessment Workflow

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While existing studies largely focus on tool adoption and user perceptions, there remains limited empirical evidence on how instructors systematically interact with generative artificial intelligence during authentic course design, teaching, and assessment processes. This study addresses this gap by proposing and empirically examining the Human-AI Collaborative Subject Design and Assessment Workflow, a human-centered instructional framework that structures collaboration between instructors and generative artificial intelligence across the full instructional lifecycle. Grounded in Design Science Research, the framework was designed, enacted, and evaluated through full-semester implementation in an undergraduate university course. Empirical evidence was derived from authentic instructional artifacts, including syllabi, lecture materials, laboratory activities, and assessments, as well as documented human–AI interaction processes. The results demonstrate that generative artificial intelligence can effectively support drafting and structuring tasks while human instructors retain exclusive control over pedagogical validation, laboratory design, assessment finalization, and grading. The findings confirm the feasibility of workflow-based, human-in-the-loop integration under real teaching constraints, showing that instructional scalability and efficiency can be achieved without compromising academic authority, ethical governance, or pedagogical quality. This study contributes a replicable, instructor-enacted model for responsible generative artificial intelligence adoption and advances process-level governance approaches for higher education teaching practice. Generative artificial intelligence Human-in-the-loop Instructional workflow Course design and assessment Higher education Design Science Research Academic governance Ethical AI integration Figures Figure 1 Figure 2 Figure 3 1. Introduction 1.1 Background In this study, AI refers specifically to Generative Artificial Intelligence (GenAI) used for instructional content generation, assessment design, and teaching support. GenAI is increasingly adopted in higher education to enhance instructional efficiency and teacher productivity (Cordero et al., 2025 ). However, instructional workload has concurrently increased due to expanded curricula, compressed preparation timelines, and frequent syllabus revisions, substantially intensifying instructors’ time demands and administrative burdens (Kaden, 2020 ; Rousseau et al., 2018 ; Sawaya et al., 2021 ). Alongside these developments, rising concerns have been reported regarding uncontrolled GenAI use, academic integrity risks, and the erosion of instructors’ pedagogical authority, as growing reliance on GenAI systems may undermine educator autonomy and ethical oversight (Küçükuncular & Ertugan, 2025 ; Tsao, 2025 ). Despite the widespread adoption of GenAI in education, empirical evidence examining instructors’ real-world interactions with GenAI in course design, teaching, and assessment remains limited. This gap underscores the need for structured, ethical, and human-centered frameworks that ensure transparency, human oversight, accountability, and academic governance (Seo et al., 2021 ; Shneiderman, 2020 ; Tong et al., 2025 ). 1.2 Research Problem Despite the growing adoption of GenAI in education, empirical evidence examining how instructors interact with GenAI during authentic course design, teaching, and assessment remains limited (Avci et al., 2025 ). This gap highlights a growing need for structured, ethical, and human-centered frameworks to guide GenAI use in higher education and to ensure transparency, human oversight, accountability, and academic governance (Zawacki-Richter, 2019; Peláez, 2025). In the absence of process-level frameworks that explicitly define human–GenAI roles, instructional workflows often become fragmented, resulting in unclear accountability—particularly in lecture preparation and assessment design (Bühler et al., 2025 ; Du et al., 2024 ; Olaniyan et al., 2025 ). Moreover, there is an increasing risk of GenAI over-automation in educational workflows, where AI-supported processes may bypass curriculum approval, pedagogical validation, and institutional control. Such practices can weaken academic oversight, diminish instructor autonomy, and undermine professional judgment and accountability (Kabashkin, 2025 ; Rind, 2026 ). At the same time, there remains a lack of empirically grounded GenAI-supported instructional models that adequately reflect real teaching constraints, including full-semester course delivery, limited preparation time, and sustained instructional workload experienced by instructors in authentic teaching contexts (Fey et al., 2020 ; Galimova et al., 2025). These challenges contribute to an ongoing tension between instructional scalability and academic integrity, as increasing reliance on technology-mediated and automated teaching practices may conflict with ethical standards, weaken assessment credibility, and heighten risks of unethical behavior and reduced trust in higher education (Amzalag et al., 2022 ; Davis, 2023 ). 1.3 Research Questions Grounded in the need for structured, ethical, and human-centered frameworks that clarify instructor–GenAI roles in authentic higher-education teaching contexts, this study is guided by the following research questions (RQs): RQ1: How can instructors systematically integrate GenAI into course design and assessment processes while retaining academic authority, pedagogical control, and institutional accountability? RQ2: What human–GenAI interaction patterns emerge when a structured instructional workflow is enacted across a full-semester university course under real teaching constraints? RQ3: How does a human-in-the-loop (HITL) instructional workflow support syllabus alignment, pedagogical quality, and ethical GenAI use while balancing instructional scalability and academic integrity? 1.4 Proposed Framework To address gaps in process-level guidance, empirical grounding, and ethical governance of GenAI use in higher education, this study proposes the Human–AI Collaborative Subject Design and Assessment Workflow (HACSDAW). The framework spans the full instructional lifecycle—from syllabus structuring and instructional content development to assessment finalization and evaluation—and systematically structures human–GenAI collaboration by confining GenAI to supportive drafting and organizational tasks, while reserving pedagogical validation, laboratory activities, and grading as exclusive human responsibilities (Abuhassna & Alnawajha, 2023 ; Griffin, 2020 ; Ketonen et al., 2023 ). The implementation and evaluation of the framework are detailed in the Methodology section. HACSDAW explicitly distinguishes GenAI-assisted drafting from human-exclusive instructional decision-making, positions GenAI as a constrained instructional assistant rather than an autonomous agent, and is empirically validated through real-course implementation rather than simulated or hypothetical scenarios. 1.5 Novelty of the Study The novelty of this study lies in its process-oriented and instructor-centered approach to GenAI integration in higher education. Specifically, this study: Introduces a workflow-based, HITL framework that explicitly structures instructor–GenAI interaction across the full instructional lifecycle Moves beyond tool-centric GenAI adoption studies by proposing a process-centric pedagogical model grounded in real teaching practices Demonstrates just-in-time GenAI-supported teaching preparation under authentic constraints, including full-semester delivery and limited preparation time Provides artifact-based empirical evidence derived from real instructional outputs, including syllabi, lecture slides, laboratory activities, and assessments Operationalizes ethical and governance boundaries through enforceable workflow stages that clearly separate GenAI-assisted drafting from human-exclusive validation, instruction, and grading 1.6 Contribution to the Literature This study contributes to the existing literature in several important ways: Extends GenAI-in-education research beyond student use and content generation to instructor-led course design and assessment workflows Contributes a replicable and transferable instructional framework that can be enacted by instructors across disciplines and institutional contexts Clarifies the central role of human judgment, validation, and accountability within GenAI-supported instructional processes Bridges scholarship across educational technology, instructional design, and responsible GenAI governance, addressing calls for structured, ethical, and human-centered GenAI integration 1.7 Aim and Objectives of the Study The aim of this study is to design and demonstrate a human-centered, GenAI-supported instructional workflow that enables scalable course design and assessment while preserving academic authority, pedagogical quality, and ethical governance. To achieve this aim, the study pursues the following objectives: To design the HACSDAW framework for structured and transparent human–GenAI collaboration in instructional design and assessment To implement HACSDAW in a real university course delivered over a full academic semester To analyze human–GenAI interaction patterns across key instructional stages, including syllabus development, content preparation, and assessment design To evaluate syllabus alignment, ethical GenAI use, and instructional efficiency under authentic teaching constraints To provide empirically grounded guidance for responsible GenAI adoption in higher education teaching practice 2. Literature Review 2.1 GenAI in Higher Education Teaching and Instructional Design Recent studies report a rapid increase in educators’ use of GenAI to assist with lesson planning, instructional content creation, learning activity design, assessment preparation, and feedback generation. Within this context, GenAI is commonly framed as a productivity-enhancing instructional assistant that supports teaching efficiency rather than as a replacement for instructors’ pedagogical decision-making and professional judgment (Moundridou et al., 2024 ). Existing research on GenAI adoption in education is predominantly tool-centric, emphasizing software usability, technical features, and perceived usefulness of digital tools. Consequently, broader pedagogical processes—such as coordinated instructional workflows, collaborative human–GenAI interaction, and systematic information-management practices across teaching stages—receive comparatively limited scholarly attention (Ahankoob et al., 2025 ). Educators’ beliefs, attitudes, and perceived control over GenAI systems play a decisive role in shaping whether GenAI is adopted as an augmentative instructional aid or positioned as an automated substitute. This perception-driven framing directly affects pedagogical authority, accountability, and responsibility, with automation-oriented adoption posing potential risks to instructor autonomy and professional judgment (Tsung-Chi, 2024). While existing GenAI-supported educational frameworks often address isolated instructional functions or focus on technically end-to-end, student-facing system deployment, they provide limited process-level guidance for instructors. In particular, there is a lack of coherent, end-to-end instructional workflows that span syllabus design, lecture development, and assessment preparation, and that explicitly incorporate pedagogical validation, ethical oversight, and academic governance across the full teaching lifecycle (Nouraei Yeganeh et al., 2025 ; Sajja et al., 2025 ). 2.2 HITL and Human-Centered GenAI for Teaching HITL approaches are widely recognized as a foundational governance mechanism for ethical and responsible GenAI use in higher education. By embedding continuous human oversight, validation, and accountability across the GenAI lifecycle, HITL frameworks help mitigate ethical risks, prevent over-automation, and ensure transparency, institutional accountability, and alignment with human values and pedagogical intent (Chen et al., 2023 ). Within educational contexts, a critical distinction is drawn between GenAI augmentation and GenAI automation. Augmentation positions GenAI as a supportive instructional aid that enhances human expertise and pedagogical judgment, whereas automation frames GenAI as a substitute for human agency, potentially displacing instructor authority and professional responsibility. These competing sociotechnical imaginaries significantly shape educational policy, institutional governance, and teaching practice, influencing how GenAI systems are designed, adopted, and regulated in higher education (Rahm, 2021 ). Consistent with a human-centered perspective, prior studies emphasize review, refinement, and validation as non-transferable instructor responsibilities in GenAI-supported teaching. While GenAI systems can assist with drafting instructional materials and generating preliminary ideas, instructors retain primary responsibility for critically evaluating GenAI outputs, refining instructional content, and validating pedagogical accuracy, curricular alignment, and ethical appropriateness to ensure instructional quality, academic integrity, and accountable teaching practice (Kohan et al., 2023 ). 2.3 GenAI, Assessment Design, and Academic Integrity GenAI has placed assessment at the center of ongoing debates in higher education, positioning it as the most sensitive instructional domain affected by GenAI integration. The use of GenAI challenges established notions of assessment validity, academic integrity, and fairness, prompting calls for fundamental rethinking of assessment objectives, methods, and evidence of learning. In particular, concerns related to cheating, inappropriate automation, and the reliable evaluation of higher-order cognitive skills have intensified as GenAI systems increasingly support content generation and problem solving (Nguyen et al., 2025 ). In response, prior research has emphasized a shift toward authentic, higher-order, and applied assessment approaches that prioritize performance-based, problem-solving, and real-world tasks over rote memorization and examination-oriented formats. Such approaches are viewed as more capable of capturing meaningful learning outcomes and mitigating some limitations of traditional assessment systems in GenAI-augmented learning environments (Wilson & Narasuman, 2020 ). Despite these conceptual advances, there remains a persistent lack of process-level guidance on how instructors should integrate GenAI into assessment design while retaining pedagogical control and academic authority. Existing AI-in-education studies and institutional initiatives frequently articulate ethical principles and technical affordances but offer limited operational direction for instructors on how to use GenAI in assessment design in ways that preserve professional judgment, accountability, and academic integrity (Chang, 2023). 2.4 Teacher Competencies and Responsible GenAI Use Effective and responsible integration of GenAI in higher education requires educator competencies that extend well beyond technical tool usage. Prior research emphasizes that instructors must develop capabilities related to ethical judgment, pedagogical decision-making, and institutional governance in order to ensure that GenAI-supported teaching practices remain accountable, transparent, and educationally sound (Stenberdt et al., 2023). Without such competencies, there is an increased risk of over-reliance on GenAI systems and insufficient critical evaluation of GenAI-generated outputs. The growing tendency to delegate ethical reasoning and decision-making to expert systems or abstract ethical guidelines may create epistemic blind spots, diminish reflective professional judgment, and weaken human accountability when GenAI outputs are accepted without sustained critical scrutiny (Hedlund, 2024 ). In response to these challenges, recent studies highlight the value of workflow-based guidance and institutional frameworks that explicitly structure human– GenAI collaboration. Such workflows help maintain instructor accountability by clearly delineating human responsibility, decision authority, and validation roles across instructional processes, thereby preserving pedagogical control, assessment integrity, and ethical GenAI use even as GenAI assistance becomes more prevalent in higher education (Chiu, 2024 ; Wang et al., 2025). 2.5 Identified Gap and Positioning of HACSDAW The existing literature on GenAI in higher education offers extensive policy guidance, conceptual discussions, and perception-based analyses, with a strong emphasis on ethical principles, instructor attitudes, and tool-specific applications. However, it provides limited empirically grounded evidence demonstrating how instructors systematically enact human– GenAI collaboration across the full instructional lifecycle in authentic teaching contexts. In particular, there is a lack of instructor-centered, end-to-end instructional workflows that operationalize ethical governance, clarify human and GenAI roles, and preserve academic authority, pedagogical validation, and institutional accountability under real teaching constraints. More specifically, prior research reveals the absence of instructional workflows that: Begin with human-led knowledge curation, in which instructors independently select, interpret, and validate disciplinary content and learning objectives before any GenAI involvement, thereby anchoring GenAI use in established pedagogical intent and curriculum governance; Systematically structure syllabus design and weekly instructional content, where GenAI supports organization and drafting in alignment with approved syllabi, while instructors retain explicit responsibility for review, refinement, and formal approval; Support just-in-time lecture preparation under authentic constraints, including full-semester course delivery, sustained instructional workload, and limited preparation time, which are largely absent from experimental or simulated GenAI-in-education studies; Formalize GenAI-assisted assessment drafting while enforcing human exclusivity in validation and evaluation, explicitly reserving assessment approval, laboratory activities, grading, and evaluative judgment as non-transferable instructor responsibilities. To address these gaps, this study positions the HACSDAW as a process-centric, HITL instructional framework that governs GenAI use through clearly defined workflow stages, role boundaries, and validation checkpoints. Rather than proposing abstract ethical principles or isolated tool adoption strategies, HACSDAW provides replicable, instructor-enacted guidance demonstrating how GenAI can be systematically integrated into syllabus design, instructional content development, and assessment preparation without compromising pedagogical quality, academic integrity, or instructor accountability. Through real-course implementation across a full academic semester, HACSDAW responds directly to the literature’s call for structured, ethical, and empirically grounded models of responsible GenAI use in higher education teaching practice. 