Advances in Teaching and Learning Technologies

Permanent URI for this collectionhttps://hdl.handle.net/10125/112395

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    Supporting Informal Field-Based Learning in the AI Era: Key Antecedents in Human-AI Collaboration
    (2026-01-06) Kirmse, Rosemarie; Latto, Chloe; Soroko, Daria; Richter, Alexander; Bittner, Eva; Tate, Mary; Nolte, Ferry
    Informal field-based learning (IFBL) is essential for developing the current workforce in the age of AI as employees’ skills need to evolve with the introduction of these new technologies. Effective human-AI collaboration (HAIC) demands for considering factors such as individual needs, work practices and perspectives before the introduction of new technologies like generative artificial intelligence (genAI) systems. However, little is known of what factors exactly to consider toward informal fieldbased learning in HAIC and how these antecedents are related to each other. In this paper, we identify potential individual antecedents of IFBL in a HAIC context, examine their relationships and derive implications for managing the introduction of genAI into organizations.
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    How to Use Generative Artificial Intelligence in the Research Process: A Modular Course Approach for Early Career Researchers
    (2026-01-06) Da Silva Cardoso, Heike; Stöckl , Raphaela; Brehmer, Martin
    The integration of generative artificial intelligence (GAI) tools into academic research workflows presents both promise and complexity — particularly for early career researchers (ECRs), who often lack structured guidance on responsible use. This study addresses this gap by designing and evaluating a modular course that supports ECRs in applying GAI systematically and appropriately across key stages of the research process, including literature exploration, hypothesis development, and academic writing. Drawing on the Design Science Research (DSR) Methodology, the course was iteratively developed and assessed through expert interviews and pre- and post-surveys with participants. Expert feedback suggests refinements to pacing and engagement. Quantitative findings indicate increased confidence and frequency of GAI use, especially for literature discovery and scholarly communication. This work contributes to DSR by offering a grounded course concept and actionable guidelines, aimed at advancing GAI literacy in ECRs’ scholarly work, supporting the transparent, responsible integration of GAI into research practice.
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    AI Literacy Frameworks for Educators: An Umbrella Review
    (2026-01-06) Shenoy, Prashanth; Saarela, Mirka
    As artificial intelligence transforms educational environments, educators require specialized literacy frameworks for effective technology integration. This umbrella review synthesizes 28 prior studies (26 systematic/scoping reviews and 2 design/resource-mapping studies) on AI literacy frameworks for educators. Our analysis examines conceptual models, ethical principles, treatment of explainability as a subdomain of ethics, implementation challenges and enablers, and assessment strategies. The results show fragmented approaches with constructivist approaches being the predominant theoretical foundation, with TPACK and AI4K12 also commonly adopted, while attention to ethics varies widely across frameworks. Key challenges include insufficient teacher preparation, resource limitations, and technological complexity, while enablers encompass structured professional development, project-based learning, and institutional support. The review identifies significant gaps in assessment methodologies, particularly the lack of standardized teacher-specific evaluation tools, and notes that explainability, despite its importance for educator trust, is explicitly addressed in only one study. This analysis informs development of robust educator-centered AI literacy frameworks balancing technical and ethical knowledge.
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    VizCoach: Designing an Orchestration-Based Tool for Data Visualization Education
    (2026-01-06) Chawla, Shubham; Kintscher, Michael; Narula, Jai; Arunkumar, Anjana; Amresh, Ashish; Bryan, Chris
    Visualization courses are commonly found in university settings, but many students (particularly those from computer science or STEM backgrounds) struggle learning the ``design thinking'' aspects of visualization. In this paper, we investigate how to design and engineer a technology-enhanced learning platform for teaching visualization concepts called VizCoach. VizCoach adopts a hands-on, orchestration-based approach that abstracts away the coding aspects of visualization construction, allowing students to focus on applying design thinking principles during learning. VizCoach also supports instructors by providing workflows tailored for creating and moderating learning activities. Empirical evaluations help validate that VizCoach supports design and engineering requirements for successful orchestration and design thinking learning scenarios. We also discuss how tools like VizCoach contribute to technology-enhanced learning for visualization, and can provide opportunities for future research into visualization learning processes.
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    Hybrid Intelligence in Higher Education: Exploring Disciplinary and Experiential Determinants of Students’ AI Acceptance
    (2026-01-06) Mueller, Mareike; Mütterlein, Joschka
    The integration of artificial intelligence (AI) in higher education is no longer a question of 'if', but of 'how'. Specifically, which roles humans and technology can and should play. Based on a survey of 731 students across diverse disciplines, this study explores how prior AI experience and field of study influence students’ acceptance of hybrid teaching models. While 84% reject AI-only instruction, 62% support a collaborative model combining human and machine intelligence. Drawing on Task-Technology Fit (TTF) and Hybrid Intelligence theory, we demonstrate that acceptance is driven less by novelty and more by perceived educational value. Students with prior AI exposure report significantly higher acceptance of hybrid instructional formats, especially in management-oriented disciplines. Our findings offer empirically grounded guidance for developing ethically grounded, effective AI-assisted learning environments that complement rather than replace human instruction.
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    Designing a Course-Grounded AI Tutor with Retrieval-Augmented Generation: A DSR Approach to Technical Education
    (2026-01-06) Vu-Minh, An; Nguyen-The, Quang; Nguyen, Ngoc; Pham, Xuan-Lam; Nguyen, Andy
    Intelligent tutoring systems (ITS) leveraging generative artificial intelligence (GAI) represent an emerging opportunity in educational technology, yet there is limited prescriptive knowledge on designing such systems for technical subject learning. In this paper, we conducted a Design Science Research project and propose design principles (DPs) for developing GAI-powered ITS that address current limitations in personalized learning at scale. We instantiated the proposed design and developed SmartStudy - a retrieval-augmented generative AI system with multimodal capabilities for machine learning education. The artifact autonomously processes textbook content, generates practice tests, provides grounded answers, and performs automated grading. We evaluated SmartStudy with 44 data science students using performance metrics and user feedback assessments. Results demonstrate improved student performance, positive learning effects, and high continuance intention. This paper provides practitioners with design principles for creating GAI-based tutoring systems with concrete guidelines that can be extended to other technical domains.
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    Introducing GAMUT - A Game-based Assessment for Measuring User Types: Evaluation of Game Design for Satisfying Psychological Needs to Enhance User Engagement and Flow
    (2026-01-06) Reinelt, Ramona; Meyer, Benjamin
    Despite the growing relevance of game-based assessment in higher education, many approaches lack theoretical grounding and motivational design. This study introduces GAMUT, a theory-driven game-based assessment that embeds user type assessment into interactive, narrative gameplay. Based on the Self-System Model of Motivational Development and the Hexad model, GAMUT incorporates achievement, social, and immersion game elements to satisfy the core psychological needs: competence, autonomy, relatedness. Implemented as a mobile adventure simulation, it transforms the Hexad scale into decision-based scenarios for authentic self-assessment. Empirical evaluation with higher education students shows that social and immersive game elements significantly promoted autonomy, competence and relatedness, enhancing engagement and flow. Achievement game elements had limited effects, emphasizing the need for context-sensitive game design. GAMUT achieved an 85% accuracy rate in user type classification and was preferred over traditional questionnaires. These findings offer a systematic, motivational GBA approach, contributing to assessment validity, learner engagement and self-directed learning.
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    Introduction to the Minitrack on Advances in Teaching and Learning Technologies
    (2026-01-06) Scrivner, Olga; Nguyen, Andy; De Laat, Maarten; Scrivner, James