3. Methodology 3.1 Research Design This study adopts a design-oriented, practice-based research design grounded in Design Science Research (DSR), with the primary aim of developing, enacting, and evaluating an instructional workflow for human–GenAI collaboration in authentic higher-education teaching contexts. Consistent with DSR principles, the study focuses on transforming conceptual and ethical considerations into an actionable instructional artifact—namely, the HACSDAW framework—and examining its enactment under real teaching conditions rather than controlled or simulated environments. The research design emphasizes iterative refinement through practice, where the proposed workflow is applied across a full academic semester and continuously validated through instructor-led decision-making, review, and reflection. Rather than evaluating GenAI systems based on algorithmic performance or output accuracy, the study prioritizes human–GenAI interaction patterns, governance mechanisms, and accountability structures, recognizing that transparency, pedagogical control, and ethical oversight are more critical than technical optimization in educational contexts (Abedin et al., 2022 ; Klenk, 2024 ; Steel et al., 2020 ; Tuure et al., 2024). Empirical evidence is derived from authentic instructional artifacts and systematically documented human–GenAI interactions produced during the real-world implementation of HACSDAW in an undergraduate university course. These artifacts—including syllabi, lecture materials, laboratory activities, and assessment instruments—serve as concrete evidence of how the workflow operates in practice and how human authority, validation, and responsibility are maintained across the instructional lifecycle. 3.2 HACSDAW Framework as Methodological Instrument HACSDAW is a seven-stage as Fig. 1 , HITL instructional workflow that governs GenAI use in course design, content preparation, and assessment, ensuring scalability while preserving academic authority, pedagogical rigor, and ethical control. Stage 1: Human Knowledge Curation Instructors select and validate authoritative textbooks, define content scope and rigor, and establish boundaries for subsequent GenAI use. Stage 2: GenAI-Assisted Syllabus Design GenAI supports syllabus structuring and sequencing, while instructors review, refine, and formally approve alignment with CLOs, PLOs, and institutional policies. Stage 3: GenAI-Assisted Content Structuring GenAI organizes weekly learning content mapped to textbooks; instructors control instructional emphasis, sequencing, and cognitive level. Stage 4: GenAI-Supported Lecture Drafting GenAI generates draft lecture slides based on validated inputs; instructors review, edit, and finalize materials for classroom delivery. Stage 5: Human-Designed Laboratories Instructors exclusively design hands-on laboratory activities aligned with tools, skills, and learning outcomes. Stage 6: GenAI-Assisted Assessment Drafting GenAI drafts assessment components; instructors refine and validate difficulty, fairness, alignment, and ethical suitability. Stage 7: Human Assessment Finalization Instructors exclusively finalize assessments, conduct grading and feedback, and enforce academic integrity and institutional regulations. The HACSDAW framework enforces a clear separation between GenAI-assisted and human-exclusive stages across the instructional workflow. GenAI is deliberately constrained to drafting and structuring functions, while human instructors retain full responsibility for knowledge curation, pedagogical validation, practical instruction, assessment finalization, and evaluation. Human validation is embedded at every GenAI-assisted stage, ensuring that academic authority, assessment judgment, and ethical accountability remain exclusively human-controlled throughout the course lifecycle. 3.3 Case Study Context and Data Sources The study was conducted within a single undergraduate Computer Security course delivered over one academic semester. The instructor held full responsibility for syllabus design, instructional content preparation, laboratory activities, and assessment. The course was implemented under authentic institutional constraints, including formal syllabus approval processes, a fixed teaching schedule, sustained instructional workload, limited preparation time, and compliance with institutional academic policies. Data sources consisted of authentic instructional artifacts and process documentation produced through the enactment of the HACSDAW framework across the full instructional lifecycle. Instructional artifacts included an approved course syllabus, weekly lecture slides, human-designed laboratory activities, and assessment instruments (quizzes, midterm, and final examinations), along with revision traces documenting iterative refinement throughout the semester. In addition, process evidence captured documented human–GenAI interactions, including explicit records of where GenAI was applied for drafting or structuring tasks and where instructors validated, modified, or rejected GenAI outputs. Together, these data sources provided empirical evidence of the framework’s enactment under real teaching conditions. 3.4 Implementation Procedure and Ethical Safeguards The HACSDAW framework was implemented sequentially across the academic semester through the enactment of its seven defined stages. GenAI assistance was applied only at designated stages, with explicit human validation enforced at every GenAI-assisted step to ensure pedagogical accuracy, ethical compliance, and instructor authority throughout the instructional process. Ethical safeguards were embedded within the implementation procedure. GenAI was explicitly excluded from grading, evaluation, and final academic judgment. GenAI use was transparently documented, and ethical constraints were incorporated into assessment design to preserve academic integrity, fairness, and institutional accountability. 3.5 Methodological Scope and Limitations This study is based on a single-course, single-instructor case, which limits statistical generalization. The methodological emphasis is on demonstrating feasibility, enactment, and governance of the proposed workflow rather than evaluating causal effects on learning outcomes. Replication across courses, instructors, and institutional contexts is identified as a direction for future research. 4. Results 4.1 Evidence of HACSDAW Enactment The results demonstrate the successful enactment of all seven stages of the HACSDAW framework across a full academic semester. Each stage was implemented sequentially as designed, from human-led knowledge curation to human-exclusive assessment finalization. GenAI support was consistently confined to designated drafting and structuring activities, while human validation was enforced at every GenAI-assisted stage. The implementation confirms that the workflow is operationally feasible under authentic instructional conditions and institutional constraints. Stage 1: Human Knowledge Curation Instructors independently selected and validated authoritative textbooks, defined course scope and rigor, and established pedagogical boundaries for subsequent GenAI use. Evidence: Primary textbooks and institutionally approved syllabus reference documents. Stage 2: GenAI-Assisted Syllabus Design GenAI supported the initial structuring and sequencing of syllabus components. Instructors reviewed, refined, and formally approved the syllabus to ensure alignment with course learning outcomes (CLOs), program learning outcomes (PLOs), and institutional policies. Evidence: Institutionally approved course syllabus. Stage 3: GenAI-Assisted Content Structuring GenAI was used to organize weekly instructional content mapped to validated textbooks. Instructors controlled instructional emphasis, sequencing, and cognitive level. Evidence: Weekly learning-content guidelines aligned with the approved syllabus. Stage 4: GenAI-Supported Lecture Drafting GenAI generated draft lecture slides based on validated inputs. Instructors reviewed, edited, and finalized all lecture materials prior to classroom delivery. Evidence: Finalized lecture slide decks. Stage 5: Human-Designed Laboratories All laboratory activities were designed exclusively by the instructor to ensure alignment with tools, skills, and learning outcomes. No GenAI involvement was observed at this stage. Evidence: Embedded laboratory activities integrated within weekly instructional materials. Stage 6: GenAI-Assisted Assessment Drafting GenAI supported the drafting of assessment structures. Instructors refined and validated assessment difficulty, fairness, syllabus alignment, and ethical suitability. Evidence: Draft assessment structures. Stage 7: Human Assessment Finalization Final assessments were exclusively finalized by the instructor. Grading, feedback, and enforcement of academic integrity were conducted without GenAI involvement. Evidence: Finalized midterm and final examination instruments. Collectively, these artifacts provide concrete evidence that HACSDAW was enacted as designed across the full instructional lifecycle. The results confirm that a structured, HITL workflow can be operationalized under authentic teaching constraints while maintaining clear separation between GenAI-assisted and human-exclusive instructional responsibilities. 4.2 Human–GenAI Interaction Patterns Analysis of instructional artifacts and process documentation revealed a consistent draft–review–approval interaction pattern between instructors and GenAI. GenAI outputs primarily functioned as preliminary drafts, which were subsequently reviewed, modified, or rejected by the instructor to ensure pedagogical accuracy, syllabus alignment, and ethical suitability. Human-exclusive control was strictly maintained for laboratory design, assessment finalization, grading, and evaluative judgment, reinforcing clear role boundaries and instructor authority throughout the instructional process. Table 1 summarizes the observed distribution of GenAI-assisted and human-exclusive roles across key instructional tasks. Table 1 Human–GenAI Interaction Roles Across Instructional Tasks Task GenAI Draft Human Verify/Execute Syllabus draft ✓ ✓ Lecture slides ✓ ✓ Lab activities x ✓ Midterm exam (questions/projects) ✓ ✓ Final exam (questions/projects) ✓ ✓ Grading (coursework, mid & final) x ✓ 4.3 Instructional Efficiency Under Real Constraints The enactment of HACSDAW supported just-in-time instructional preparation under realistic workload and time constraints associated with full-semester course delivery. GenAI-assisted drafting enabled the timely development of lecture materials and assessment drafts, while mandatory human validation ensured consistency with course objectives, approved syllabus structures, and institutional requirements. Analysis of weekly instructional artifacts and revision traces indicates that instructional materials were produced and refined on schedule throughout the semester, despite limited preparation time and sustained instructional workload. GenAI support reduced the time required for initial drafting and content organization, allowing instructors to focus effort on pedagogical validation, contextual adaptation, and quality assurance. Observed workflow execution demonstrates that instructional efficiency was improved without compromising pedagogical alignment, content quality, or institutional compliance. Across all stages, human approval remained a required checkpoint prior to classroom use, assessment deployment, or evaluative judgment. Figure 2 illustrates the observed instructional artifact production pipeline, showing the sequential flow from human-curated knowledge sources through GenAI-assisted drafting and human-approved instructional outputs across the full instructional lifecycle. 4.4 Assessment Design Outcomes Results indicate that GenAI effectively supported the initial drafting of assessment components, including question formulation and structural organization. However, all assessment-related decisions concerning difficulty level, alignment with learning outcomes, fairness, and ethical appropriateness remained under exclusive human control. Final assessment instruments reflected consistent alignment with course learning outcomes and institutional assessment standards, demonstrating that AI support did not bypass pedagogical validation or academic governance. Figure 3 illustrates the enforced task-level permission boundaries governing GenAI use during assessment design, highlighting that GenAI was limited to drafting support while all evaluative, grading, and instructional decisions remained exclusively under human control. 5. Discussion 5.1 Interpretation of Findings The findings confirm the practical viability of HITL GenAI integration when governed through a clearly defined instructional workflow. The successful enactment of all seven HACSDAW stages across a full academic semester demonstrates that GenAI can be systematically integrated into course design and assessment processes without diminishing instructor authority, pedagogical judgment, or ethical control. Rather than operating as an autonomous or decision-making system, GenAI functioned strictly as a constrained instructional assistant, supporting drafting and structuring tasks while remaining subject to continuous human validation. Human instructors consistently exercised final authority over pedagogical decisions, laboratory design, assessment approval, and grading. These findings indicate that instructional scalability and efficiency can be achieved without compromising academic governance, provided that GenAI use is embedded within enforceable workflow boundaries rather than ad hoc or tool-driven practices. 5.2 Alignment with Existing Literature The results extend existing instructor-centered GenAI research by moving beyond perception-based, policy-oriented, and tool-centric studies toward a process-oriented, enactment-based perspective. While prior literature emphasizes ethical principles, instructor attitudes, and governance frameworks, it often lacks operational clarity on how these principles are enacted during everyday teaching activities. HACSDAW contributes to this gap by operationalizing ethical and governance principles into explicit workflow stages, role separations, and validation checkpoints. Through artifact-based evidence, the study demonstrates how transparency, accountability, and human oversight can be embedded directly into instructional processes, rather than relying solely on institutional policies or abstract ethical guidelines. In this way, the findings provide empirical support for calls in the literature for human-centered, instructor-governed GenAI integration grounded in authentic teaching practice. 5.3 Pedagogical Implications From a pedagogical standpoint, the findings suggest that structured GenAI support can meaningfully assist instructors in managing instructional workload and time pressure under real teaching constraints. GenAI-assisted drafting reduced the effort required for initial content and assessment preparation, enabling instructors to allocate greater attention to pedagogical validation, contextual adaptation, and instructional quality assurance. By explicitly reserving syllabus approval, laboratory design, assessment finalization, and grading as human-exclusive responsibilities, HACSDAW preserves pedagogical coherence, syllabus alignment, and assessment integrity. The results indicate that instructional scalability does not necessitate pedagogical automation; instead, it can be achieved through carefully governed human–GenAI collaboration that reinforces, rather than replaces, professional judgment. 5.4 Institutional and Policy Implications At the institutional level, the findings highlight the limitations of purely prohibitive, tool-restrictive, or compliance-focused GenAI policies. Such approaches may fail to reflect the realities of instructional practice and can inadvertently encourage unregulated or opaque GenAI use. The results suggest that workflow-based governance models, such as HACSDAW, offer a more effective and sustainable alternative. By embedding ethical safeguards, role boundaries, and validation checkpoints within routine teaching workflows, institutions can support responsible GenAI adoption while maintaining academic standards, accountability, and instructor autonomy. Rather than framing GenAI governance as external regulation, the findings demonstrate the value of process-level governance enacted by instructors in practice. 5.5 Addressing the Research Questions This study explicitly addresses all three research questions through the design and real-world enactment of the HACSDAW framework. RQ1 is addressed by demonstrating how GenAI can be systematically integrated into course design and assessment through a structured workflow that enforces human validation and preserves instructor authority and institutional accountability. RQ2 is addressed through the identification of consistent human–GenAI interaction patterns, characterized by a draft–review–approval cycle across all GenAI-assisted stages. RQ3 is addressed by evidence showing that the HITL workflow supports syllabus alignment, pedagogical quality, and ethical GenAI use while enabling instructional efficiency under authentic teaching constraints. 5.6 Limitations and Future Research This study is limited by its focus on a single course and a single instructor, which constrains statistical generalization. The primary emphasis is on demonstrating feasibility, enactment, and governance, rather than measuring causal effects on student learning outcomes. Future research should replicate the framework across multiple courses, disciplines, instructors, and institutional contexts to examine transferability and adaptability. Comparative and longitudinal studies may further explore how workflow-based GenAI governance influences instructional sustainability, institutional policy development, and long-term pedagogical practice as GenAI adoption continues to expand in higher education. 6. Conclusion 6.1 Summary of the Study This study proposed and empirically examined the HACSDAW as a human-centered, GenAI-supported instructional framework. Grounded in DSR, the framework was designed, enacted, and evaluated through full-semester implementation in an authentic undergraduate course. The study focused on demonstrating the feasibility, enactment, and governance of structured human–GenAI collaboration across the instructional lifecycle rather than evaluating causal effects on student learning outcomes. 6.2 Key Contributions This study makes three primary contributions: First, it introduces a workflow-based, instructor-enacted framework that explicitly structures human–GenAI roles across syllabus design, instructional content development, and assessment preparation. Second, it provides empirical, artifact-based evidence derived from real instructional outputs—such as syllabi, lecture materials, laboratory activities, and assessments—demonstrating how GenAI can support instructional processes without undermining academic authority or ethical control. Third, the study operationalizes responsible GenAI governance by embedding enforceable role boundaries and human validation checkpoints directly into instructional workflows, moving beyond abstract ethical guidelines and tool-centric adoption models. 6.3 Practical Significance From a practical perspective, HACSDAW offers instructors and institutions a replicable and realistic approach for integrating GenAI into higher education teaching under authentic workload and time constraints. By confining GenAI to drafting and structuring tasks while reserving pedagogical validation, laboratory design, and grading as human-exclusive responsibilities, the framework supports instructional efficiency and scalability without compromising syllabus alignment, assessment integrity, or professional judgment. As such, HACSDAW provides actionable guidance for responsible GenAI adoption that aligns with everyday teaching practice rather than exceptional or experimental conditions. 6.4 Final Remarks As GenAI adoption continues to expand in higher education, the central challenge is no longer whether GenAI should be used, but how it should be governed in practice. This study demonstrates that structured, HITL instructional workflows offer a viable pathway for responsible GenAI integration, enabling instructors to harness GenAI’s support while preserving academic authority, ethical accountability, and pedagogical quality. By shifting the focus from tools and policies to workflow-level governance enacted in real teaching contexts, HACSDAW contributes a practical foundation for sustainable and human-centered GenAI use in higher education. Declarations Ethical approval This study involved normal educational practices conducted within a university course and did not include the collection of personal, identifiable, or sensitive student data. According to the institutional research ethics guidelines of Lincoln University College, studies focusing on curriculum design and instructional workflow evaluation that do not involve identifiable participant data are considered minimal-risk educational research and therefore do not require formal Institutional Review Board (IRB) approval. The research was conducted in accordance with relevant institutional ethical guidelines and the principles of the Declaration of Helsinki. Consent to participate Students participated in the course as part of normal academic instruction. Informed consent to participate in the study was obtained from participants involved in the instructional context. Consent to publish Not applicable. Competing interests The authors declare that they have no competing interests Authors' information BIOGRAPHIES OF AUTHORS Mony Ho is a Ph.D. candidate in Information Technology at Lincoln University College, Malaysia, and holds a Master’s degree in IT and Data Science from the European International University, France, and Master Trainer from the Republic of Korea. He serves as a master trainer at the Ministry of Labor and Vocational Training and lectures part-time at several universities in Cambodia. His teaching and research interests include data science and cybersecurity.He can be contacted at [email protected] ; https://orcid.org/0009-0004-3389-1951 Sokroeurn Ang is a PhD candidate in cybersecurity at Lincoln University College, Malaysia, MSC in cybersecurity at University of London, UK, Micro master in cybersecurity at RIT, USA, Certified with CISSP, CISA, CISM, ECSA, CEH, CyberOps, CCNASec, CCNA, and AWS cloud Practitioner. He can be contacted at [email protected] ; https://orcid.org/0009-0000-9746-5469 Sopheatra Huy is a Ph.D. candidate in Information Technology at Lincoln University College, Malaysia, and holds an M.Sc. in IT from the Royal University of Phnom Penh. With over a decade of part-time lecturing experience, he has taught programming, software engineering, and IT project management. He currently serves as Senior Manager of IT Audit at WB Finance, with prior roles at Phillip Bank and PRASAC MFI. His research interests include IT automation, cybersecurity, and audit technologies. He can be contacted at [email protected] ; https://orcid.org/0009-0006-7383-1667 Dr. Midhunchakkaravarthy Janarthanan is the Dean of the School of AI Computing and Multimedia at Lincoln University College, Malaysia. He holds a Ph.D. in Computer Science and has published widely in Big Data, Web Text Mining, Machine Learning, and GPU Computing, with over 1,000 Google Scholar citations. He also supervises postgraduate research in AI and cloud-based systems. He can be contacted at [email protected] ; https://orcid.org/0000-0002-0107-885X References Abedin B, Meske C, Junglas I, Rabhi F, Motahari-Nezhad HR. Designing and managing human–AI interactions. Inform Syst Front. 2022;24:691–7. 10.1007/s10796-022-10313-1 . Abuhassna H, Alnawajha S. Instructional design made easy! Instructional design models, categories, frameworks, educational context, and recommendations for future work. 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Du S, Hou L, Zhang G, Tan Y, Mao P. BIM and IFC Data Readiness for AI Integration in the Construction Industry: A Review Approach. Buildings. 2024;14(10). 10.3390/buildings14103305 . Article 3305. Fey SB, Theus ME, Ramirez AR. Course-based undergraduate research experiences in a remote setting: Two case studies documenting implementation and student perceptions. Ecol Evol. 2020;10(22):12528–41. 10.1002/ece3.6916 . Galimova Elvira G, et al. Mobile learning in science education to improve higher-order thinking skills and communication skills: scoping review. Front Communication. 2025;10. 10.3389/fcomm.2025.1624012 . Griffin TM. Centering graduate students’ research projects in data management education: A pilot program. J Librariansh Sch Communication. 2020;8(1). 10.7710/2162-3309.2365 . Article eP2365. Hedlund M. Ethicisation and reliance on ethics expertise. Res Publica. 2024;30:87–105. 10.1007/s11158-023-09592-5 . Kabashkin I. Cognitive Atrophy Paradox of AI–Human Interaction: From Cognitive Growth and Atrophy to Balance. Information. 2025;16(11):1009. 10.3390/info16111009 . Kaden U. (2020). COVID-19 school closure-related changes to the professional life of a K–12 teacher. Education Sciences, 10 (6), Article 165. 10.3390/educsci10060165 Ketonen L, Lehtinen A, Koskinen P. Assessment designs of instructional labs: A literature review and a design model. Phys Rev Phys Educ Res. 2023;19:020601. 10.1103/PhysRevPhysEducRes.19.020601 . Klenk M. Ethics of generative AI and manipulation: A design-oriented research agenda. Ethics Inf Technol. 2024;26., Article 9. 10.1007/s10676-024-09745-x . Kohan M, Changiz T, Yamani N. A systematic review of faculty development programs based on the Harden teacher’s role framework model. BMC Med Educ. 2023;23., Article 910. 10.1186/s12909-023-04863-4 . Küçükuncular A, Ertugan A. (2025). Teaching in the AI era: Sustainable digital education through ethical integration and teacher empowerment. Sustainability, 17 (16), Article 7405. 10.3390/su17167405 Moundridou M, et al. Generative AI tools as educators’ assistants: Designing and implementing inquiry-based lesson plans. Computers Education: Artif Intell. 2024;7., Article 100277. 10.1016/j.caeai.2024.100277 . Nguyen A, Duong AT, Nguyen DTB, Lai VTT, Dang B. Guidelines for learning design and assessment for generative artificial intelligence-integrated education: A unified view. Inform Learn Sci. 2025;126(7–8):491–512. 10.1108/ILS-11-2024-0148 . Nouraei Yeganeh L, Chen Y, Fenty NS, Simpson A, Hatami M. STREAM: A semantic transformation and real-time educational adaptation multimodal framework in personalized virtual classrooms. Future Internet. 2025;17(12). 10.3390/fi17120564 . Article 564. Olaniyan D, Olaniyan J, Obagbuwa IC, Tsetse AK. Explainable AI framework for automated lesson plan generation and alignment with Bloom’s taxonomy. Computers. 2025;14(11):494. 10.3390/computers14110494 . Peláez CA, Solano A, Ospina JA, Espinosa et al. (2025). Toolkit for inclusion of user experience design guidelines in the development of assistants based on generative artificial intelligence. Informatics, 12 (1), Article 10. 10.3390/informatics12010010 Rahm L. Education, automation and AI: A genealogy of alternative futures. Learn Media Technol. 2021;48(1):6–24. 10.1080/17439884.2021.1977948 . Rind IA. Conceptualizing the Impact of AI on Teacher Knowledge and Expertise: A Cognitive Load Perspective. Educ Sci. 2026;16(1):57. 10.3390/educsci16010057 . Rousseau M, Könings KD, Touchie C. Overcoming the barriers of teaching physical examination at the bedside: More than just curriculum design. BMC Med Educ. 2018;18., Article 302. 10.1186/s12909-018-1403-z . Sajja R, Sermet Y, Demir I. End-to-end deployment of the educational AI hub for personalized learning and engagement: A case study on environmental science education. IEEE Access. 2025;13:55169–86. 10.1109/ACCESS.2025.3554222 . Sawaya RD, Mrad S, Rajha E, Saleh R, Rice J. Simulation-based curriculum development: Lessons learnt in global health education. BMC Med Educ. 2021;21., Article 33. 10.1186/s12909-020-02430-9 . Seo K, Tang J, Roll I, Fels S, Yoon D. The impact of artificial intelligence on learner–instructor interaction in online learning. Int J Educational Technol High Educ. 2021;18:54. 10.1186/s41239-021-00292-9 . Shneiderman B. Human-centered artificial intelligence: Reliable, safe & trustworthy. Int J Human–Computer Interact. 2020;36(6):495–504. 10.1080/10447318.2020.1741118 . Steel A, Peng W, Sibbritt D, Adams J. Introducing national practice-based research networks: An overview to inform future practice-based research. Sci Rep. 2020;10., Article 846. 10.1038/s41598-020-57918-7 . Stenberdt VA, Makransky G. Mastery experiences in immersive virtual reality promote pro-environmental waste-sorting behavior. Comput Educ. 2023;198., Article 104760. 10.1016/j.compedu.2023.104760 . Tong A, Zainol Z, Chong TS, Renganathan K. AI governance on young consumers in higher education: A content analysis of policies for generative AI. Young Consumers. 2025;26(5):865–85. 10.1108/YC-10-2024-2303 . Tsao J. Trajectories of AI policy in higher education: Interpretations, discourses, and enactments of students and teachers. Computers Education: Artif Intell. 2025;9., Article 100496. 10.1016/j.caeai.2025.100496 . Tsung-Chi L, et al. Perception and Action Augmentation for Teleoperation Assistance in Freeform Telemanipulation. J Hum -Robot Interact. 2024;13., 1, Article 11. 10.1145/3643804 . Tuure T, Robert W, Jan. Dealing with Complexity in Design Science Research: A Methodology Using Design Echelons. MIS Q. 2024;48(2):427–58. 10.25300/MISQ/2023/16700 . Wang SI-C, Liu EZ-F. AI tools and inquiry-based instructional workflows in higher education: Exploring human–AI collaboration through the Predict–Observe–Explain model. Computers Education: Artif Intell. 2025;9:100488. 10.1016/j.caeai.2025.100488 . Wilson DM, Narasuman S. Investigating teachers’ implementation and strategies on higher order thinking skills in school-based assessment instruments. Asian J Univ Educ. 2020;16(1):71–84. 10.24191/ajue.v16i1.8991 . Zawacki-Richter O, Marín VI, Bond M, Gouverneur F. Systematic review of research on artificial intelligence applications in higher education – Where are the educators? Int J Educational Technol High Educ. 2019;16., Article 39. 10.1186/s41239-019-0171-0 . Additional Declarations No competing interests reported. Supplementary Files HACSDAWFiles.zip Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 03 Apr, 2026 Editor assigned by journal 13 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 11 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8998746","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619875582,"identity":"78bcbe06-49d4-4b2a-b604-b3e224bfafdf","order_by":0,"name":"Mony Ho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYDACCSBmbGAGMRgPMDDYyLCBRB+AiAOEtYDUpPGAtSSQoOUwDwMhLbqzmw8wF+6wtucHMg783HOeh0+6+eGDxB0Mcnw3Elg382BqMbtzLIF55pn0xBlAxsGeZ7d52GSOGRsknmEwlryRwHYbm5YbOQbMvG2HEwwkcgwO8BwAapHIYZNIbGNI3EBAiz1Iy8E/B87BtdQT0sK4AajlMM+BA3AtCQY4taQlHJ7ZBvQLiCFzIBmoJQ3olzYJw5lnHrbdnINNS/LBx4VtwBCbkXzw4ZsDdnLyM5IfPvjYZiPPdzz52I032AP6MDZBSHwxYXEYCDBjFwYBxh+45UbBKBgFo2DEAAAoA22TIrYoJwAAAABJRU5ErkJggg==","orcid":"","institution":"Lincoln University College","correspondingAuthor":true,"prefix":"","firstName":"Mony","middleName":"","lastName":"Ho","suffix":""},{"id":619875583,"identity":"2ade714f-6280-4fd9-8c6f-fde2656b56c9","order_by":1,"name":"Sokroeurn Ang","email":"","orcid":"","institution":"Lincoln University College","correspondingAuthor":false,"prefix":"","firstName":"Sokroeurn","middleName":"","lastName":"Ang","suffix":""},{"id":619875584,"identity":"45d00b56-bac6-45f3-a1a7-7e2b98ffc264","order_by":2,"name":"Sopheatra Huy","email":"","orcid":"","institution":"Lincoln University College","correspondingAuthor":false,"prefix":"","firstName":"Sopheatra","middleName":"","lastName":"Huy","suffix":""},{"id":619875585,"identity":"c645e1db-5541-4174-875b-0cc98eef11ad","order_by":3,"name":"Midhunchakkaravarthy Janarthanan","email":"","orcid":"","institution":"Lincoln University College","correspondingAuthor":false,"prefix":"","firstName":"Midhunchakkaravarthy","middleName":"","lastName":"Janarthanan","suffix":""}],"badges":[],"createdAt":"2026-03-01 02:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8998746/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8998746/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106546515,"identity":"2d5e1fb5-b420-4533-ab11-7415e310d661","added_by":"auto","created_at":"2026-04-09 17:03:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64182,"visible":true,"origin":"","legend":"\u003cp\u003eHACSDAW: Human–GenAI Collaborative Subject Design and Assessment Workflow\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8998746/v1/73782a566cf797cccdee5d58.png"},{"id":106724912,"identity":"e59f44fb-d743-4e82-ba0d-151be5af7ca5","added_by":"auto","created_at":"2026-04-12 18:30:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33795,"visible":true,"origin":"","legend":"\u003cp\u003eInstructional Artifact Production Pipeline under the HACSDAW Framework\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8998746/v1/242a40627d117760f37d663a.png"},{"id":106546516,"identity":"ae018f9a-bc46-479e-b583-2befe793056c","added_by":"auto","created_at":"2026-04-09 17:03:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40578,"visible":true,"origin":"","legend":"\u003cp\u003eTask-Level GenAI Permission Boundaries in Assessment Design\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8998746/v1/295435e68a4f44f5d8e604d7.png"},{"id":106726693,"identity":"7b5133fb-65f1-4357-85fc-e0d6fcaf5304","added_by":"auto","created_at":"2026-04-12 18:37:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1296925,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8998746/v1/d2486a70-9fc7-4914-b3e2-b1da64f65c34.pdf"},{"id":106546514,"identity":"db38335f-6422-4f09-9d74-c1a1bb64338e","added_by":"auto","created_at":"2026-04-09 17:03:54","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":22447344,"visible":true,"origin":"","legend":"","description":"","filename":"HACSDAWFiles.zip","url":"https://assets-eu.researchsquare.com/files/rs-8998746/v1/c416894b534b4bf7179c5a56.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Human AI Collaborative Subject Design and Assessment Workflow","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Background\u003c/h2\u003e \u003cp\u003eIn this study, AI refers specifically to Generative Artificial Intelligence (GenAI) used for instructional content generation, assessment design, and teaching support. GenAI is increasingly adopted in higher education to enhance instructional efficiency and teacher productivity (Cordero et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, instructional workload has concurrently increased due to expanded curricula, compressed preparation timelines, and frequent syllabus revisions, substantially intensifying instructors\u0026rsquo; time demands and administrative burdens (Kaden, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rousseau et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sawaya et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Alongside these developments, rising concerns have been reported regarding uncontrolled GenAI use, academic integrity risks, and the erosion of instructors\u0026rsquo; pedagogical authority, as growing reliance on GenAI systems may undermine educator autonomy and ethical oversight (K\u0026uuml;\u0026ccedil;\u0026uuml;kuncular \u0026amp; Ertugan, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tsao, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Despite the widespread adoption of GenAI in education, empirical evidence examining instructors\u0026rsquo; real-world interactions with GenAI in course design, teaching, and assessment remains limited. This gap underscores the need for structured, ethical, and human-centered frameworks that ensure transparency, human oversight, accountability, and academic governance (Seo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shneiderman, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tong et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Research Problem\u003c/h2\u003e \u003cp\u003eDespite the growing adoption of GenAI in education, empirical evidence examining how instructors interact with GenAI during authentic course design, teaching, and assessment remains limited (Avci et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This gap highlights a growing need for structured, ethical, and human-centered frameworks to guide GenAI use in higher education and to ensure transparency, human oversight, accountability, and academic governance (Zawacki-Richter, 2019; Pel\u0026aacute;ez, 2025). In the absence of process-level frameworks that explicitly define human\u0026ndash;GenAI roles, instructional workflows often become fragmented, resulting in unclear accountability\u0026mdash;particularly in lecture preparation and assessment design (B\u0026uuml;hler et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Du et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Olaniyan et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, there is an increasing risk of GenAI over-automation in educational workflows, where AI-supported processes may bypass curriculum approval, pedagogical validation, and institutional control. Such practices can weaken academic oversight, diminish instructor autonomy, and undermine professional judgment and accountability (Kabashkin, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Rind, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). At the same time, there remains a lack of empirically grounded GenAI-supported instructional models that adequately reflect real teaching constraints, including full-semester course delivery, limited preparation time, and sustained instructional workload experienced by instructors in authentic teaching contexts (Fey et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Galimova et al., 2025).\u003c/p\u003e \u003cp\u003eThese challenges contribute to an ongoing tension between instructional scalability and academic integrity, as increasing reliance on technology-mediated and automated teaching practices may conflict with ethical standards, weaken assessment credibility, and heighten risks of unethical behavior and reduced trust in higher education (Amzalag et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Davis, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Research Questions\u003c/h2\u003e \u003cp\u003eGrounded in the need for structured, ethical, and human-centered frameworks that clarify instructor\u0026ndash;GenAI roles in authentic higher-education teaching contexts, this study is guided by the following research questions (RQs):\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eRQ1: How can instructors systematically integrate GenAI into course design and assessment processes while retaining academic authority, pedagogical control, and institutional accountability?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRQ2: What human\u0026ndash;GenAI interaction patterns emerge when a structured instructional workflow is enacted across a full-semester university course under real teaching constraints?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRQ3: How does a human-in-the-loop (HITL) instructional workflow support syllabus alignment, pedagogical quality, and ethical GenAI use while balancing instructional scalability and academic integrity?\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Proposed Framework\u003c/h2\u003e \u003cp\u003eTo address gaps in process-level guidance, empirical grounding, and ethical governance of GenAI use in higher education, this study proposes the Human\u0026ndash;AI Collaborative Subject Design and Assessment Workflow (HACSDAW). The framework spans the full instructional lifecycle\u0026mdash;from syllabus structuring and instructional content development to assessment finalization and evaluation\u0026mdash;and systematically structures human\u0026ndash;GenAI collaboration by confining GenAI to supportive drafting and organizational tasks, while reserving pedagogical validation, laboratory activities, and grading as exclusive human responsibilities (Abuhassna \u0026amp; Alnawajha, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Griffin, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ketonen et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The implementation and evaluation of the framework are detailed in the Methodology section. HACSDAW explicitly distinguishes GenAI-assisted drafting from human-exclusive instructional decision-making, positions GenAI as a constrained instructional assistant rather than an autonomous agent, and is empirically validated through real-course implementation rather than simulated or hypothetical scenarios.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.5 Novelty of the Study\u003c/h2\u003e \u003cp\u003eThe novelty of this study lies in its process-oriented and instructor-centered approach to GenAI integration in higher education. Specifically, this study:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIntroduces a workflow-based, HITL framework that explicitly structures instructor\u0026ndash;GenAI interaction across the full instructional lifecycle\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMoves beyond tool-centric GenAI adoption studies by proposing a process-centric pedagogical model grounded in real teaching practices\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDemonstrates just-in-time GenAI-supported teaching preparation under authentic constraints, including full-semester delivery and limited preparation time\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProvides artifact-based empirical evidence derived from real instructional outputs, including syllabi, lecture slides, laboratory activities, and assessments\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOperationalizes ethical and governance boundaries through enforceable workflow stages that clearly separate GenAI-assisted drafting from human-exclusive validation, instruction, and grading\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e1.6 Contribution to the Literature\u003c/h2\u003e \u003cp\u003eThis study contributes to the existing literature in several important ways:\u003c/p\u003e \u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eExtends GenAI-in-education research beyond student use and content generation to instructor-led course design and assessment workflows\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eContributes a replicable and transferable instructional framework that can be enacted by instructors across disciplines and institutional contexts\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eClarifies the central role of human judgment, validation, and accountability within GenAI-supported instructional processes\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e Bridges scholarship across educational technology, instructional design, and responsible GenAI governance, addressing calls for structured, ethical, and human-centered GenAI integration\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e1.7 Aim and Objectives of the Study\u003c/h2\u003e \u003cp\u003eThe aim of this study is to design and demonstrate a human-centered, GenAI-supported instructional workflow that enables scalable course design and assessment while preserving academic authority, pedagogical quality, and ethical governance. To achieve this aim, the study pursues the following objectives:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo design the HACSDAW framework for structured and transparent human\u0026ndash;GenAI collaboration in instructional design and assessment\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo implement HACSDAW in a real university course delivered over a full academic semester\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo analyze human\u0026ndash;GenAI interaction patterns across key instructional stages, including syllabus development, content preparation, and assessment design\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo evaluate syllabus alignment, ethical GenAI use, and instructional efficiency under authentic teaching constraints\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo provide empirically grounded guidance for responsible GenAI adoption in higher education teaching practice\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.1 GenAI in Higher Education Teaching and Instructional Design\u003c/h2\u003e \u003cp\u003eRecent studies report a rapid increase in educators\u0026rsquo; use of GenAI to assist with lesson planning, instructional content creation, learning activity design, assessment preparation, and feedback generation. Within this context, GenAI is commonly framed as a productivity-enhancing instructional assistant that supports teaching efficiency rather than as a replacement for instructors\u0026rsquo; pedagogical decision-making and professional judgment (Moundridou et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExisting research on GenAI adoption in education is predominantly tool-centric, emphasizing software usability, technical features, and perceived usefulness of digital tools. Consequently, broader pedagogical processes\u0026mdash;such as coordinated instructional workflows, collaborative human\u0026ndash;GenAI interaction, and systematic information-management practices across teaching stages\u0026mdash;receive comparatively limited scholarly attention (Ahankoob et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEducators\u0026rsquo; beliefs, attitudes, and perceived control over GenAI systems play a decisive role in shaping whether GenAI is adopted as an augmentative instructional aid or positioned as an automated substitute. This perception-driven framing directly affects pedagogical authority, accountability, and responsibility, with automation-oriented adoption posing potential risks to instructor autonomy and professional judgment (Tsung-Chi, 2024).\u003c/p\u003e \u003cp\u003eWhile existing GenAI-supported educational frameworks often address isolated instructional functions or focus on technically end-to-end, student-facing system deployment, they provide limited process-level guidance for instructors. In particular, there is a lack of coherent, end-to-end instructional workflows that span syllabus design, lecture development, and assessment preparation, and that explicitly incorporate pedagogical validation, ethical oversight, and academic governance across the full teaching lifecycle (Nouraei Yeganeh et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sajja et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.2 HITL and Human-Centered GenAI for Teaching\u003c/h2\u003e \u003cp\u003eHITL approaches are widely recognized as a foundational governance mechanism for ethical and responsible GenAI use in higher education. By embedding continuous human oversight, validation, and accountability across the GenAI lifecycle, HITL frameworks help mitigate ethical risks, prevent over-automation, and ensure transparency, institutional accountability, and alignment with human values and pedagogical intent (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Within educational contexts, a critical distinction is drawn between GenAI augmentation and GenAI automation. Augmentation positions GenAI as a supportive instructional aid that enhances human expertise and pedagogical judgment, whereas automation frames GenAI as a substitute for human agency, potentially displacing instructor authority and professional responsibility. These competing sociotechnical imaginaries significantly shape educational policy, institutional governance, and teaching practice, influencing how GenAI systems are designed, adopted, and regulated in higher education (Rahm, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consistent with a human-centered perspective, prior studies emphasize review, refinement, and validation as non-transferable instructor responsibilities in GenAI-supported teaching. While GenAI systems can assist with drafting instructional materials and generating preliminary ideas, instructors retain primary responsibility for critically evaluating GenAI outputs, refining instructional content, and validating pedagogical accuracy, curricular alignment, and ethical appropriateness to ensure instructional quality, academic integrity, and accountable teaching practice (Kohan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.3 GenAI, Assessment Design, and Academic Integrity\u003c/h2\u003e \u003cp\u003eGenAI has placed assessment at the center of ongoing debates in higher education, positioning it as the most sensitive instructional domain affected by GenAI integration. The use of GenAI challenges established notions of assessment validity, academic integrity, and fairness, prompting calls for fundamental rethinking of assessment objectives, methods, and evidence of learning. In particular, concerns related to cheating, inappropriate automation, and the reliable evaluation of higher-order cognitive skills have intensified as GenAI systems increasingly support content generation and problem solving (Nguyen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In response, prior research has emphasized a shift toward authentic, higher-order, and applied assessment approaches that prioritize performance-based, problem-solving, and real-world tasks over rote memorization and examination-oriented formats. Such approaches are viewed as more capable of capturing meaningful learning outcomes and mitigating some limitations of traditional assessment systems in GenAI-augmented learning environments (Wilson \u0026amp; Narasuman, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite these conceptual advances, there remains a persistent lack of process-level guidance on how instructors should integrate GenAI into assessment design while retaining pedagogical control and academic authority. Existing AI-in-education studies and institutional initiatives frequently articulate ethical principles and technical affordances but offer limited operational direction for instructors on how to use GenAI in assessment design in ways that preserve professional judgment, accountability, and academic integrity (Chang, 2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Teacher Competencies and Responsible GenAI Use\u003c/h2\u003e \u003cp\u003eEffective and responsible integration of GenAI in higher education requires educator competencies that extend well beyond technical tool usage. Prior research emphasizes that instructors must develop capabilities related to ethical judgment, pedagogical decision-making, and institutional governance in order to ensure that GenAI-supported teaching practices remain accountable, transparent, and educationally sound (Stenberdt et al., 2023). Without such competencies, there is an increased risk of over-reliance on GenAI systems and insufficient critical evaluation of GenAI-generated outputs. The growing tendency to delegate ethical reasoning and decision-making to expert systems or abstract ethical guidelines may create epistemic blind spots, diminish reflective professional judgment, and weaken human accountability when GenAI outputs are accepted without sustained critical scrutiny (Hedlund, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In response to these challenges, recent studies highlight the value of workflow-based guidance and institutional frameworks that explicitly structure human\u0026ndash; GenAI collaboration. Such workflows help maintain instructor accountability by clearly delineating human responsibility, decision authority, and validation roles across instructional processes, thereby preserving pedagogical control, assessment integrity, and ethical GenAI use even as GenAI assistance becomes more prevalent in higher education (Chiu, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., 2025).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Identified Gap and Positioning of HACSDAW\u003c/h2\u003e \u003cp\u003e The existing literature on GenAI in higher education offers extensive policy guidance, conceptual discussions, and perception-based analyses, with a strong emphasis on ethical principles, instructor attitudes, and tool-specific applications. However, it provides limited empirically grounded evidence demonstrating how instructors systematically enact human\u0026ndash; GenAI collaboration across the full instructional lifecycle in authentic teaching contexts. In particular, there is a lack of instructor-centered, end-to-end instructional workflows that operationalize ethical governance, clarify human and GenAI roles, and preserve academic authority, pedagogical validation, and institutional accountability under real teaching constraints.\u003c/p\u003e \u003cp\u003eMore specifically, prior research reveals the absence of instructional workflows that:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBegin with human-led knowledge curation, in which instructors independently select, interpret, and validate disciplinary content and learning objectives before any GenAI involvement, thereby anchoring GenAI use in established pedagogical intent and curriculum governance;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSystematically structure syllabus design and weekly instructional content, where GenAI supports organization and drafting in alignment with approved syllabi, while instructors retain explicit responsibility for review, refinement, and formal approval;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSupport just-in-time lecture preparation under authentic constraints, including full-semester course delivery, sustained instructional workload, and limited preparation time, which are largely absent from experimental or simulated GenAI-in-education studies;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFormalize GenAI-assisted assessment drafting while enforcing human exclusivity in validation and evaluation, explicitly reserving assessment approval, laboratory activities, grading, and evaluative judgment as non-transferable instructor responsibilities.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTo address these gaps, this study positions the HACSDAW as a process-centric, HITL instructional framework that governs GenAI use through clearly defined workflow stages, role boundaries, and validation checkpoints. Rather than proposing abstract ethical principles or isolated tool adoption strategies, HACSDAW provides replicable, instructor-enacted guidance demonstrating how GenAI can be systematically integrated into syllabus design, instructional content development, and assessment preparation without compromising pedagogical quality, academic integrity, or instructor accountability. Through real-course implementation across a full academic semester, HACSDAW responds directly to the literature\u0026rsquo;s call for structured, ethical, and empirically grounded models of responsible GenAI use in higher education teaching practice.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Design\u003c/h2\u003e \u003cp\u003eThis study adopts a design-oriented, practice-based research design grounded in Design Science Research (DSR), with the primary aim of developing, enacting, and evaluating an instructional workflow for human\u0026ndash;GenAI collaboration in authentic higher-education teaching contexts. Consistent with DSR principles, the study focuses on transforming conceptual and ethical considerations into an actionable instructional artifact\u0026mdash;namely, the HACSDAW framework\u0026mdash;and examining its enactment under real teaching conditions rather than controlled or simulated environments.\u003c/p\u003e \u003cp\u003e The research design emphasizes iterative refinement through practice, where the proposed workflow is applied across a full academic semester and continuously validated through instructor-led decision-making, review, and reflection. Rather than evaluating GenAI systems based on algorithmic performance or output accuracy, the study prioritizes human\u0026ndash;GenAI interaction patterns, governance mechanisms, and accountability structures, recognizing that transparency, pedagogical control, and ethical oversight are more critical than technical optimization in educational contexts (Abedin et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Klenk, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Steel et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tuure et al., 2024).\u003c/p\u003e \u003cp\u003eEmpirical evidence is derived from authentic instructional artifacts and systematically documented human\u0026ndash;GenAI interactions produced during the real-world implementation of HACSDAW in an undergraduate university course. These artifacts\u0026mdash;including syllabi, lecture materials, laboratory activities, and assessment instruments\u0026mdash;serve as concrete evidence of how the workflow operates in practice and how human authority, validation, and responsibility are maintained across the instructional lifecycle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 HACSDAW Framework as Methodological Instrument\u003c/h2\u003e \u003cp\u003eHACSDAW is a seven-stage as Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, HITL instructional workflow that governs GenAI use in course design, content preparation, and assessment, ensuring scalability while preserving academic authority, pedagogical rigor, and ethical control.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStage 1: Human Knowledge Curation\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eInstructors select and validate authoritative textbooks, define content scope and rigor, and establish boundaries for subsequent GenAI use.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eStage 2: GenAI-Assisted Syllabus Design\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e GenAI supports syllabus structuring and sequencing, while instructors review, refine, and formally approve alignment with CLOs, PLOs, and institutional policies.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStage 3: GenAI-Assisted Content Structuring\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGenAI organizes weekly learning content mapped to textbooks; instructors control instructional emphasis, sequencing, and cognitive level.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStage 4: GenAI-Supported Lecture Drafting\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGenAI generates draft lecture slides based on validated inputs; instructors review, edit, and finalize materials for classroom delivery.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStage 5: Human-Designed Laboratories\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eInstructors exclusively design hands-on laboratory activities aligned with tools, skills, and learning outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStage 6: GenAI-Assisted Assessment Drafting\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGenAI drafts assessment components; instructors refine and validate difficulty, fairness, alignment, and ethical suitability.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStage 7: Human Assessment Finalization\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eInstructors exclusively finalize assessments, conduct grading and feedback, and enforce academic integrity and institutional regulations.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe HACSDAW framework enforces a clear separation between GenAI-assisted and human-exclusive stages across the instructional workflow. GenAI is deliberately constrained to drafting and structuring functions, while human instructors retain full responsibility for knowledge curation, pedagogical validation, practical instruction, assessment finalization, and evaluation. Human validation is embedded at every GenAI-assisted stage, ensuring that academic authority, assessment judgment, and ethical accountability remain exclusively human-controlled throughout the course lifecycle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Case Study Context and Data Sources\u003c/h2\u003e \u003cp\u003eThe study was conducted within a single undergraduate Computer Security course delivered over one academic semester. The instructor held full responsibility for syllabus design, instructional content preparation, laboratory activities, and assessment. The course was implemented under authentic institutional constraints, including formal syllabus approval processes, a fixed teaching schedule, sustained instructional workload, limited preparation time, and compliance with institutional academic policies.\u003c/p\u003e \u003cp\u003eData sources consisted of authentic instructional artifacts and process documentation produced through the enactment of the HACSDAW framework across the full instructional lifecycle. Instructional artifacts included an approved course syllabus, weekly lecture slides, human-designed laboratory activities, and assessment instruments (quizzes, midterm, and final examinations), along with revision traces documenting iterative refinement throughout the semester. In addition, process evidence captured documented human\u0026ndash;GenAI interactions, including explicit records of where GenAI was applied for drafting or structuring tasks and where instructors validated, modified, or rejected GenAI outputs. Together, these data sources provided empirical evidence of the framework\u0026rsquo;s enactment under real teaching conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Implementation Procedure and Ethical Safeguards\u003c/h2\u003e \u003cp\u003eThe HACSDAW framework was implemented sequentially across the academic semester through the enactment of its seven defined stages. GenAI assistance was applied only at designated stages, with explicit human validation enforced at every GenAI-assisted step to ensure pedagogical accuracy, ethical compliance, and instructor authority throughout the instructional process.\u003c/p\u003e \u003cp\u003eEthical safeguards were embedded within the implementation procedure. GenAI was explicitly excluded from grading, evaluation, and final academic judgment. GenAI use was transparently documented, and ethical constraints were incorporated into assessment design to preserve academic integrity, fairness, and institutional accountability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Methodological Scope and Limitations\u003c/h2\u003e \u003cp\u003eThis study is based on a single-course, single-instructor case, which limits statistical generalization. The methodological emphasis is on demonstrating feasibility, enactment, and governance of the proposed workflow rather than evaluating causal effects on learning outcomes. Replication across courses, instructors, and institutional contexts is identified as a direction for future research.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Evidence of HACSDAW Enactment\u003c/h2\u003e \u003cp\u003eThe results demonstrate the successful enactment of all seven stages of the HACSDAW framework across a full academic semester. Each stage was implemented sequentially as designed, from human-led knowledge curation to human-exclusive assessment finalization. GenAI support was consistently confined to designated drafting and structuring activities, while human validation was enforced at every GenAI-assisted stage. The implementation confirms that the workflow is operationally feasible under authentic instructional conditions and institutional constraints.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStage 1: Human Knowledge Curation\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eInstructors independently selected and validated authoritative textbooks, defined course scope and rigor, and established pedagogical boundaries for subsequent GenAI use. Evidence: Primary textbooks and institutionally approved syllabus reference documents.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStage 2: GenAI-Assisted Syllabus Design\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGenAI supported the initial structuring and sequencing of syllabus components. Instructors reviewed, refined, and formally approved the syllabus to ensure alignment with course learning outcomes (CLOs), program learning outcomes (PLOs), and institutional policies. Evidence: Institutionally approved course syllabus.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eStage 3: GenAI-Assisted Content Structuring\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eGenAI was used to organize weekly instructional content mapped to validated textbooks. Instructors controlled instructional emphasis, sequencing, and cognitive level. Evidence: Weekly learning-content guidelines aligned with the approved syllabus.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStage 4: GenAI-Supported Lecture Drafting\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGenAI generated draft lecture slides based on validated inputs. Instructors reviewed, edited, and finalized all lecture materials prior to classroom delivery. Evidence: Finalized lecture slide decks.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStage 5: Human-Designed Laboratories\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAll laboratory activities were designed exclusively by the instructor to ensure alignment with tools, skills, and learning outcomes. No GenAI involvement was observed at this stage. Evidence: Embedded laboratory activities integrated within weekly instructional materials.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStage 6: GenAI-Assisted Assessment Drafting\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGenAI supported the drafting of assessment structures. Instructors refined and validated assessment difficulty, fairness, syllabus alignment, and ethical suitability. Evidence: Draft assessment structures.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStage 7: Human Assessment Finalization\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFinal assessments were exclusively finalized by the instructor. Grading, feedback, and enforcement of academic integrity were conducted without GenAI involvement.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEvidence: Finalized midterm and final examination instruments.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eCollectively, these artifacts provide concrete evidence that HACSDAW was enacted as designed across the full instructional lifecycle. The results confirm that a structured, HITL workflow can be operationalized under authentic teaching constraints while maintaining clear separation between GenAI-assisted and human-exclusive instructional responsibilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Human\u0026ndash;GenAI Interaction Patterns\u003c/h2\u003e \u003cp\u003eAnalysis of instructional artifacts and process documentation revealed a consistent draft\u0026ndash;review\u0026ndash;approval interaction pattern between instructors and GenAI. GenAI outputs primarily functioned as preliminary drafts, which were subsequently reviewed, modified, or rejected by the instructor to ensure pedagogical accuracy, syllabus alignment, and ethical suitability. Human-exclusive control was strictly maintained for laboratory design, assessment finalization, grading, and evaluative judgment, reinforcing clear role boundaries and instructor authority throughout the instructional process. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the observed distribution of GenAI-assisted and human-exclusive roles across key instructional tasks.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHuman\u0026ndash;GenAI Interaction Roles Across Instructional Tasks\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTask\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenAI Draft\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman Verify/Execute\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyllabus draft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLecture slides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLab activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMidterm exam (questions/projects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinal exam (questions/projects)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrading (coursework, mid \u0026amp; final)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e✓\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Instructional Efficiency Under Real Constraints\u003c/h2\u003e \u003cp\u003eThe enactment of HACSDAW supported just-in-time instructional preparation under realistic workload and time constraints associated with full-semester course delivery. GenAI-assisted drafting enabled the timely development of lecture materials and assessment drafts, while mandatory human validation ensured consistency with course objectives, approved syllabus structures, and institutional requirements.\u003c/p\u003e \u003cp\u003eAnalysis of weekly instructional artifacts and revision traces indicates that instructional materials were produced and refined on schedule throughout the semester, despite limited preparation time and sustained instructional workload. GenAI support reduced the time required for initial drafting and content organization, allowing instructors to focus effort on pedagogical validation, contextual adaptation, and quality assurance.\u003c/p\u003e \u003cp\u003eObserved workflow execution demonstrates that instructional efficiency was improved without compromising pedagogical alignment, content quality, or institutional compliance. Across all stages, human approval remained a required checkpoint prior to classroom use, assessment deployment, or evaluative judgment.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the observed instructional artifact production pipeline, showing the sequential flow from human-curated knowledge sources through GenAI-assisted drafting and human-approved instructional outputs across the full instructional lifecycle.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Assessment Design Outcomes\u003c/h2\u003e \u003cp\u003eResults indicate that GenAI effectively supported the initial drafting of assessment components, including question formulation and structural organization. However, all assessment-related decisions concerning difficulty level, alignment with learning outcomes, fairness, and ethical appropriateness remained under exclusive human control. Final assessment instruments reflected consistent alignment with course learning outcomes and institutional assessment standards, demonstrating that AI support did not bypass pedagogical validation or academic governance. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the enforced task-level permission boundaries governing GenAI use during assessment design, highlighting that GenAI was limited to drafting support while all evaluative, grading, and instructional decisions remained exclusively under human control.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Interpretation of Findings\u003c/h2\u003e \u003cp\u003eThe findings confirm the practical viability of HITL GenAI integration when governed through a clearly defined instructional workflow. The successful enactment of all seven HACSDAW stages across a full academic semester demonstrates that GenAI can be systematically integrated into course design and assessment processes without diminishing instructor authority, pedagogical judgment, or ethical control.\u003c/p\u003e \u003cp\u003eRather than operating as an autonomous or decision-making system, GenAI functioned strictly as a constrained instructional assistant, supporting drafting and structuring tasks while remaining subject to continuous human validation. Human instructors consistently exercised final authority over pedagogical decisions, laboratory design, assessment approval, and grading. These findings indicate that instructional scalability and efficiency can be achieved without compromising academic governance, provided that GenAI use is embedded within enforceable workflow boundaries rather than ad hoc or tool-driven practices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Alignment with Existing Literature\u003c/h2\u003e \u003cp\u003eThe results extend existing instructor-centered GenAI research by moving beyond perception-based, policy-oriented, and tool-centric studies toward a process-oriented, enactment-based perspective. While prior literature emphasizes ethical principles, instructor attitudes, and governance frameworks, it often lacks operational clarity on how these principles are enacted during everyday teaching activities.\u003c/p\u003e \u003cp\u003e HACSDAW contributes to this gap by operationalizing ethical and governance principles into explicit workflow stages, role separations, and validation checkpoints. Through artifact-based evidence, the study demonstrates how transparency, accountability, and human oversight can be embedded directly into instructional processes, rather than relying solely on institutional policies or abstract ethical guidelines. In this way, the findings provide empirical support for calls in the literature for human-centered, instructor-governed GenAI integration grounded in authentic teaching practice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Pedagogical Implications\u003c/h2\u003e \u003cp\u003eFrom a pedagogical standpoint, the findings suggest that structured GenAI support can meaningfully assist instructors in managing instructional workload and time pressure under real teaching constraints. GenAI-assisted drafting reduced the effort required for initial content and assessment preparation, enabling instructors to allocate greater attention to pedagogical validation, contextual adaptation, and instructional quality assurance.\u003c/p\u003e \u003cp\u003eBy explicitly reserving syllabus approval, laboratory design, assessment finalization, and grading as human-exclusive responsibilities, HACSDAW preserves pedagogical coherence, syllabus alignment, and assessment integrity. The results indicate that instructional scalability does not necessitate pedagogical automation; instead, it can be achieved through carefully governed human\u0026ndash;GenAI collaboration that reinforces, rather than replaces, professional judgment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Institutional and Policy Implications\u003c/h2\u003e \u003cp\u003eAt the institutional level, the findings highlight the limitations of purely prohibitive, tool-restrictive, or compliance-focused GenAI policies. Such approaches may fail to reflect the realities of instructional practice and can inadvertently encourage unregulated or opaque GenAI use.\u003c/p\u003e \u003cp\u003eThe results suggest that workflow-based governance models, such as HACSDAW, offer a more effective and sustainable alternative. By embedding ethical safeguards, role boundaries, and validation checkpoints within routine teaching workflows, institutions can support responsible GenAI adoption while maintaining academic standards, accountability, and instructor autonomy. Rather than framing GenAI governance as external regulation, the findings demonstrate the value of process-level governance enacted by instructors in practice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Addressing the Research Questions\u003c/h2\u003e \u003cp\u003eThis study explicitly addresses all three research questions through the design and real-world enactment of the HACSDAW framework.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eRQ1 is addressed by demonstrating how GenAI can be systematically integrated into course design and assessment through a structured workflow that enforces human validation and preserves instructor authority and institutional accountability.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRQ2 is addressed through the identification of consistent human\u0026ndash;GenAI interaction patterns, characterized by a draft\u0026ndash;review\u0026ndash;approval cycle across all GenAI-assisted stages.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRQ3 is addressed by evidence showing that the HITL workflow supports syllabus alignment, pedagogical quality, and ethical GenAI use while enabling instructional efficiency under authentic teaching constraints.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Limitations and Future Research\u003c/h2\u003e \u003cp\u003eThis study is limited by its focus on a single course and a single instructor, which constrains statistical generalization. The primary emphasis is on demonstrating feasibility, enactment, and governance, rather than measuring causal effects on student learning outcomes.\u003c/p\u003e \u003cp\u003eFuture research should replicate the framework across multiple courses, disciplines, instructors, and institutional contexts to examine transferability and adaptability. Comparative and longitudinal studies may further explore how workflow-based GenAI governance influences instructional sustainability, institutional policy development, and long-term pedagogical practice as GenAI adoption continues to expand in higher education.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Summary of the Study\u003c/h2\u003e \u003cp\u003eThis study proposed and empirically examined the HACSDAW as a human-centered, GenAI-supported instructional framework. Grounded in DSR, the framework was designed, enacted, and evaluated through full-semester implementation in an authentic undergraduate course. The study focused on demonstrating the feasibility, enactment, and governance of structured human\u0026ndash;GenAI collaboration across the instructional lifecycle rather than evaluating causal effects on student learning outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Key Contributions\u003c/h2\u003e \u003cp\u003eThis study makes three primary contributions:\u003c/p\u003e \u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eFirst, it introduces a workflow-based, instructor-enacted framework that explicitly structures human\u0026ndash;GenAI roles across syllabus design, instructional content development, and assessment preparation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSecond, it provides empirical, artifact-based evidence derived from real instructional outputs\u0026mdash;such as syllabi, lecture materials, laboratory activities, and assessments\u0026mdash;demonstrating how GenAI can support instructional processes without undermining academic authority or ethical control.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e Third, the study operationalizes responsible GenAI governance by embedding enforceable role boundaries and human validation checkpoints directly into instructional workflows, moving beyond abstract ethical guidelines and tool-centric adoption models.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Practical Significance\u003c/h2\u003e \u003cp\u003eFrom a practical perspective, HACSDAW offers instructors and institutions a replicable and realistic approach for integrating GenAI into higher education teaching under authentic workload and time constraints. By confining GenAI to drafting and structuring tasks while reserving pedagogical validation, laboratory design, and grading as human-exclusive responsibilities, the framework supports instructional efficiency and scalability without compromising syllabus alignment, assessment integrity, or professional judgment. As such, HACSDAW provides actionable guidance for responsible GenAI adoption that aligns with everyday teaching practice rather than exceptional or experimental conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e6.4 Final Remarks\u003c/h2\u003e \u003cp\u003eAs GenAI adoption continues to expand in higher education, the central challenge is no longer whether GenAI should be used, but how it should be governed in practice. This study demonstrates that structured, HITL instructional workflows offer a viable pathway for responsible GenAI integration, enabling instructors to harness GenAI\u0026rsquo;s support while preserving academic authority, ethical accountability, and pedagogical quality. By shifting the focus from tools and policies to workflow-level governance enacted in real teaching contexts, HACSDAW contributes a practical foundation for sustainable and human-centered GenAI use in higher education.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved normal educational practices conducted within a university course and did not include the collection of personal, identifiable, or sensitive student data. According to the institutional research ethics guidelines of Lincoln University College, studies focusing on curriculum design and instructional workflow evaluation that do not involve identifiable participant data are considered minimal-risk educational research and therefore do not require formal Institutional Review Board (IRB) approval. The research was conducted in accordance with relevant institutional ethical guidelines and the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudents participated in the course as part of normal academic instruction. Informed consent to participate in the study was obtained from participants involved in the instructional context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBIOGRAPHIES OF AUTHORS\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cimg width=\"87\" height=\"91\" 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G6eyvHEmywvnszxxmmA4wYBfzvHGRT4/lzETZ3judIorzsox2RXHEqcSEFPU8fisaerxgehJ+ClqInaHjcWOEAfsFPYImYBlbkORNrwD/Lo2hGMLC/SxoPOwNEAzug1rikEzBln6I+oTCAIGPbJCPaaEukwb0v5RixZZWjDNqwfMsKxZubI6Q/zrpYYv4IDyeXNgb2OFGn/+kwq+IenfkDVHRJYIqHo/fgfDWjVgQ5Zob6GHgU2o+Lu2QAhTQMKY3sibxhQQMg2bYjywPd4Te2d54kCSF44y8EcJgOOpnjjJcjzFgwDwxYUcP1zM8WXgvXAp21sdz2d64HK2Fy4SCOfT3Hn0wLnUGTiX4oILZI2zZIqTBMGxuCk4TpaQcpTscDhmMhmDoIicgD1h47AzeDw2MF3Mc+qLmP4t4dzSBMOs66MX9Us72k9LspohraaMcTCSkVOSLgQE39ZQvaEaJ/SDckrSCqpLwTmkX188f/LkbzLp7z41CLorli1FY3s71UMo7QLGurRedesqb61plq3BmvM9TOrUQCO9eujXyBiuvVoiadJAzHFn7o50xdZ4L+xM9sPeNH/sS/PFwVRvHEnzJhC8CQJvnM30Y033wXkWOV7M9CUDEAzZPriUxcLjZQLiCpniRp4frvLx5UxPXCIoLma446oAhsA4w5RxhhpCjufIFFJOJU7DoThHHIlzwoHIidgVNBpbvIdgM7XEMrfByBpDp9HDnrbVCgMsG6AFnY0FBa5JA2l40kE9poIG0u9RS5eAr6PaF6StRFodLY1kDKWuGuAyr7T0XxMI8vPgwQMM6NuPdooMIH0ChkYw4B8urXK6tTX9CfUIBIPaP8K2fm20NtaDE/NvjpsD1sZ5M/iBLP7Ymx6C/ZlhOJAThkM5ITiaE4iTeYE4kR2A03x8OtOf+d8TZ9M9mQ58cKUgEFdZrheG4HpBMK7nBxEA/rie66eAcC3HR4Hheq6vKle0QDhPDXEuZYYqF8ga8vw0QXGcYDgs7MBykOlif+hY7CYgNvuPxErvYVhIl1I4uQ+CerfEACt9NNGpBSvaTBM6hLrfiSP6hnpBAm7Av1lH2ydRS42plDGUYp+HDhyAx7xfX4LhXwYIGzdsgK2VDYz0DGBlZg49XelN1NEMGZM8SiCIirak2Gqqr4Pu1mYIG9EVK6NmYGuSH3anBWFvRigO5sfgEMvh3CiCIAxnCsJxoTiCxxCcyQtW6eFwwnScpw64UhiEayWhuD03AvfmRuFuaSTulITjZlEwbhEctwoCVLmZ769AcacoSB0FENVsISDQMIYHTlNfnCEwlJZIEEDQgUSMxx4CYUfgaGz0HU6XMRIbAsZioftwBPZqjn4W9dGknsZN6FIf1Pr2W/zwzZ9YATQVoh5tZZ0fNY1nMtDFVLc+rIyNUT5nzn9r0v7nAOFXABwWGg4jfWM0tLaHmZEp6hEAujr1NcPOZDQQgSEdN7a0YS0N68GhbRPM9RqPnSk+2JPqj58yg3EoLxJHCmJwvCgOp/KicaEgCldmR/EYhkuF4biUH4bL+SG4nOuPqxJIqfEM7u054bhTGoG7cyJxj2C4WxqGu8UhKvACBDnenR2Eezx3uzBQAUPOV4NBQCBgEHY4z3IpnYzBcmLWZzBsDxiJzbSdm7xZAhywifqhjFbTt2czikh92JD+G1AXiIuoXeNb1Z4gA2V0CIh60shEJpAR1TLSSVLmqKHDPmmFfxmx+PTRE4x3cIAxRVNTu8YEghn06suwL1PYGFupbmPporUxNYG9gT6Flg48+7fF2ihX7E8PIghCcIQgOFoQjeOFMThJIJwujMa54jhcKY3H1eIYXC+JxY3iaNwqieYxHFcLg3FJqJ4p4Dof3ymJxO3ZBEBJBB6UhjPwIdrAB6nygOCQIgAQMEiRFFINhiuiJcgySlNkeGqAQadxnO7iIF3FrpAx2B44CtsCRhEII7EliGDwH43iiT3g2dEO3YzrwvSHb1BPNTmTBX6QPgjqhJqaIqOqpJFJnIMeHYe1uTHWrlqhqVeVVb8c0f0r8zF/F0A4eugw2rVqrYaVt2rcAhbG5jA1NIONuY0GCGQFS2MjNLW0RMMG9dHbWg+pTgOxI9kHB7NDqQlCcZhAOEYQnJ4dh5MEwVkezxMEV+Yk4FppHIEQh5ulsbgzNw63SqNxvTiMJQTXhPoZdJUWeO7+HAJhbqQCwq18sgFBcIep4n4JgTCXzFFM0JAdbkvayA9QILnJ4zVJF2SKawKMXBGgIj69lB09GD2Z9nKSchICiG3BY7CVRRqo1niNRO6EPpjQyhr2dX+AAYOtX0cXdQmG2t//qPol6mk7quS5iMd6tckWFMyRocG/FNy/ohm+eiB8ebFLFixEI2sZX2iHJraNYG1mBQtTCzS0soWNiZlmHgHTQiMzEzSmsJrQ1hYLfCcQAGSDrBAWaoOccKaEWJxiOVMUw8LUwOBfL0vErXmJuDc/CfdZ7pQRCHPIDNQEt8uYCsqi8GBeNB7Oi8Hj+XF4ND+Wxxg8msfXZzPoBMtdguBOcSjfF4mH5VF8b5Q6f4/nBCDVrCHlhgCrKFClnUtkiPMZHgoMh2IcsS98vALCjrDxqqVyq7CD/1gs9xmD4EEd0MmU+b9uLbqGumqEtJ5OPU0HlQCAR42LqKEGwchEneGDBuLhg/t/dT//GeLxn88IvOZZ8QkMvjma2Ngz+HakPSsKRgvYmlvAkiwhI4vUVDSyQRujevCiZVwb5oyDWUEabZAbQXEYiROFsWQEAmF2DM4zHVyeE48bBMGt8kTcX5CEBwuSeZyFu+XxPCbgXnkcHsyXEotHC+PxfEkSni5KxOMFcXhcHsuAR+P+XAk+gbGA7xGAsDzh4wcEkTDHPYrL+6IvisM0hWnlOoXmZaaci5nSLkHLSkGpXASZQcCwJ1Ian8Zjmx91A8v6wAlIn9AbA20N0ISW0lR6VOkWZPiaDH6VNoW6qqu6jmIEAYNM0bOztFCzqH6NCX53QHj35i3cXWfA3Ii1nYxga24NSxML2FnxSNEoQ7xk3oFMMLFuUA8dzeojYngXbI11xZHsYBzICiYbhOJofpRig9OzhQ2icJGa4KoAoTwBN8gCd+Yl4NHiFDxenKqODxfNwsMFDDqPDxbEK0DcK4th0GPxZGEig53AkohHBM2j+fE8J8/j8JSAeURgPGZ5wPffoaaQtPJgroCG+qMoBDdmhyrdcY1W9EKGF87QWh6nizga56yYQZqkd4aIgByFjT5Dsd7fAcXTBmFMM3M00qkB07p1lVVsIB1qapCrnhoMq4blSZe2DKuvLc3Q3yHAx5sA+PhPY4J/KhC+RO+Vy1fQt1dvNdSsobUtgWADK6YFmXwiI3mk1c2CgLDjczuDBuhq2QAJ4/tgV5IXDpERDmQE4jB1wvGCSFVOMy2cIxtcnRuP63MTFBvcJQsoRliYRBBogPB4aQqeLBFgJCtASHm0cBYDPgvPFiWTHVLV44cEwmO+dq+MKYMM8pQAeTQvlloiioGP0thNBv4uH9+ivpDHt7XnBBRX8wOZHryZHmYQCNMUEPZGOJIRJmIndcJmvxGqf2Kuy2A4trFCYx1pMKujhrfJzChpUJPh80ZaIGi6qesolqz13ffo1bULHmnTwz/z558GhOqffXv2ok2LljDTN0IjagILikQLMoJZAyNFi+ZGxrCmeLQ3MYedXn30In1mTh+O7Une2J/qi0MZQTieR32QF4GTwgqFUThPNrgyRxxDnBYI1AdMC3fnz8I9guIRg/x0WboCwlOW58vS8Gxpqiov+fj18iy8qcjG88Vp1AVkC4Lh7lymBDKDgOERQSE28zZTgbQ/3NOC4A5Txf2yaGVDBQTXCoKUVb2Q5YOTyW44EisOYgoOxkzBPtVRNQFb6R4ECGXTBmB6B2u0MaitxkLqUhwKEAwYfEOdBmqWlWgGfZlUIxNqeJSxjmZ0UetXr/79AqEaDLt37FTzFGXOQGNrOzKDiSoy/FuaVWWksintozWZoZlRAwxtbolc19HYluCD/Sm+OJjmhyNkhiPZIQoMpwoiFCMIEIQRbopGmJ+IOwTBbaaH+2SFx0vTmBrIBKzxEviXFel4UZGGp2SHFwIIYQyywCu+JixxhyC4Wyb6IE6lCGGEh3wuVvMOraiAQkTmw/JoBQRpk7hFkAgbCBguZvvidOpMnEiagcNxUwkIJxygXhAgiIMQICxyGwqPrg3RyUQHNjJ8Xoas6ejAnH+7Ub0GanaVCj7TgoBCzhnU0UE96oWosLDfJxC+/Nm9YwdaN22umlRtLWzU/EQBgrW5zFwypFX6UU1Jb0gN0cpEHw50DBlOQ7A1zpeM4IeD6f4KCEepF06TDc7OjlaMIKnhRvksFgKBQb1NMAgjPCAAhBGEGYQRnkkKoECs1gmPF2hSwf2yOJUaHqvX4giaBD5OUinkwTzRB9HUBTzOEasZSksZwsfhTAuhuCmNUaWa9CCMcCnHTzVpCyscS3TB4ajJOEwg7I0URhhNCzkMC92Hwr9nM3Qz04W9bh2YSO2nHrCRSbkNNLOrJFWKYDRUs7MN1QjpH//8J0wcOxZv37z9/QGh8osu503r19E6Wqu5iFam5top6Wa0jZaqj0FG/tqRFRoSFB2sTDGtZ1sUuDmozqW9yZ44kOZLEDA95AbjTGEEQUDbyJRwZW4i2SCJrJBI55CgBQKZQfK7cgoMuAg/FVTmezqEB+WxqtY/kBpfHqcRjQTCk8XUD4t4nm7hfnmMpubTNkpbw+2iAD4OYQlV7Q23pF1BBGNhKC7lUizmBOBCdgBOpXniVMpMgsEdB2LoeKIdeXTUtCX4jEDZ1P4I7NkYfSx10ahBHTokBp9BFzaU4feSDkyYLuvWqK3mQFjoG8DK0Eh1Vw8dMAAvnj39faeGBWVlsDIxUVPVbcgC1gSDWpuAWsHcQKaxkx0MDdHc0hRd7c0xnUAo9Z6I7bN8sDfFi4wgQOCNLgglAGJwcU4sLpcl4FqZAEADhOtkh5vM87elDYHK/i6DrtoPxAKWRah2ASmPFb3HKFfwZGGcxi6KO2Dg781j/meNfyg6QGyiBD3fFzfzvPk8iM+D+Nyfz/1wIz+AhfpAQJDlh/NZvjhH93CSqeFQrDP2RdNKKjtJ0Rg6ianBAeXT+sOvmz36W+uiqQHBL6Ohq+dd6tbXLuxhgHq1dFTnm4Ws00CGkJ7Jof374/mTx7/P1FANhMy0VApCQzSxtVNOQQIv09XMjS15tKB2MIIdX29rbYbBrezgO7grFgZNw46UAOxJ9SYIAnEiL0SVkwIGBYR4XKZGuCzMwOPVubG4ToF3o5T5WyyfiDsG85Y0IbMm358bgccEhzQiPS7XHB+VfS7SwHS7WNPSeK8gGHfyAnCbdH8j0xO3crxwK9cHN1lu5AowfHA91xvXVa+lDy7xPefTZ+IsLeSpZFdsDxyJVW79VUvjTxSMu0InYnPgOFR4jkT0oNYY18ZaDbmzYTo0VvMr6/Ee6KmBrGYNDKkR6qH29z/QXjaAkUynq1UL3Tt2xJ1frLfwO9QIsZGRBIAZ7CwsNemBaUGxgpmNcg/SstjQmGnBxhSj2jVGuEMfVETOxM70YBxUKSFIA4L8UOqDSJwtoWsgMwg7XCqNZYkiEGJwi+r/1hyCYE70J8snILjLWn6jMFB1OwvVPxEwMPgPmePvzda2HkojEUFwnYG9kcNgyzHLG1fT3XElw40g8MLtfD9cy/bCVYLgKsEhALiiHctwPs1NtSWcISCOJEzDWveB2EbbeCBykhq8sjlgLFZ4jUDq6M6Y3qUxetiawK5BfVjqNlATba2M9NUQfStZ2YUMUbdmDZUqRGDrkxGa2dnizInjv18gfPzwEd4eM7UjcIyVRpC0IO0JNqphyVy5BntjI3RvbIeJXVoi2Xk4KiLcsDcjBMcYfNEGAoTTheEEQLRihPMMuLDCNYrF6zze4DlpULovRRzA7DDcYQ6XpmNhg/vVQWduv8+a/6g0jEAIU48FCAIQTf4P0PRKEjgPeP6adEOnuvA1f54PZAmibQzG9UJpYvYnEHxwQdhAxkMmz8DxFDecSPGgc3DG3tCx2BvCEiY20hEbg8ZhtlNveHRrjCF0Rq0seT/09VVvowxgNdfTVcJRZkjp1KqhcQ9kBN2aNWHLe7R7+7bfp0aQn2ePn2DcqFEUP4JuQ2oFMwUEK1Mz9Vxa1cz5h7ewskC/1k0xo38X5Myg7473wNH8SIIgjCAIU5bxbFEUQRDPkoDL8+gWFqbhOsvt+Sm4vyCFaj+VuX4WbV8Cg0v6n0sNwJQhYvEJtcET0QZMGY+pAR7R/j0gW0gvpLCDpAt5zz3RCCqFaN77aK70VPJ9c0JVz+Rj6bugBrk5O5xACMIVpo/LmV44nyrD28gIKe7UCe44njhdDWfbReu4xW80foqZir0UkMs9hyF2WAeMbmKMtuYGsNEurCGLb0gXtLWxWGt91K9Tk7aR9rJOfVrI2nQR9bBgXtnvlxEunTuPHp068g/VVbOYbCkWLdWKJvoUQw3QkEhvbG6O9jaW6NvUGsEjemFx8FQcyiEACsJxgmA4kRehaU0sicfFuUm4MC8ZVxdl4vriLNxYko07LA+W5eLh8jw8WJxJG5hOy5hJMZhGm5hEV5DMMks5hGc8SsvhY+qIJ+XSehhNfRCvdQ9JqpVRGOX5ohRVHjHdSHvCI5Z7syP4nM6CdvU2U9LV/DBcyQvGZRGKBMKZZBc1gulkwnTV73CAGmGvtCMEjsHOsIn4KXYKdkRMRIFjbzi3MkNbozpoaKgPcwEAxbQstCErvhjI9P+6taHzPdODzK+srRmrkJGS8vsFwu7tO9G8YUO1VkDLxk2oFSyUWLTlsbGFOUFgika8GW2MdDG1W0uU+zlhb1ogjuSE4hjLyYJogiFadT1fLEvBhfI0XJifjsuLsnFzRRFuVRThzorZuLe6BPdXFePeikI84vHZmmI8XVGAp8tz8Izl6dJMvKjIxMsVWXixLINOgYFfmKSamV+vyMbrVbl4tjSDIEpVzPJkUQYtZSbdRxJ1R5zq1bxfLg1UabjD99zk48vFZKd8MlVWEE5l+KhRUUfip+FI3DTsj3LET9I1HTkZu8MmqIalfRHjsZdAWOE9ElEDW9I9NEAz/bqwrq8DezO6KlYQfZ0GaoCOkV591P9RJsnUURqhzvffwd1l+hcrrvzOgLB04SLFBDJCt6G1tVrmxs5clryRhacojmTIupkBJnVtiXl+U7A/zR8nqQlOMh0cydZ0NJ2cHY9TxQk4OzcZ58vTcVKOC7NwaWk+Li3OxZVlBbiyvAhXKgp5Phu3BBAEwl0C4eGqIjxeNRtPVxbgWUUuXqzIJRjyCIYsvOLj58uz+DwXP28oxfsNc/C0Ih/PVxbh1do5ZJh83CYYbixg+iHD3F6ShZuLshQY5VouzEvH6eJZOEOQHssOxq6Y6azxtIuRU9SEmT3h4xQj7CIQBAAHonguZDS2Bo/DUi8HePZuidZ6NWCjUwP2FIvNqJvEUteXhqb6MklWBwY1dZge6qg1FcaMGok3r17/joDwRQfZulWr0bJRYzoFzeJX9pa2yjHI6mTS7WynXx8DmlghzWk49qUHkQX8mQr8cTqfdjEnQg1LO1IUq4amyc0/VpyE3Tkx2J2XgB1ZcSyx2JgWiYqEIJSHeWBRlC+WRvtjcbgXlod7YEu8Hw5nR+FMYRzuzE/Dcwb6DcHxelUB3q4tJgDm4BVB827jPPy8dT5ebSzD8/VluL0sH8cK4nAwNwbbU4OwKtoTyyNmojzYFQXeU5HvOQXLYgKxMTUa29KjsTsrEhupa5b4OmAVreLOSEcFgF0h42kfxxMUE3BIRj7HTMKOkDFq5FLhlH4Y1UgfTet8B1veCxsTpkkbe9XXIAt3SQtjXbKCTJit+ac/wXHcePz87p3mxqp1pirxn607/psC4ctV0devWY3m9vZqJo+scGZsaKQGZppRIVsyN7Y0qgfXHq2wMtgFhzMDcZwgOF4QQEDIqORwHC8kCKgNzlIbnJqThJ0M6tIYf5SGeCHXZwaSp01ElONYBIwdgZmD+mJyVwqxNs3h2rMjEiYMw1zPyVgf5obNETNwkp93d0G6AsC7dcV4s3o2XhIUr9aV4s3Guao8WVPEFJSM7Qm+WBvlhXm+zogZ1Qcze7fHhPZNMaptE4xo1xKjO7SCc+8uCJ/ogFnTJqHYbwYK3CegeMZolHuOxtrA8dgaOglbBRR0C3sIioNxU3Ao3pnAGINtvsOx1mc4Zo3oiF4m9WBNTSCTfC2NDFVzs4munhq6VrPGj6hTsxa+/9//C4Genoy99sbKbKjKj5r1Rn4PQCibU6oWqpSmUmlKlYWsZKKrLHHbSF8Hfe2MkDJpIHbM8sGhDKaFAjJBfrB6fDQnXKWFM6WJOE0QHCtNwYr4AKTOdITP6MGY3KcbRnZshUFtW6FX86boSfvZu5EthjSzg2uv9ggd3gPFM8dgfZQLdsTNxE9JPrhYSHfAtPBu7WyVEp5SFzxfQR1BnfB4WSZu0o2cYEraHueF1WEzUDjdARHDemFal1ZwaC22ryH6NbElGJqjX1NbDGrVBFP7dUeQwyBEOgxAzIieSBvbFysCHckME7HGfyw2+jlgk88w7CEAZC7EzrDxihEECPPdhmJqh4ZoWLcGGlAHWNBO2prKYl711KosdWrUQL26dVHjz/+BsAA/zUozkM0FJPyV/9DJUP8cIGh/sjPS6Q7olQ2MYKYvayEbqg4mG/0G6GKhB6/ebcgGTtif7IkjWWSCvBAczQrEgXRSem44DhfGYH9OJHZlRmJNYiCyPRzhO3ogAdAavZs1RHt68bZm0kdhgs7WJuhtY4KRzazh3ac94kf3QonLSGwIZ+6OcsXu2Bk4kRmAuxSKDyj+Hi9MJhDSKCYz1DiG+/MTcYlO5WiKD/O9Ozby9yr8nTDfezJmjRsIl87N4NKtDdx6dcT0Hu0woVML9KfTGda6Eab374zoicMRQdcT2KctMiYORKk72WHmCKxmutjoPQLbGfg9tJMycmljkANWegzCUvfBiBzSAR0MaBG/+waWDTRL/NpRPOoQGHW1ayrU/OaPCPDy+AIIsjXBx/98T4qvAQgyqiYiJFih3FLfCOYssrSN2KVG9MqjW9oiS3oZo6ZiT4ILg0/lTRAczQhUPY57WFbHMu9GuDM3uyB5ykj4DenJ2s5A9OoEz0G9WROHINl5LHJdJyLT2QE5TiORNqYv8icMxFKPcVjrPwn7yAZHk72p6mfgAlPOndJo3CqWoe3RnwarPJifoLqir0vLJcFyIM4NB+LdcDDRAz8lemJz+DTMnzES8z3GYKGPExb5OWPDrBAspx5JnToGYaP6IZGpKHp4L8QO6w6/Hs0QNrAdksd0w+wpfRn0YdhMMOwMYFoIdMA6/+FY6dYPy1z6IX1sFwy2N4BNrR9hKc3K9erDnm5KmpZ1f6gNPeqE7/7jf2PU0EF4Vb2gRpV2Lmnl7wAIlZUfEBLgr1KBtaxfyGJlbEQ20ENz5kL3Xu2wNNAJu+JcsG8Wb3yGr1LfJ7JDcIh6YcssdxR5jmOuH4DQYV0ROrgL0p1GYLb7JCwMcMHaaD+KxwzayRycFwXPNHIsJ4yf5Yk90S707dNV8E+neOJytj8uZHrjUqYPrks/QlGwGtF8TwajyCglAuHunBjcmB2OawXStRyESxSul7L9+PszKfR4jdFTcXDWTH4PwVIai/PULTfoXK4vm41z8/OwhppiEdljsc9kpDr0QFDvpvDv0QgRfZuhcEJPrKV22Ep22OI/Emu8B2ClK1OI6wBkjemISc1N0EZPB2ay4JZaoU1mgNWlfawH3R/r4Ls//V+MHDEYT59q5zmoxWaq/qETZf+JqaEKsRHh1AN6sJU1kA1lJJKxmrfQlnkwbFgPbIqZid0M1k+82ftTvXAwjWAgCGQa2xrasLSJ/ZHo0BspY/uh1I35XvogEvywJ4nOoige9ytm4+qidJwri8XFudG4WBKOswXBaiLskVkztN3BU3CM/v5c0gxcTHNXzcYyGvku3yuthI+YEmSE0oN5cbg3J0o76ojClb+/O2QCdodS8ce7qHmVlwiCGzL2YXEqj6m4tSATDyqK8GBlKW4uzqfYjcauRB+s9BuP0mmDEDOwlQJC7tieWOXpgM3UC1v8RmCdZz+sde+PFTP6YY5Td0QMaIne5g1g+sN30K9ZW60HLX0N0q4go5Z++Pb/wtvbDR8+vv8EBLX8EKq+brFYDYXstFSmBj3YmZox75nDhkzQ2EAPfSnq0iYNwnbS7t6kmTiQ6kGB6E1AeOI4QXAoyw/LAngzXYZhRYATtsZ6Ytcs0nt6AE7mhOBsUSSulCfh2mJ6+4WpaoTSNVL9pSLWaFL/1eJonM8NxlnqjiOsxfvp749GO6kJrRdS3XGzIBBP5sepEc0yLuHhvDg1RE2AIE3Hp1I8sD9qCvZEOOEor09mUcmwuIsyf4Lfc5vAuTY3lsCIwvWyJFynyLyzOA2X+dqxdC/sT5yBfSwV3qNQML47Sh1Z+z1HKuG4kcJxo9cgrPcagjUeQ7DYhalsUk9MbmMNyx+/Qf3va8DM0EzZyAYy2rlOLVYmHezZvLb6Bn9a9v/j1w0EihktK6yjfbQ3t4AFRaLMYrJlamhmZIRBzRthjttIBtcde5PdcSTdmymB1pFUfDTXH/v5fHOsG7bFzVBjEkQ7nMoPxcWiMCr/EJwrCsXBLH8sDpiAcg8Hlb+LHQdg3vShWBfkyCB441gGQUOdcYrC81iiO47GTsMpYYUML9VhJEPPXixNwbPFiXixJBHPFsbjdmkULpENTjD4klZOEJgnyFQH+HhDEF0Ay8aIKdgU6Yzt8TOwKXoatsS5YnXwBKwLHoddkZNxUnois31whH/X3mhnbA2ZhNXeI7GcwnC93yhsYtlC+yjHdV7DsXR6P5Q49UJAvxZoVv9H6NeoDQsjS7VWo55OXTVKqbWZEZbnZeHNixfae6xZpVbtBfHbA+HXZ9tUp4b3L55gzcJ56NK0iVqSzpYCSFoSmxjqK6W9wGs0dsS7khEECD70+RLsYJwkbR/LC9S0K8hM5/xAMkCIovwdMVNRPmMIwkmlE5oZo4fed2ij8wOa1PwWjb75X+hrWh8zqe4j+3dA8ZTB2BQ8GQeoFY4muOJooosaRnYuzRNXqBOkf+Hn1Tl4vSIdz5fSQSxhzaaIPJfli+MCTr53T6QT1vuMQb5DF0T3bQ7f7o3h1Kkp+lkboJdZffQx18H4VpaY2b0RUsZ1Q/GkXih37IXdZCBZo+GgNDfHTqFgnYrtkZOwkUJxM63jNt8R1AoOWE8BWTG9N0ondkXM8PZqPKO9aCoTzSLjumQD4xrfYxztapHXdOwuK0Ll6+fa3FCp2amuqvK3AcLnoFd+ev6X4vXt45s4uCAfS6L8MIb+W9KBTNaQNNHMUA8TujRDRdAk5tOZ2JfkQSD44UROEE7LjObCUNJ/AI5T3J0hQ0jtOpbuhkUz+sOngwWGWOmim4URRnfphKZU2DPHOqF3mx4Y1rk3xnbvg7yAQKRNdcT01raIYy3bEe6EQ3QlR9USOW4KCDI9XkY0f9g4Gz+vL8KbtYV4uSoXt0ntZ/i9J5g+Dia6Ynv4FOSN6YmZre0wrWMLOLRvSY1jDGeHCWhj3xwjuveFbe066GhcH531v4dTK3OE9W6OpL5NscJtMPbLaOaYidgf70T76oQdZIxN/qPoHkZhR/B4bPIdiZUuPTHfqSvSxvZAHxs9NDXSUylU+hzqEwSN6nyLGW1skDW4DdYETsKzc4fUPf7waTuD3xwIf/G8WsHSLVzdswab4zyxO9EPCQ4D0IrBb2glPY8GaGFiCNe+XbBebGOSm0YfsPYcy/TDSdLyqdwA0jqDn+aBE+nu2B09GZkjWsHRtja61/3fGGStj4XxYViYFMMUYw+v8SMxtHN7DO/SEWN6dMFustCr4wewNjGUYq0tckZ3wvm8EFwiyI4nz8RJ5v9rfCwjmrG5GJUEw/v1BQRGOgVkLM6l+zItuOF4hj/WMs34dbRH9NBeOL58HpbnpKCzvT08JzuiV5v2cB01Fh3MrZDs5op4p7FoVfOPaFPrPzC9mSHi+zRWK7nti5yguqQPyqzpaEfNVLhgBzXvYRvZYZVrHyx07oHs8T0xtLEprHVqq7UkZQVX0xrfYEIrW6QP7QZ38++oN3ri9sFtn6thVRX+UW2L/2UgfDpX3X7w9iUuLp+NUtqiyNYNMJvKf2zrpmhiYQZbU0M0NdSF16Ae2Mj8uSthOvbLfIA0LzKAL04w78vxaCpzNGuktMLNmdAZoZ0sMdGmHkZY10fYiF7YnBGH/XNy4DusFzoZ10bzut8wPfwRk7q0xMGFxdhfko1dGTFYFTgVGUM70vZRF5RE8TPdcSbDD1eYgsQyvl+Tg3ermHtXZlAs0kIWUmQm+9JpuBA8oSh1GoDUUT2xMT4U++fmYxWB0MfOEnZMRdY//hkW3/xfDLC3RkVcFJaGBcCxpQ3a1vxf6Ff//8CntTGKx3WhMByGXQGjcZCpQSa/7Awdiy2B1Ech49QU+hUu/bF0Wj8UTOwFh6YWsGQ6sJClAmrVQF9rIxROHoLdQc7I7GyFtEFtcevg1k9a7FN6+GrEYmXV51av548p7mKQ2NEEUc1ro2LGUFrAgWhrZoAmpvpoY9IAgUT4Jqryncyhe2R2EEXcMQrEkxSMUk7w8SEKtE10DvOnDsKcKUNQwFLkOgqFLqNR6j4O5b7OiBjZB9M6NIdLu6aY1toeKeMHYa7nBJTwPYs9xmNXLIVmMkVjBnWHLLRFcXqOYLtCZ/CALuEFtcHLpUkss9RAlFt0GpfSvLEvfDKOkK12x7pic4QrVvhNQ/60sUh1HoPI4b0xo2MTTKY28OrbBlGjepPWB2P2pKGY5zwUKcPaIrp3IySQEQpGtcdCp95Y4z5EMYAMaN0ts6V9hypwbBSxOLUfljj3RfGEXhjXzAI2dWvDRt8AFrW+Q8jgztgSwGsJdcKCoa0xd8ogPL9w7LMqxz9uVff/MhA+74CrFYlP7vMGBiC+rQnmDGuOAwz41lhfDG1mh8bGumhnpofw4V2xNZJAoM/fEz0VB1j7hRWOZYlWkNZF1kpqhx0xM7Exyh0baR+3zPLFXtrHPcne2DnLE3PdHbA4aAZ2pMQQVAGYP3OiahKu8B3Pz53J9BKE86zhl0vi1WIaaqSxmrTqi6u5QWqG9DPaR5nr+Lg8So1aPpdGxkhxxfGkaTjK42WZxDKH6aIgBgczwrEpzhebI72x0G0sgTYWK2VFNzqUTZHuODzLD4djZ2BbwBhs8JIBrENR4TpYrdG4LWQCtYIzfopxwh6mhV1+Q7GdYFgzczCB0hflE7ujcGxXgssKjerpwKxWbXSzNMCaiBk4Sndyguw5f2R7plsvVD29+0mUV1Z9/Hr6Gqp++Q8+PL2Pw7xpqd1ssXhsR5yiYr9YFI8ZfTqjoXEDWiEDxDn0xg5qhF1RzthNK3ZglpsCwTEZqEqbeDg7iK4hgDYyENtSArAlNQC70gJVO8JxUvshgmZPijeOF8ThfHEyQRSK7dHu2JPoif0p1BcZwTiTF0YgROCaTI2j7ZQV1WRo2SUC4VpeMO7OidKMTlIlVgHhbJIrTlDlX0j3wAV5P63qlcJwnKaDOZUTjhOyYktaEA4wIMcIgNPpQThHsF0i2C4VxdBueuOINJBFUv+EOGJb4DhspybYGUF2kqFrkhZ8h5ERhmGD50Csch+EkrFdWDohf0wXTCLLNNGtB7Pvv0X4yL64UByHw3HT8RPZpMyhE44vzCHlvvlkISvxldnHL59/ePEIJ4rikEPlvJQ58mq6Fx4szEAo/7DmZoZoaaJPRuiObcIILMII++nFjzLAAoKTRZE4KuMUCyNxPD8ch3LDcCgvXPVGnuCNP5sRSCcRgNO0m+cLItVqKVdk4gtz+mkZ2UQmOJMTgRulibhTJn0IsbhBIFyjDb3BwMpchBsFYbg3J1bpgifl8Wo8432Z4cy0cCLCCed4828xfVxnuVkgi2dE44Is3JVKa8kUc4JsdSLeHRdSZNGMMAItEpdnR/K6AnGCn3E2IwBn6IROqnTkou2CnqKGuG8JGIkN3kOx2r0/lrn2x4IpfTCbQMh26IyJZISmunWod77DYp8puDU3AUdZSXYEkoGYIq/vWPX5vitt8Junhl/LT9o+hjfPcXp+BoqHtceC0e1xLdsXT5Zko9htErqYG6CjuR6Ch3bBhtBJ2BYllsoJe+NcVDo4yQCdJo2fLY7H+ZIEnC6UuY7hrPWk58IoMksMrpXIqigUdgzy7bnxavSyDCe7ykBcIXiuFkTx9Vm4Py8ZD2Uwa7msoBKJWyXSfCzjDENxi4F9vCAZTxck4fG8ODwiO8go5xvSJ0HHcDJiCq6Qde4UBhMg4XjA35Vh8jdKCLDZTBeyXhM/50puKIEVjItkmEsE29XiKFyfLQt08JpktnZJNC7mSwunL05RAMvinbvJDhuYFla4DcCSaX2xcEpvlIzrjPRRHSkWTdCIIOhNHbUvzg93+XdfpG7ZFTIJ6/0c8fjkT1+w8AdtO8JvCoTKv43Gn1/j+lparWmDMY9AuMGb+XJ5LrZF+2KItQF62xjCe0AHVEi7uyxrSyDsiqFOoK07XRSOiwzyxTmJOD87EWcZ+PNF0QRBJC6VxuBqeSJuymIYC5PxaFEqHi9Jwx0+vyGTYUvjcLuENVvV9ESWWWqms5rsMjeaYIjFNX7OFbLMzRKZ1pakgPBCJsouSMD94lBczQ/AlSwfnJ3lirPJrmp1tXtMGTLiWZbcuTtXJr8SUBJs/v69BSma6fiLZuHeogTcm5+oPveBzM5emKQm5V4r0TCW9HyeoSWWZX53hIzCep9h1BADUT6pK0rHEwhkhFG0jy3r1YRrp5Z8nw8upfrhMgG3I4hWM8INLy+d1ABBiXPZPvDD19vpJCh9sH8DVng5oGJKL6I6HPdYk/bHe2BsE3N0NNGDc7e2WOg9EptCKewiJuEnisXDmT44w/deKaO4m5eES3NSCIgEXCADXGLNuFk2C3fna6a+35EAs9xmuUpavi4B5o2+zCDK3IVHpeGaCSylsqReDMFIRiiKUu+7QWDdJWgezI3DM6aGFwzecwLnIVPMTda+y+m+OJ/miYvUCTdy/XCP4HhORnlKZnjM1HKXwL6ZJ+srBaoi/Ra3tTOqZNb0A37nQzLMQ1lYoyxWTbq5mBuoWQQ03QenZ83A/tAJaiWVpTMGYi6F4mzqg6SRnTHE3gjNa/8J/j1a4EDYFBwlM13MCsT24AnYEOGC5+ePQdsH/YUuq/xKgcCf52d+wkq/idjoMRz3iiNYu9zVgA/Pbi3RXrcWJrS1R/nMYVgfNA47ZBiXLDLB1HBGRB1zugDhGtPLtfkpZIEk1bFzk+XOvERcL5YUEMpaGa4e3yyJVrQvq6bJrGXJ9U9kJpNMdCEINK/H4ebsODJCNG4zjdydQ6cwPwnPySbP+JlPCL57TC3XmOMvpHixeOAqQaVJDaF4ysA+k1LKlEKA3Zdu7Hx/3JEFt/IC1Opr12SGlFqci4CQCTZSVEoiUHmtAoTTyR5qMY0DkY7Y4j8Ky90GYZ5jL+RTCCYM7YCBVvXRTudb+HdpjB2+Y7DPfyzB44+dTA1rAifjyZkDnxzDL1vyvlIgvL5+DhujPbHVbxxuMy8fDJtEle2JqIEdMchKD45tbej1B2IDbdXW4PE4SGdxPNNPLZp5UuYKsPbfWpKL6wvT1UjiWwvSFBBukN4vU1BeLQhRN/lOSZSahyBzD57MT8AzOoDnLE+lN5GBfrQgFXfLU8gc/P3SZJZ4pS3uEgyP+R0v+PozppBHzP+yPtJVAuFiqjfOJc5UGuEez/28MhOvZIEN0v0rMtKL+XF4MS8Gz5hynhIUT2RcQ0EQ7uYHKba4lRdIB8Lrk1VWeI1KUxSEsmb74ZTMlI52UkvwbfAaiqUuAzCXQMihPoge2BojbfQw0FQHIV0bY63rIGydORRnaJV/inDGMrfhuLNnParHJ/2jF+3+pwDh5/s3sD0ljKifiBs5QdgXMBb7Qp2QMbI7xjY0xJS2liia1p+CcSK2UyccpOU6Tko+y9p0jjftqix+sSQHt5fm4t7yfAIhQy2uebmAeVpqLwP3gOUJheDzBYnM8yzM02+XpeINdcPLxal4tSwbb1YX49GyAtwqJ5jmptNFJFMzMA2QcZ6VpeBleRqDmsS0EMGUEkaqp5gkfZ+RlsV4N1yg6r9PhnpdkYXXy7LwhuXtsjS85Xe8WURg0HG8KIvGc7LFkxKmIwLgLlOFTJ+7LdPoeO4WizRgCSMcp/aQ6XCywvs6z6GocBuM2RO7IWd0B8QMaIlJTYwxyd4ASf1bosK5FzZRUJ6Kn4GTdCgr3Ubi9PJSyoK3f5EevmIggBZyf34igTABt/NDcCCIOdF9BIrH94FjYyO4tLdD6YzB2BQ+GTsiJqtu3hO0j2fJBldE3ZcLI2ThXkUB7izLww2miUuz43GTlP5oUQqeLJjF2p+I14uTGfgUvFmagncVaXi/IoMlB+9X5eHNykK8WDEbl0uSCa54AkncA50EP+vFQtZyHt/R1r4mMzxj+nhAd3KV1vRcAgVdKO1esCNz9DQcipiKc6Tnd6vygc1lqNo4B+/XFOJtRTZeL0ojM8TiBVnpxRzqiNIIPKGoFAciC2vcIFNczdcMdBEgSO/nkfip2BM5Hpt9R2Gl+1DMceyJAtps2XZoWjMTeLYwx/yJvbHMsTs2EgjHaK/PkE3Xeo3FsUVFSoxrFePvAAhvX+J4eQ42+o4lVYbgSPgUrHUeiHkT+8KDbODToznK3IdhI1OGAGE/ffspAQKp+VJhGDUBXcCSDNxenoe7ZIT7rNUPl+bjydJsvKzIUWsgvWAQ3i7NxM8rsvBxVTYqWapW56FybRF+XlWAJ4uycbkwgUwTjiMpQSzM43Qir1eU4OeK2XhFlnlN/fGG7POC4vFxAfVEVgjOUNQeIAgOhUzByWhql/Bp2MfHl7JD8X79HGDXUnzcUo6f1xXhDa/j9WINO7yh83jF8lzWcSRL3JZFwJkmruQGKBCcS/fG0YTp2Bs+EbvCx2MLXdMKtyGY59wHeeM6IWVEewR0tMesvi2xeFIvLJ5Ai+02EIfpqk4leGKNzwRc2lyhhrF/Eolf81A1TW54jbMVc7DSYxTtmD9zrgdWTu6LpY79kDioHYJ6t8JCjxEKCNtke53Y6TiWRttG5X+JqUFG/9xYyNy+NAcPVxTh+epSNfvoFan+7ZrZrJ0FeLcijzU/Hx9W5zP4BcDaQmDdbFStZTooT6ZVC8bJ1GCcy4rGST4+muyLGyVJBFIxgVKK92SbtwTba1rA50wXT4qpH/IjcSHZB0cjp+OcdFSlB+Byih9Ox3kwIK4MariaF4Gdi4Ft8/GeYHgnDMTU8W5pGl4tSsZLmUonI6UpEm9Ry1zJ8VeTY0+IM4qdquY4bKNIlv2lKtyHoMypF/IcOiBzVCdE9W6OzEFtUDqCroossdV7BM4kuVMjTMdG2sfnFz5Pja/8YqvErxcIH97j1tZVWOHtgKPxLrhGC7Rp+hAsmdCTf3RXJA7vgqU+DtgUJu0Ik7E7lhRMRX2SgvFCbpDqKbw8N56ugVS+PBcvCIA36+fi7bo5eMdAvGf5yJr/YXUhKtcUEQiFqCIIKlnulybgUNQMHI7xoJZIoWjMofen82BqeTg/k0AgY6yfB+xYig+s4a+pQz6sLSG4Cika6fszg5V/v5kdQocQjwe0rjfIBidj3LDDawyu8PwHfje2Mk1sKkXVhmJUMVV8ICBek6FEm7yiyH0wO5ouIli1S1xIm4ljsVNwUPaWkokvAeOwzmc0KjyGYe6k7nQNHZA/pgfi+7dGWBsT5PdvhI0zhuIERfRJ6oq1rFAH8uNQ9ezBJ6PwqV2x6it2DXKhz4/uxhbS6jrP4UqEnYp0wZKxXZE1pA1SR3XDqgDmSWqE7bKqSKwz9tNCHqNSP08qvVIUwdweg+vSMsgb+2xlAdlAxg3MxccNc5in5wLM1ZUM4EcC4f2aAqr7PFrGRByg397iPQ5XcyLxbjlBsqaM+qEYTxfk4NHCLDxbloufN88HDm4gxS/B65Vkh80Exq5FeDyPzoK1/nFJArVGHj7yM9+S/h/KeMUkH6x3GohdPmNJ/Qn4uK5QAQEbWdaVEJS8vtVFeMvjOzLZC1rfOxSglzO9cT6VKUbWXyQb7KRe2sq/fa2vA5bTFQgQigiEAgIhkUCI6mSG5Y49cITa5HSSB98/hkJxOC7yOvH+1V8Aoeor64b+FSS8uXkBx3LjsGxiTxyJnIIb+VHY6jsFcyb2wWznHlgXOgHbZUwfc+bumCk4QOQfTfVQ/QiXC8NxXeYfzEvAAzoAGT30hjT8cfNcVG5hTaRowyaCYUOJSg0/Uxy+pXa4lh2MrR6jsY3C9G5eKH5elq5o+8OqHL6eRTDQMlJsvl2eQV2Rx4BlUmek45XMiub33C+mymfwpMXxDX9XWdG50bhD53Mq2hXryWp76O2vM4W9ojb4SCaoWj9bsUIlAVHJa/pAkL6lUH25JJPATMD13GACgamFf+PO4HFqltOmgDHYyM9ZQSDMY2ooHNsZheO6IWlgC8yhPjhBC3uAzuVI1DRsmDEIK3zG486B7VAtidV3+B9sHP5pQKh88RDXV5Rhk+doLBzfBYdi3KmgE3EqKxxLvIcpIGylENoaRvEUReeQOF2NSjqV6a+0wpUiaTCKUiuuP6ReeE0h+GFjCbBlHoHAGszaWLmuSAHhPV8TAXktNxR7AyZip6cDzs+aiTuy5hJv6r4wMg/z8lZ/B1ykKJU8/nrhLDyfF0sbSftXHqvWPzgtXdyhk7HcdTCWTRuINTOGYBdp/GgkU1f4dLINdUJ2EG4VRiiWeLs0g5qE6WldAcEwm9dEMbm5nNqhGC+oP55QiN7IoU5J8eLf74w91EMbAxzUFoLr/UZjJYEw36k3Zk/oShvZA1kj22EJ3dQ5/s6+6OnY5DEcCyd0w+Yodzy/fPoz3VYf/oEbhf7zpsV/fIv7uzfi4CxflPAPrJg6EDeKEpn/E7FU9lGUwGiBsDvaCT9RUUsL48kMX5yTCSYFms6di7khqnPplYgyBr5qM282WeEj9cDPwhQEwFuZv7g4XfU1XMsNw3lZqJPUeiB8KpZO7oeS0V2xaMpAJPVqisIRHXGR7uApHcOjObF4ROZ5yHIiYSbWeo5D6bjeWOHhgIMJ3qhwHY7VM0bgeLwXrz0e98vS8ZRu5T5r+p2iaNUq+Z7s8oGO5SNZR7SCsJTYy5dLNUAQ13SWFH+Q7LczdCLWeo/EKgZ4oy8pf+YwzHfsg5Lx3TCbJW9MZ6zkvZE5FNuCxmPJ5F6YO6YrdiQH4O3da9r4V33R6/+Pm9nwT1w6pwovr5zGKQq1tXQICyf1xMmUAFwojMa6oElYFyKpYQq20T4KEA7QY++Lm6ZGEp0nEC7mh+AaWeFmsSxXE6cWtXjHmv+eDuFnssC7lTmqxU+o/tmiVNXTKB1RLwmMV0uzcJ/C71xaIPaFimVzJajicZOuYV+0O/ZFudJV+NISSl9BGFW9J9bzGneFyaag/rhSnIAzWTyfKWswy2qtuXi7qoRCs0B1oL2Uzycj3J8dqSzjuyXJTDOp+JmgqFyV/wmgj8vimUaCcDqRyp+g3xFCIHiNxJJpAxQQlH2c3Fulg+KJvVAwrisWuw7CjlBHbCBg5oxqj6IRHbA1KRDvH935JRDUvx+/Ykb4oh+k6uVjXK4owZbAiVgwsRu2BU/GTubalf7jsTHcEdsUEBwJhCmqLeGnOAaC7uEUWeFCnmzIFaqmpT1Qy9kwb/NGv5UVTpZl4sXiZDxboGlSvl8qHTx8zPNvBCQUj29X5uP5YgFEClNSLM4yJd2am4J78zNwNEmTAvZEyBiBaUwbE7GeDucQbeIVAvUGncd1sZPzUvBoAVlgcSZer8jHG4rWt7SuP/Pzn5MZbuQF48ncKLqEBLxZwnSjtAavsSKDAE0m48TgKlPd8VgX7I9w4n0Yj9UE3CLn/thAsbjSbSjKCIBSAqGE9rpoQnfVwCQ7zq6no8gb0BglBMOO1CD8/Oj257Rb3YDwDxyT8M8BwkctSn9+jUurylTDUunI1vwDR9Ih0A4REFuipmAHffVOGcQa5Yx9MdNUC+PBRDcFhIt50ioXjJtFsphVFKk8jiUeLxenaITcgkQ8kwUzZb8m6WgSoJAx3jFn/0x7947W8rUsn7Mkl0yRi9vzZNZzLh4vycOtOUk4nx2Gk6kBODrLB8dS/HGC7HGbLPJkCWsyVf99gujO/HQ8XJLD38nBS3EutJivV2m/g8fb1BWy8Kas4ficYHjJ4L8gK8m1PZ0ng12Y3miJj8e5YE+YI++DA9YQCCvdh6lgL3cZjHKmruLxPRUQZk/ohXlT+mD1TDLF2A7I7GmJldP64EBeBN7cu/YLjfCP3r/hn5AaxNto5zy8f4OrGxaz9jmhYFBj5ule2BM3gyCYpoCwK366AsJuNUrJBT/Fu2IHmUIGjsrStjIR9WpegNo8Q1ZAe0QFL4CQvoUnZbGqTV98ujDGC4LjpaQLBugVbd+rVRK42XiyvBAPl+bhPoP5RKh9ZRGeLxdwpLPGyyYfSbi/KEMF/j5r/72FPL8wE9fmpeHK3DRcX5CF24tzcX9ZPh6vKMSzFRoH855O4QmdxjXqmFsM+OPqfg+WJ7zOh7KEb2HIZyDQKm+h8JSymY5hvfdoapAhKHfsy7RAwUggCBgWTh+ACpf+TAttUDyoiWpUOjFnFt4+uKm5vZVVX0rGrxkIX1zlh7e4vX0lDsS5o3x0e6aH7tgexnQQPQ1bGfzd8S7YGTNVDUzZqwWCDF07mOCiZgpdzPHDlTw/TZ8/i9SwB6XUDLR5twuDVUONDEaV/ofny7NZe9MZbFn0go+Z15+smI2HFUV4SEBIkQmrTwmEZ8vzVc1/soiWku97rlovS/h7uXggfRwExaU5yTjPtHK5PBMX5qar3tB7Ffw9WsNnq4vwYm0xPytfDT65Wih7RUWqpfuky1pY6r7sGcX0dindi2JTA4StTA07QiYpMEhqWEV2WEARXerUH0UCBKe+1As9sWRqX6whGDa49sNO6ooTBGbly8d/BYSqrx0In7JW5Ts82r8eF7KCsY1KebnzAOyKmI5DFGrbY6dhF3XBbpZdfLyPIDigLUeSZP6BDy5ma4BwJccHN/P81RK5ah1kacPP9sHJBGdcZgq5Q6F4b2Eq7jJvPyCt32OA7yzMxl3psGIAr0iXNoN9d2UxzxWQAbKZ97NV6njJ4D4TcKwqwiOKwAfM8fdp/W4uIiuQDa4syME1ppTbAgTV91GgQKF6Rgkc2SrgEoWtdGM/Kg7FI1mss4jXxOu8muFNxzADR6VpmUCQpuXtweNVkbaEVdQBCygc5/K+FDFFzKWzWelBMenUB6uce2EHbfY+VpqLa+mUPrzRGoWqL2Ti15waqjQLPVV7nNdXzuHKonycSvPDvnAnNVP5TFEkGWGKalDaFccSQ9cQPwPHqA2OpHrgWPIMnKbfv5Dtq1nXIMsb11jzb5Ih7sjIIFKuzGeUhSzP5YeSxmcxcBm4xuBdWcjALc7H9cUFuEptcGNJIa4vLcBtssP9lbNZ27OpFQiY8mQ8IXgey3Cz+QRR2SzcYM2+QScgw9puyGCWshTcJCNcX5jFz8vFNQLgElPGBZ6/TLBdJ3tcIhBk4KzsNCtM8GB2AO7Iwt2y9XDqTDoGFwWEfZGTyQYTlHPYHTJZCWgZxTWXjFA8dQByyQjFUwfjpyRPNRNb1mfcFu2CA0Wz8OrqSXUvP6C6j6FKO5z9K54NrVRt1Qc1I1qt5/D6Fe7u2YjzVOxXimWeQQIOZYZhXeQUbKJq3xY7RQFhv3Q8ybQ32QRDlrXN9mdtD1R7IVzhucvpnrhBPXCbmuFqbiAOUmvsj3dX6zGe42dens+cvlCWv8sg8AgABv9ORTHpvAT3lxaRKcgEkusXZODOnERqj3Bc5e/KCKBLGUw9RTG4xXKzKB63S2Ypp3ElPw63qROulCTjKo+XytJwRi33F4ezZUm4REBdpsY4mxfGNBaoxiDcLvRjSvDD9SwNEM5R7xyROQ1RTthLC7kvShqWKJSpm1b6jEXJlAEomNyHQOiHgqlDsSqY70kmGFhxTvKan189y+i/V7X/oxYI1ZNfv+5V1fjfe2L3w6emDl7ws7u4d3gbLm9egrt71+PlmUPYVzwLK0KnUDQ6MT1MVWsKCCOcoUCU3d1ll3e1aols8C2be2d4Md964mq27K7mj4NkkMMp3jiYHoQTBbEqQLIp6GUGWcodWj4ReHeX5uPRskI8WERaL2f+n5+tKfMyGfAk3GXQn9MdPCtPx/3ybNwsTcf1EorEklRcIBAu5MjE2DDaWdl1Nh5Hs0PUepDHZFlg2kxZFvhUbghOppG18jS7yMoq7reYymS425lZM3CEf9+BGFlvwVEDBFaArQRChc8YzKaVzJ/UBzmOQ7C9MBVHV83DyRVz8PD4btrvhyoJfBp7UFXdRiN39gO+6Hr6SjXCF4tlfL7w9zLWXXvxwLV9m7E8wg0b6BJ2xU/DT8nuOE5dcEFsY4FmZxTZIucCU8J5Ci7ZWk+AcC03gDQcpsYuHEv3w+qgKVgdTI2RzODkhOBCYRSulsTiemk8LhfG4nJRAi4WxOGSNCjNSeNrqbgxNxO3yugG5mTgbmkaxWcyGSGBgYzDmfQInEgNo3D1xY6IGdga7IzNASL0HCl6PbB/ljc2hlPsxrrjELXPkaxAnMwMxNksfwWEO0UhSs9cE0YgeE9Q+AoQZIsfSWU/xUwnM0zFpuApWOA1FnnUB9kT+yPPdRJunTiivYEfPmmtj9X3sFKrD7T3turTmISvuWXxo3a6ttCXrAcogymqPa9W9T65fhFrUkKwIcJZtSccpDY4JV22MpJYhozJ0HJZAZ2Bl/WOpePmSiZFI3377ZIoCrRQskeImpu42GscFnuMwVLPMVgfJM3W07CbQdxDoB2K9cX+SJlT4IWzacE4nRqE44n+OBrnjRPxPjjB41G+/lPwdBwIccUWL0escRuHFS5jsMJ1DOZPGoy54/qjdGw/5A/vhtRB7ZA8pD3F3QAsYSB3xLjiJAF5tUC2G47EQxk5TUdzldd6nqnhDFODgOE4y7EEN6Y0V16bM13DJMybPgx5tI9Z4/tjYbg/3j28/0lmSfnwqb5/1KQD7dqKH6sB8rW7hk8ti2oROM3ErL+0Ox/ePMdu1spVpEixkrKO0gnWfFnV7EJOMK6r2UlBamDH5SymgzRP3CQoxD7eJRBuzI7CRQLmCGl6X5Iv1oU4I2loRwR2tkfm6F6ocJ+InaHu2B/uhd1Brtju44SdPlOwz38K9vsxD/tMxkE/0rSnI3a4SUcVX/d3pW+fjnVuU7DB3Vkdl0wZQxAMRuqALojr2Rbh3Zsgc2R3BnEkFriOwI7IaTiXKW5GhtFH4aEMZhVWyBXr6K2AcIxAP0ZnJPMYD8S6avSB5wQUOw9BxuTeSCXYNhZlKSaonjFS+anOVGmB8F6lg2qv8MW8468YCNoL/KgSglYrVHebfuoxq8T5XWuxjLV3Y6Qz9vKGHWeePaMdsnZ9dgSuF4RQGFIc5mjy7U2mC2lLeKT2eY5UtfA88/NpMsOx7FDsiPfGnOmjkD66Lxa6yIYZbjgQ6YdDEQHY6++JbW4Ubd4uOBFIMRrCVBMRiMtRobgUFYFr8TG4lhiL26lJuJ7IdBIThaPB/tjpPQPLpozGbH7mPMehWMc0dCQjHIfTQrEn1hN7omRJHnda2gDcl8GrTA8yqlnmPlwkEGQo/wlaZGlmPko22Bs1HRvpGBa5jUHh5MHImjQAKVMdcGLbxk91qOrLFdOqqpWXZvRyVbVeqEbKVz1Cqao6i2l7xyT4lZp0UUlUf9T2qz++fhYrkgKwKswZ26WfIcUT56R5WQZ85st2egQCb6wA4ToBIRNOZPPuB7Lp9+xQ3CwMVTOBpNtWJr2eo5g7TlG3NcoLGwLdcDghnHoiA7cKMnEvLws30ugGEqNxNT4S91Li8TI3Ey+yM/GmIB+v8/LwMisLz7L43pRkBYhTkYHYPHMSysf3w8Ipg7AxyJEawQen8yJxkd91XvaiTPXCadpd2exDHM0tXuttWtyr2ZqJMqcJhJMyb4NAkHUXdoSRDbzHYs60och37IdcxwEoJBM9unFZe+8+aMYlVn4Wgp/Z9AtdoF2G96vVCFWfjc4Xoyc+w1t2gKveBe7j2xfYWZ6LZaT1TaTYn5hDz6Z7MLDe2vkLYaq/QdZJvEK3IFPQ7hQFUNwFq422ZF6lrHAi8xCl2/piIZU8beHhlEDWVh/sjQ3AT3GhOJlC8ZifgUfziugOivGoOB9PZufhVWkRXpfk431pIV4X5uJZfg5elBThfmEOzqbEYXewJ1bMGIcl00dgrdd47Ah3UU7lIgXpjbm0oExPp9M81F4NtwpoGwnUG9QzIhJlv6dTKW44qrYKnoZDTH97Qh2xlpZxkdtI5E8diPwp/ZE1oT/WZCfRIWqHqYtQrKyuRB9V60HlLxjicyXTEMJX2vtYVc0Ef0VvX+CiUuuH+XP10E4KpRlYGTqVotFN9TOcTHWnewhWE1lELKq2BFpKCf5tguEuj5fS6NFT3dTs5msF4bhWFKEGslzm8Xi6P/bGyKpmHtgX44N9BMTh5HCcy5uFJ8vm4s2qxXi+ZC4ezinAg5JcPJ2TTzrPIEhK8GpJOc5nJtElzMR632lY4z0Z6/0mYUuQtHXwughQGVZ/pyyR1xWEsxSx51JcKQ49cDPHB1ek2Zsi8TwdzrHE6cotHI2drno51/tPIAhGoITskkWRmO3YH5lOI3Fq66bPGbWq8oul8yoVEKr+AghVX+iIr3yE0v8LsGj/gndP7mJ1TgwW8EavjXChlSSlJ83EaQrEy+IYCICL2V48eitvfocscEvaFXjDz2fNxFXZh1GmvpXGMkBxuDk3TrOmAcGwn4p+Vzj1R7Qb9hAUPyXQ12dHEzQpuFaYTLZJwq3idArPNH5HPNkollYvGLtDZmKL3xRs9nPERr9x2BI8AYdmuTEVBKnvkRnP14qYHrKoA9JobVPccT7JhUcXXEl3IxA81KjlQ3HOaiOPQwTBjhAnLPUagxKX4cib1F+1JGbxWBLsgSe3rn/WVlWa1Fn5D57A8lUC4ZcW4iOOrF2EMvr0ZYGTsCGcFEp1LTdeaPdCljdVuac6Sk/kdTKAUK/ssnZJUoj0QdBF3CtPUPs+aqakExBzYnGRKeNgvDvdwxRsDZpMtT6FwKCPp63cH07ajvHGqVkBpG4v7At1wVbfydjsPYFlHFax5m70kW14ZBmdmWqY/VWZ+k4AnKcmOZ8t+z6yZGpAeZa1/2ziVJxLno6T8TKsTXZxoaaQUVh+47HG0wHlLsNQ6MSUQOtZIOszTeyPraXZquWw+r5onNbHf+DKB181EDT5oRoL984fx0LW2AW+47E8gFTMWiwjlw7z5p7O8MRJycMZPmrlk5sy2KMwBBfpIK7n+6s1E8Vq3pONvgmGWwoMcWqK+oMFyaoGn870V503W4Mm0L+PxeqZo7DSdRhW0/6tYcBXuQ3HUuf+qJBxim5DCQQH1ehzSqs/ZLHua2Qdabs4RwCczQpUFvcKxaxol0vUNacSphIAzmq08k+ya0voROwKk67n8dQXDlg4dQiKnfqjYHJ/FDoOROEUWsepo3B+/7a/qiCV/51U8JsC4bOSUM8q373Gzvl5KPNjDmXtqQicjA0h9PaRkyn2puLwLE26OCL2sjBMzY+8RGdxNddPtTNc4zm152N5/Kcp8wKKe7JGgmzStTSdxzRcKY5Ry+b9FO+B3UxDO2ldtwbKyKHJfD6VNO6Kk0meTDmBTDexqiPq1pwE1QEl8y7PZdOdZAYSnLSz1CK3ZLlf2RuaQJD1FE4QCIcJhAMyVyN0khqpvNprFBa5yNS2AQoI+TzOJiiyJw3G/OgAvPs0V+Fze2HVfz8OfisgVI/Jr/z0V18/th/zqMoX+I8jK0zAmsAJ2Bg8HtvCJjHHT8Nu5tmyqf2Q4dAB68MmkqqDVRuDrIsgYxtvzYlVK6dIWpDjvfJEBYRHi9PwtCIHj5fl4P6SLNyan6q29ZFBKDdLZ+FyYQyZJpYsMgv3ymSl9lQySara2OshwXNnHsEgzdUFkbiQG6rSwoXcEAUEaQq/RM1yMctT7f14lJpgX8RENWxdxh6slLGaLkNQynRQTGFYRBAUUCgWTR+GNMfhOLJu+edqUYlPQPgt0PAbAkGjD6onaLx/9RwbilMwl4ywjDdxOUXaalnEOmg8NgeRZiOdscR9KNJHtSNIxuIUc7NoBtly7yqBcKcsHrfVyimxWiDMUvtBPpJh5cuy1I5vr9YU4cWqAjxfkYsXy/PUTm53GHwJ/OPF6WrDL5lb+Wp1Pl6upJ2syFJAks0+rjI1XOb3CAhkwe+LeSGqx1HGTJyjQzg+y0Xt7ibD5jf4jsKqmcOxaPogzJs6CKXOgzCbmmA2QVDiMgLpk6XtwB0v793UeoNKTTvL5ybZXyxu/i8NhKpqOHwxq/fy4V2YEzJVaYVl/hOx3Hu0AsP6gLHYSHBsk8EdaqkdevPkmZoRTDKtrFCjHdSaSgTEo8Wpapuex9qtel6uzMMLBvitTJBdX4LXqxjoCtpGBl/GIzxlsGXbvxdq6788vFlbgLfrCvFydR5/PxsPF6eoVdkv0s7Kiu+nMxl8WcyLwvUcASl29wCvaQsBKsvprZg5DEsJgkXThmDe9MEEQD+UOA9EGUEwe/oIJE8YgJ2Ly7Q66YOmXeWLthb8NzuG3xgIVV+2j6ifdy8fY11+Asp8mB78J2MlU8RKAmGlz0is9R1NZhiPrSETsJOA2BM1BUeTZqju6itME9dLIhUYRB88YuCeLc/Ec9ZoNYZxtUyRz8cLGdks2/5VyLjGfLyW4zICYEkm3qzIU/s8qfcTKK/XFOLnTaV4s2429UWqsqayspqA4GS6jxpge4rHo7NmYA81wVYywSrP4VhO1lruOQLLZgzHYlrFuWSDIqc+BMIAzGVKyHYcjIKZ4/Hk2nntjXivaUD6ZBOEIT/gvxsJvxkQPu87UP1YqxWO7sG84GlYSAu3kop7tWyw7TNKzRXcIGDwG61uugKE7KeYMF1twCmrl10rjiAQYkn1iXi8JJmBz1YLbr9aM5spoRjPV8lRFuEWVihiKmCtJxieL8tWx9cyXJ3p453MZdw0D+83lqmRy4+XZlNDJOHa7GhlI2X0lCyjJ41fe5gONgU4YA3BuooAWOE1Wu0Mv3TGMJQxHYg2KCUbSJktk2zGD8D6nHg1nlPTnvLhLxrdPnU+/xu0I+CLFWSrqptStSu3vnuJNYUpKPMeiyU+owkGpgjqhpV+Y7FB7XUwCuv9x2CtTC0PHIMttGj7Et3VDCnpo7hRQitZFqWW2H2yJFWlhdcEgoxolom0P6+fy1KmRjPLln8vKvK1DFGAd2tkom0pPhAAlZvm83kJnlFgysSZO0w7koLOyzLBqV50Mu7Yy3SwPtCBDDCYYB2BtbzGNb4TsMKdItGVIlEGpk4dTDYYrHRCPjVCgvMoHN+9FV/bz2+rESo1/1T9xVDMk9vXothrEhZ6jsYyn7GoIBBWUyOsIxtsYM3bILYsYAzW+I/iuZHYETFJNfqcyfJXvv8afb/oBWlLkFXXZcvf12vy8YZ0L9Pq364pZhoooC4oUuXZYgrDJWQPCknZIFRel8ksL8kEj2S4O52ILNZ5LsNb7QYvk3Bk5djVvI5F7kOw1G0Q1jCFrSF7VXg7YInbCCxyG4Z5rkMVCEQkChtkTOyHXL/peH73xi+U0v8Aobp/Wts3Ue2YHl29gHlRvmp3liVeY7CM4nEFgSCrsK2XFCGPmRo2BpEZfFkTZTOMcEcclKbpnCCcyw1WzcyySOYjWedoWTprfbaaP/mGOuDZ4gw1cFWmrr2UIW1zEtUUObUAF92GrNH4aB7tZ0mMWgxMZjSfSfPECdWe4UHB6oylMt5w2iAsZO1f6ePAlDCKrDVarR+5YMYIzHcdQU0wRLUgSmtiAZ1C8tjeKA73wdsXT/6HEap/1GCVL9YL/HLJuKqfX2N1UQbmeDI9eI/BIjLDYu9RtI3jsSl4ktqZZWPIRGwOHqvAsTqIopK1cx3P7SFln6Clk3UWpJ1B1li+L03QsjwOg/tMdpCXTcLLZNd4nqPAlAEl92SepQyNLwxl8ENwQ6yh6kfwwtlkT7UdkAy13xo5DYs8RqLAkU6AjkAWAa9gGpNFQZZ5j+S1Dqc7GK5SQv7EPsglC+Q5DkDW+H5IcOiN/BBPvH32+H+A8CUjVP1i4ciP2vYlDUMcYXqY6++IxWSERTPlBlOAUTOsD5qMLaFO2Bw2iS5iLLZETMbmaGesCZmkaunKoEnYHuOCwwzeqTQPXMryxJUsL9xUC2QGqbEC94pCcb9QlsULxV0JfnYAbmT64mqWDIDxU/0HatBpjDNOyDD7eDcciHXBluCJWDhzFPIm9sYcssFSH7IV2WApQbBI5jTOHI6FbsM1Yw0IguxxPZA9gceJfXkkI4wfBP8xQ7F97Zpf3ouq3z5F/MZAwGcgyJLzLB+rNINW7l09j9JAcQ/jsdjTAQtZCxfIvEHqhY2hDH6EI7WBE3ZEO2J77BRsj5+ODaTsFRSXqwImY1PYVLUVwD6eF5t5KsUFJ5NccJqPrzDoV+g0rlH938j0x0XS/XnW+AupHjiZ4IJD0U7YEz4R+6OnYm+07AI/jelnAspdhyFvUj8l/Fb4jsNSuoTFXgy+1wiUEwTltIzl04ejcGJ/ZI3pjpxxPZE3WRhhIHImDUbShKGImDoRM6dOxcOHj7UgqPx3B0KldlQuPtkoNdBVpOPHDyjJy0O6+2QsDpiKZQGTMN9rpFqJbTHTxAqq87XUCJvoGLZGSCOTsMAU7JZ9mGIJiPApFJeTUeE/CWtCp/OcbAk0jWU6A8tAJ8zE0UQP1a9wLs2H4CB7JLrhXLIMPXfGvijNqvHbo1yoRabyc6ZgzgwHZE8ehDmk/RX+E5VNFCBIKpBrm8frKpk6FHlj+yJzdE/kju+FQpnhTH2Q5zgImZOGIH78UKyZnY8Rg/i+vKJPbFBZWfnvCwRN8/KXg1o/34w1qzega9suCJtIbRDqjoqQKVhKHTCXrFBGRb5g5ggspyZYRb2wlkWm2G+V2dXRDGD8NOyMm44tTA9rGMRl/s6oCJqO1aGuWB/hjo2RM7E1moIvyl0NV98TrSl7o11V7Zc9pLfy8apAJ1QETCEjTUARA1wyYxTKvcTBjCPjTMAqfv8y79EEggNF7RgUEyDZE/oizYFMwGPh5P6qk2k22SNXxiY6DkO660Sc27UDU52mo0PHrrh3794nMPzWrPAbA+Gvl5U+f+4SRgwfj5aN2iLUcQIqYv2xItwFK8KmYDHz/zw6iXnMw2Xi1SUw1AxrwpwYZGemC5k1pQnmPtb4nfEzsYlBXh3hinLW6jLvyay5kzDXfTzmujCArmNYqPJZFrmPw3y+tsDHCWVeE9V2QXPcx/C7RmI508CGkMlYHzoR64MnqQ6x5b5jlX5ZQBCUTBuJfKehyBhPNphEEDgNplgcSq0wBKXTh6Fw6gikTRyCuWF+uHPqJLw9A1GnbgNkZmZpgfDb64R/2oopf/2HfTnA6vNQNrUTiZYab926j8mT3dG6VS80adIVxZl52LVwLhaSFdZEumFV2HQsZf6fR8EmzFBGm1ZOZ7E0mLU3dCpWhU/HuqgZ2Bzjju1xHtiV4IF9SV4sPtgSO1PtHL822BnLqTsWuo+m8GONJrAWuI9Uz5fMHKMWrhIHIkGXlWG3xVBrMK1soECVHeTXR7lhOdliPkE4b6YwwSgUTBuBXAY/X3oZKSLnTBuOea7UNBS55TwWT3NAstMYbJg7G/euXIGniw/q1zVAq1btcOrU2a+CFX7DlsXP7Qfy8/LVa/gGRKJ5y+5o32EAGjfuhoqFK/Dg7DksiA7CmmhPugM3rA2fiiXUDOWsjULJcxnM+V4TsCyYuZzMsSpyBtYRNBujZ2JbnCe2J3hiB8vuWd4KEPuTWQiOvWSLn5IFJJ7YkyDbAJFB4tywI2YGdsTKkLkZquygY9jCc+vCKBhDyUwh07HIbzLKPMaidMZozHYZiaLpI1DoTAZwGaZcxcKZDqos4ON5M2Sg6kjkec/A9YOHcOfSdUx3coWhvhn0GhgjPCzqP6lA//KpofKLneWrkJ07G63b9US7jv3RlkBo2qwnli9Zpxbd2L9sGRaGeWBzHGs6A7OOOXwJa+USikHJ4fNmjuNxEs85Y0nQNB6nYjlF4oowV6yJcGMtdmeKcCNLyB7TXhSVBAKL2lmen7c3wQ274hh4bTqRsiOOQIr3wEayizBRBYG2NMAZ8701IJjj5oC5LGVuo0n/ZCfWfGGXpWSKpb4TNMVnAsrIWDkzxmHv/DL8fOcxLp64CCdHZ5gYW8LEyBKNGzXH1q07/kZF+ZcEQtVfIP8zEFau2oAOnfoSCH3QpuMAtGrXH81b9cbaNbvU66/vPcTa3BSsjGGQWItll7eNdAJraevWhE7jDZ+MRWSFRb6S4ydjga8Tyn0dWZywMECAwaIVjRUEy8qgqVgf7ooNYS6K7jdFumBTlKtKKesjplOETsFKppBlgVOwgAArZynj58+ZKbphLNPReIKPR5ZF0ujlNY5OZhIqfEVETuLvOmEdmasiwBHFM5li8jPx4e4DPL/1CKcPn8YYhwkwM7WFlUUjsoIRmjdrjZSUNBw7ehwfPnz8/3VPv04gVP2aNtCuFfoXvvn06QsYPIzisE0vBYC2nYegdYeBBEV/bN1yCNpmBVw9ehAL44OxeZY/9qcEYA9r9nZS+VbStuxuspwBWMpgLPEnKPwcCQZqCS+KS6nBFIFSyr2kNk9QYrGUArHEdRRr9mi1dWAZ8/1ct1HaMlq9VjpjDF8fy/QzXv3ePAJggbeUiVjEsozAq+B3bgh1xkaW9XQ363mUNCJAW0IQLo+PxNOLl/DxxRvcuXIbh/cewaB+I2Fl3gQW5o1gZGiJBvUNoFNXF40aNqVGcsK8sgW4dOnKf8qoX/1s6CpZQ0lNxqlUjURVVR//aujV6zfvsGvPQYyf5IIWbXqiZdv+aNpmANp0HopW7QcxPQzCrh1HNXtYyWf9/A67VyxFRXwYgRCKo6l+OJzqo3aT35Hozto8nYBgHmcQVrE2ryZTLGeNXubvhMWKMSbSZUxQtXiO62gUThlGcTdYefx8loIpg6nuh6BoGq2ii7RZUAyyLPCgsPQchyWk+WUE2VI/AoCgE3u5PnwatkROx46o6dgqM5z5/ZvJLFuoT1aHe2BNUiwenj0D/PwBT+4+xJ3Lt7Br0x50ZfqztWkNM7OGBIIVDA3MYKBvCj1dYzTQNYK+nglaNG8DTw8frF27Hk+ePPurelZVLby16yT8V9nhnzfl7W8s8nPjxh3MX1iBqS4BaNdpIFq26422nQajaWumg3aD0K7LSLTtOBTtBAjbDqq/9cMHDepfP3mCLXNKsCExFAfTgnAsPQBHswNwmGV/mhdzvDvFnTu20T4K3W8kU2yOpj4g3W+g/VwX5Ki2xFlNsblSGoUY1KWk9eUMstD6Ml/ZRFQaohzVcQUdyuogJzXBdh1r+zpV6535WVP52dMZcFdsJwh2RbtQcFJXUFNsjffE2igvrE6Mwt1TmtVS37x4jUc37+H+5TtYTd3TulV32Nm3JRAaw9DQmsWSxQzGRlbUDtY8WjJlmKBOLV0Fil49+yM+PgV79x7A69dvf/12/xcnxf4TF9z8/PPu/UfsP3gMUTHp6NVvDJq37o0mDHyrjoPRtsswtOo0BM3aMR10GobOPcegaw8HtGFq2LheoxE+fqjSTAWEzJe8jk1F2dieEYljshdkTjBO5AXjdGE4jmQFqNVGdhMQuxNF8DF1RM9gbZW1mhgsmYRKJ7CX5/ew7KX43B2rebyHx51RElhXbOP7N4VLuwQLj1sipqpzO5mGdsfwfTGudBbuPLrz89xxUJa7SfamEPWldvHG+uxEPDh3SgPid1V4+eQVgXAfD67dw+zcuWjSuD0aNmoPc7OmMDCwZrGCkYEFjIxsKCBtCQY7mLIYESSmJrZ83RK6umawsG6K4SPGobBoDi5cuPRXFe2rA0L1z9179zBv/mKMI/03atYFJhZt0ah5P2qAkWjZcThadx5JEIxA8w5D0LTtALTsMBQdyAjdeoxC+/YDUbFcO0P4o3Z3O+3aCg8uncfWgjTsSw/GqexQnMoNxem8MBzPCsTx7CAczRQd4UUd4YE9SXL0xE8pnjjEcwcoNg8xaGozzzRfnEj3xdEU2ajTA4dZqw/SSh6aNZMBdlVFlgPcRYbZTRexJ9YD+wQwZJtdMR44mh6E4xmBOMjP3Jvkjc2xntiXHYMnNy5pbsDHKrx78R6vH77F45uPcfv6XcRGz0JjpoXG9gSCeVPFCPr6ZAQBg6ENAUEgGNmz2JEZ7MgaTWBj3Qp2dm1hadMSRqYNYUKh2a5tV7hMm4nFi5bh5s1b/wCNIDdXO1P512bTVX3RDFz1K6qw6i9m475/T7t38AwiotPRrecwmPOPNrZoCVOrdrBt0hOtCYBWHYajTSeCocMwAmIomrUdxNRAt0BG6NhtNBlhDNoTHHNLlqrL+lCpmTxbVVk92pcgu3AO6wuzsTszHKdzw3ChIBLnCIgTMu8gV0ARhIMpftgv2wQTCIeoKY7x3NEMWWnFH2cJGM3QdBl6FohTTDOnMwJwPF2z5fCBZAY93l0zXS5KWMMDB/h5hxN8CBRvtXOtrPkkcy0OpvhiWyLT0/xivL6rXSFV+lF+rsIHBYTXeHznKS6ev45pzp5o1rgjgdABlpbNCQBbAsFKFSMG3sSEusFAGKERzEyaEAwNKSqbws62DUs72Ni2ha1dO1jbtIWRcWOYmjZBp469ERoah/37j1J7/fwrovLLRTo1uu1z7Cq1QPiyB/DvCI4q/P0Gj/v3H6OiYj2mOHugcbOeMDanGLJqD+uGXWBp1xmW9l1I/xSBXUagZfshaNFusAJEq47DlEhsIkAgKDp2dyCAxhMIw5CckIef31ZqfcfHv9Ied69cws6STOzPCMfZ3ChcKYjARZkAUxSB8wVRuJAXgdMMtmwleCTNh+LSF4d5FGY4mupFRvBR4w9lwsrxFG8cI3vI1PyDBMEBmVDDmn4iLYAg0SyPc4JAOkw2OZLK92Z4E2y+1Cp+2EbxenLNMrx/+fzzzRIGY0w+vHyPV49e4vH9Z9i3+yj69h6Oli26oSEZwcqyBVnAniCwUcXQkCxAAEjwTSXI2iLvsbJooYAgALC2aU8wdII976mtXUdYWJIpTBqhMVPOqFGTkJtbjJOnzrJSfvzVtP1JwH9q5a38W6mh6pflF1OyP/+I5T164hxmpeah3+DxsOYfZ2LRCraN+Ic27Qnbxt0VCCxsu6Bh8z50BCPQpuMIagENGwgQhCGEEZoTGG06D0P7bqPQo/dEdO48CoF+cXj5/GftFWmQ/WmooxbVL25fVzVxd1okU0QELqiZUMG4XBipZiedzQvBmbwgPg9V+0XJfhCnZFd6lsPpki5mqg1DTqR7qwGpMjL5ZJqP2mNKRjpdKBC2CVW/dyKDYErzYDrwIqME4GBqALVKNC7v2Yaq9++1N/r953VveOnvCYTnD18oICxesAJtW3enI+jKIHaAlXVrMkBjBQIDAwGCLUHQiLpACwIezVjjjQzsyRx2sLJqpQVCO1jZCDt0oOjsRL3RGQ0bCzDawUxsqUUjtG3bDS4uXli0cAVuXL/9K4CoZgoNKP7w6yCoRop2NO1fWNX7D5+gYtUGTJvhjxZte8OMF2dGZNo26wX75n3RsFkf2DftDZvGPWDZsDvM7bqSAYaQDagJGPi2XR3Qmo9bCDModhhClhiqgNK+K1NDz7Ho2m0MJk/wwPVr2nWFtAtJfaxeW6iqWjjIxuTPcGH7RuzIjMGRDM0E1TNkgfOyDW9ukNoD4gIDdyk/GBdzA9Q6zxf4WEBzWoQm9cVppoizfO8ZCk4BwRnW/lNZ/kwjPmqRL1nN5RRZQCbWnM3xZ6rxx+HidDy+eObTffmoRl9WonogchWF4tunb/H03lPcu/UA4SFxaNa0I5o37coAdmQw2zDfN1VBNlTagGmBxUTSAgFgytQggBBw6OvZ8FwzgodAYHpQRzKDPUFg27Azj11g37Ajjx0JDILMqqVKG1bWLdCpc39Mn+6JuWWLCIo7v1rx//DrIPj1rWZPnDqP1KwiDB4xiUHuABNejEXDrgQAg04QWFMD2DbrC7umfWDDxzZNesGCILAjMNoz97fqNBwtKAhbkhXkcfXzFmSHNhSOrXlejl17jkPvPhMxYMB47Ntz7NOliUZQi0xWVQ91+/BZo/Dk7aMHsStnFhV8gNrz4BRruQTzeJq/RgcwwBLME6kysdYL5xh0mct4gq8dpW44muaj5kaeygzASeqFkxSSwg6ns/wVA5xTJQg/pQbiSFkOfr79xXR27fDbT2MxP2iA8PrJGzy++xSnj53DKN63tm16oXnzHhR/nVQwjc2awcCIrGAoboFpgEE35tGc5xUQWEy06cGIqcNSWEELBGEFK7KCTcNOBEMnBQYbqZDCEo01LGFLlrYk85iZNyZTNEX3HgMRFByFZctW4cqVG/ioXTf7D58Ew9/I/c9evsGmbbvg7RdBgdePNb+9qv12UvsZdFtVGPimvVh6wq55bwUAKdY8Z0FGaM5a3465XwLfmrW+pQBAAt9lNEEwXJU2fCwuojVdROfuY9CL6aFbjxEoLl6Mj+/xN+YDVn6eSqt96cXNazi0uARbE1lrKe5OSv5PlvzvrWr3Meb1EzI5JZU1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height=\"12\" src=\"data:image/png;base64,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\" v:shapes=\"Picture_x0020_33\" alt=\"image\"\u003e\u003c/a\u003e is a Ph.D. candidate in Information Technology at Lincoln University College, Malaysia, and holds a Master\u0026rsquo;s degree in IT and Data Science from the European International University, France, and Master Trainer from the Republic of Korea. He serves as a master trainer at the Ministry of Labor and Vocational Training and lectures part-time at several universities in Cambodia. His teaching and research interests include data science and cybersecurity.He can be contacted at [email protected]; https://orcid.org/0009-0004-3389-1951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cimg width=\"87\" height=\"77\" 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v:shapes=\"Picture_x0020_1\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 499px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSokroeurn Ang\u003c/strong\u003e \u003ca href=\"https://orcid.org/0009-0000-9746-5469\"\u003e\u003cimg width=\"12\" height=\"12\" 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v:shapes=\"Picture_x0020_1086338743\" alt=\"image\"\u003e\u003c/a\u003e \u003ca href=\"https://scholar.google.com/citations?user=s-smY-0AAAAJ\u0026hl=en\"\u003e\u003cimg width=\"12\" height=\"12\" src=\"data:image/png;base64,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\" v:shapes=\"Picture_x0020_395542845\" alt=\"image\"\u003e\u003c/a\u003e\u0026nbsp;\u003cimg width=\"12\" height=\"12\" src=\"data:image/png;base64,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\" v:shapes=\"Graphic_x0020_1181744931\" alt=\"image\"\u003e\u0026nbsp;\u003ca href=\"https://www.webofscience.com/wos/author/record/MBG-8086-2025\"\u003e\u003cimg width=\"12\" height=\"12\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABIAAAASCAYAAABWzo5XAAAAAXNSR0ICQMB9xQAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAABl0RVh0U29mdHdhcmUATWljcm9zb2Z0IE9mZmljZX/tNXEAAAM7SURBVDjLdZR7SFNRHMdv4CPJAiMisiB6aNFS+qOhohJEhkkFqT0gkoQSgkgiDOyP0ig0kZyo2WOKPXxQQo1F6nSh5SPLRzlwpSYjdXNrOudjj3vvvv3OZEOH/uDDOfwO53PPPed3DpeTk8MtBUAgISXuE32CIM447cIc9YeIcuIYsd53nq8kjHhCGAinxewUXxYMuR5k9EL9Vu/6p7fzooBZGlPwPB+1oogGo4hOLImG6jGkhDfh5I4GXDioxq3kbjTXTsI6JbBhDcAnLBNRcrfL5eqyWq3Q6XSY0I9jRGtETloPErd8RMreJpzZr0LqXhXOSlS4f7kffzQLTKYlYtwi6vgRBQaDARkZGYiPj8OJk8dRWlyBxupxyG5qkJnUjnOSRdH5iGac3t2Iawkd6FfPYtrkUNL8YCYKJ3T19fXgyOsh916u+/cE3oVfvdOokw0hM/ELksMace5As3uVV2I70fXRzA7iKBNdFEVRKC0t9UpCQ0OhUCjgG4Pfp/Dwah+JVDi1qwHpMa3Qfp1lG/aUieQk4ktKSrwiqVSK0dFRrBRmgw3P7miRJunA6/wR8A7BRelhJmqloxSLioq8ori4OJhMJqwWVqOIuuca6EfdGw46qDkmUgtUdcXFxV5RbGwsjEbjqqJvlnakd59Hh/mzJzXDRDIyOsvKyryiyMhIaAY0K0raJptxWCUF95pD8udEWPgpkdI/mOg0ifjy8nKvKGRjCKqqqpYJxub/QqYtQIRyDwKrOawl1tVxUEzWs83OY6JtJNK2tLTA39/fK8vKyoIdNvy09ODxr0dIUh9GyJsgBJBgAwn8aEVH2qLRMdVmJschJlpD3LBYLKJcLkd29m3k3cvHK+ULZPVfxz5lODa9CV5cQS2HINbWcEj9koTBBQ0E0Smn+QGeK7KJqGG/4HA4IfIiDAsTONt2ClwlhwD6elDNYhv2PhT5g3dh4vXufSf2+17anR6ZJ5r0H7Dz3XYE1qyBRLmDVpiJAWs/HC47G24nold7RjYTN2nPBqm1O0QbX6l7KsiGCsXhud/8PD/noDyr1EJ20Vd9jzzYbLatVO2prDSo3D6xUycqiEtUvOzN8vOd8x+gopZAR0FY9gAAAABJRU5ErkJggg==\" v:shapes=\"Picture_x0020_100210053\" alt=\"image\"\u003e\u003c/a\u003e is a PhD candidate in cybersecurity at Lincoln University College, Malaysia, MSC in cybersecurity at University of London, UK, Micro master in cybersecurity at RIT, USA, Certified with CISSP, CISA, CISM, ECSA, CEH, CyberOps, CCNASec, CCNA, and AWS cloud Practitioner. He can be contacted at [email protected];\u0026nbsp;https://orcid.org/0009-0000-9746-5469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cimg width=\"87\" height=\"95\" 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src=\"data:image/png;base64,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\" v:shapes=\"Picture_x0020_112613556\" alt=\"image\"\u003e\u003c/a\u003e is a Ph.D. candidate in Information Technology at Lincoln University College, Malaysia, and holds an M.Sc. in IT from the Royal University of Phnom Penh. With over a decade of part-time lecturing experience, he has taught programming, software engineering, and IT project management. He currently serves as Senior Manager of IT Audit at WB Finance, with prior roles at Phillip Bank and PRASAC MFI. His research interests include IT automation, cybersecurity, and audit technologies. He can be contacted at [email protected]; https://orcid.org/0009-0006-7383-1667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cimg width=\"87\" height=\"85\" 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alt=\"Midhunchakkaravarthy\" v:shapes=\"Picture_x0020_4\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 499px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDr. Midhunchakkaravarthy Janarthanan\u0026nbsp;\u003c/strong\u003e\u003ca href=\"https://orcid.org/0000-0002-0107-885X\"\u003e\u003cimg width=\"12\" height=\"12\" 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v:shapes=\"Picture_x0020_19\" alt=\"image\"\u003e\u003c/a\u003e \u003ca href=\"https://scholar.google.com/citations?user=YE4yYWwAAAAJ\u0026hl=en\"\u003e\u003cimg width=\"12\" height=\"12\" 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v:shapes=\"Picture_x0020_18\" alt=\"image\"\u003e\u003c/a\u003e \u003ca href=\"https://www.scopus.com/authid/detail.uri?authorId=57188637698\"\u003e\u003cimg width=\"12\" height=\"12\" src=\"data:image/png;base64,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\" v:shapes=\"Graphic_x0020_17\" alt=\"image\"\u003e\u003c/a\u003e \u003ca href=\"https://www.webofscience.com/wos/author/record/1692073\"\u003e\u003cimg width=\"12\" height=\"12\" src=\"data:image/png;base64,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\" v:shapes=\"Picture_x0020_16\" alt=\"image\"\u003e\u003c/a\u003e is the Dean of the School of AI Computing and Multimedia at Lincoln University College, Malaysia. He holds a Ph.D. in Computer Science and has published widely in Big Data, Web Text Mining, Machine Learning, and GPU Computing, with over 1,000 Google Scholar citations. He also supervises postgraduate research in AI and cloud-based systems. He can be contacted at [email protected]; https://orcid.org/0000-0002-0107-885X\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbedin B, Meske C, Junglas I, Rabhi F, Motahari-Nezhad HR. Designing and managing human\u0026ndash;AI interactions. Inform Syst Front. 2022;24:691\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10796-022-10313-1\u003c/span\u003e\u003cspan address=\"10.1007/s10796-022-10313-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbuhassna H, Alnawajha S. 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Systematic review of research on artificial intelligence applications in higher education \u0026ndash; Where are the educators? Int J Educational Technol High Educ. 2019;16., Article 39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s41239-019-0171-0\u003c/span\u003e\u003cspan address=\"10.1186/s41239-019-0171-0\" 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":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Generative artificial intelligence, Human-in-the-loop, Instructional workflow, Course design and assessment, Higher education, Design Science Research, Academic governance, Ethical AI integration","lastPublishedDoi":"10.21203/rs.3.rs-8998746/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8998746/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid adoption of generative artificial intelligence in higher education has increased instructional efficiency but has also raised concerns regarding academic governance, pedagogical control, and ethical accountability. While existing studies largely focus on tool adoption and user perceptions, there remains limited empirical evidence on how instructors systematically interact with generative artificial intelligence during authentic course design, teaching, and assessment processes. This study addresses this gap by proposing and empirically examining the Human-AI Collaborative Subject Design and Assessment Workflow, a human-centered instructional framework that structures collaboration between instructors and generative artificial intelligence across the full instructional lifecycle.\u003c/p\u003e \u003cp\u003eGrounded in Design Science Research, the framework was designed, enacted, and evaluated through full-semester implementation in an undergraduate university course. Empirical evidence was derived from authentic instructional artifacts, including syllabi, lecture materials, laboratory activities, and assessments, as well as documented human\u0026ndash;AI interaction processes. The results demonstrate that generative artificial intelligence can effectively support drafting and structuring tasks while human instructors retain exclusive control over pedagogical validation, laboratory design, assessment finalization, and grading.\u003c/p\u003e \u003cp\u003eThe findings confirm the feasibility of workflow-based, human-in-the-loop integration under real teaching constraints, showing that instructional scalability and efficiency can be achieved without compromising academic authority, ethical governance, or pedagogical quality. This study contributes a replicable, instructor-enacted model for responsible generative artificial intelligence adoption and advances process-level governance approaches for higher education teaching practice.\u003c/p\u003e","manuscriptTitle":"Human AI Collaborative Subject Design and Assessment Workflow","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 17:03:49","doi":"10.21203/rs.3.rs-8998746/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-20T10:57:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186846104802856657411663597492947727839","date":"2026-04-20T10:49:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140997472162818347123152731081559067666","date":"2026-04-16T06:23:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-03T15:39:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-13T05:53:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-11T16:39:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2026-03-11T10:54:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2035e08e-7dc4-4f6d-8528-865a4edce98f","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-09T17:03:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 17:03:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8998746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8998746","identity":"rs-8998746","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}