Conference Agenda
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Please note that all times are shown in the time zone of the conference. The current conference time is: 19th Aug 2026, 21:29:09 EET
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02 SES 05 A: AI and Digitalisation III
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02. Vocational Education and Training (VETNET)
Paper Developing Teachers' AI Literacy in VET: A Design-Based Pilot Study of a Transnational E-Learning Curriculum 1: Budapest University of Technology and Economics, Hungary; 2: University of Bremen; 3: University of Novi Sad; 4: University of Ss. Cyril and Method in Trnava Presenting Author:The rapid proliferation of generative Artificial Intelligence (GenAI) in our life and work presents a dual challenge for Vocational Education and Training (VET): educators should utilise AI for pedagogical efficiency and improvement while simultaneously preparing students for labor markets where AI is increasingly embedded in professional practice. Despite this "dual significance" of AI as both a teaching and learning tool and a vocational competency (Attwell et al., 2020), there is a paucity of research on how VET teachers acquire and should acquire the necessary literacy to integrate these tools effectively. This paper presents the results of a pilot study conducted within an Erasmus+ cooperation partnership involving four European teacher educator universities. The VETAssIst project (2024-2027, No. 2024-1-HU01-KA220-VET-000253387) developed and piloted a specialised e-learning course designed to enhance VET teachers' AI literacy. The partners designed the curriculum and learning content in eight modules focusing on different aspects of the VET teaching work (e.g., GenAI for active learning, GenAI for assessment, GenAI for planning, GenAI for professional development etc.), developing VET students’ AI literacy, and ethics and data protection. Each module consists of a Lesson, a practical Assignment, and questions encouraging self-reflection (AI Learning Diary) and the exchange of experiences in asynchronous group discussions (Forum). By aligning the modules with the competencies outlined in an AI Supplement to DigCompEdu (Bekiaridis & Attwell, 2024), UNESCO’s AI framework (Miao, F., & Cukurova, 2024), and the cognitive levels of Bloom’s Taxonomy (Oregon State University, 2024), we aimed to create practical and pedagogically sound learning experiences. The course was implemented in Moodle in five language versions with slightly adapted and supplemented national language content to suit the local needs. The pilots were organised in slightly different ways in the four partner countries but using a uniform core set of questions to collect participants’ feedback to finalise the curriculum and learning content. This study thus adopts a Design-Based Research (DBR) approach, characterising the educational intervention as an iterative process enacted in real-world settings. The theoretical framework distinguishes between AI as a mere technical tool for routine automation and AI as a tool for pedagogical augmentation (Huang & Rust, 2021). While early adoption often focuses on efficiency (Celik et al., 2022), our study also aimed to explore the deeper transformative potential of AI in VET. We define this potential specifically as how and in what aspects AI can improve VET teaching - moving beyond mere workload reduction to enhance active and collaborative learning, personalisation, and the creation of adaptive learning environments that were previously difficult to implement (Kasneci et al., 2023). The primary objective of this study is to evaluate the pilot implementation of the VETAssIst course. We seek to answer the following research questions:
The study posits that successful AI adoption in VET requires a curriculum that is responsive to the specific "timetable realities" of VET educators and addresses the ethical and social risks unique to the sector (UNESCO, 2023). Methodology, Methods, Research Instruments or Sources Used The study employs a Convergent Mixed Methods Design, collecting quantitative and qualitative data simultaneously throughout the pilot phase to provide a holistic evaluation of the VETAssIst course. The pilot involved 53 VET teachers (including 6 student teachers) from four partner countries (Hungary, Germany, Serbia, Slovakia), selected to represent a diverse range of VET subjects and digital skill levels. Data collection was embedded directly into the learning process to capture immediate reflections and behavioral patterns: Quantitative Instrument: - Feedback Forms: Short, closed-question surveys administered pre-course, post-module, and post-course via Moodle. These measured satisfaction with content and technical usability. Qualitative Instruments: - Focus Group Discussions: Online or face-to-face meetings were conducted in each partner country. A core set of semi-structured questions was utilised to explore the strengths and weaknesses of the VETAssIst curriculum and learning content, and the potential and deep-seated challenges of using AI for improving VET teaching. - AI Learning Diaries: Participants were requested to answer questions prompting self-reflection on learning processes and "lightbulb moments" regarding AI application throughout the course. - Forum Analysis: Asynchronous discussions on the Moodle platform, prompted by tutor-designed questions, were analysed to identify shared challenges and peer-to-peer learning dynamics. The analysis follows a triangulation protocol. Quantitative data from Moodle forms provides descriptive statistics on course satisfaction and usability. Qualitative data from transcripts of focus groups, diaries and Forum posts are subjected to thematic analysis. Conclusions, Expected Outcomes or Findings The study anticipates two primary categories of results: Curricular Refinements and Pedagogical Insights. Regarding curricular refinement, the analysis will yield a validated version of the VETAssIt course. We expect to resolve the tension between providing a broad "buffet" of GenAI tools versus deep-diving into a select few. Feedback on the assignments will determine if practical, artifact-creation tasks drive higher engagement than theoretical reflection. Pedagogically, the study will report on the transformative potential of AI in VET as perceived by practitioners. We anticipate findings that detail how GenAI may improve VET teaching by enhancing active learning, enabling differentiation in heterogeneous student groups and simulating complex professional scenarios in the classroom. Furthermore, the results will highlight the shift in teacher mindset, from viewing AI as a "shortcut" to understanding it as a partner for pedagogical innovation. Ultimately, the findings will inform VET decision-makers at both school and national levels, outlining how institutional and policy support must evolve to sustain AI literacy. The final output will be an open-access, empirically validated course tailored to the unique needs of the VET sector. References Attwell, G., Deitmer, L, Tutlys, T., Roppertz, S., & Perini, M. (2020). Digitalisation, artificial intelligence and vocational occupations and skills: What are the needs for training teachers and trainers? In C. Nägele, B. E. Stalder, & N. Kersh (Eds.) Trends in vocational education and training research, Vol. III. Proceedings of the European Conference on Educational Research (ECER), Vocational Education and Training Network (VETNET), 30–42. https://doi.org/10.5281/zenodo.4005713 Bekiaridis, G., & Attwell, G. 2024. Integrating Artificial Intelligence in Vocational and Adult Education: A Supplement to the DigCompEdu Framework. Ubiquity Proceedings, 4(1): 20. https://doi.org/10.5334/uproc.142 Bükki, E., Papp, Z., Manojlovic, H., Sanitizer, A., Luprihová, J., & Kovács, E. (2025). Artificial Intelligence in VET: Interest and concerns by VET teachers. In: VETNET Conference Series, 8 (ECER), 59-67. https://doi.org/10.21240/vetcon/2025/ecer/32 Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research. TechTrends 66, 616–630. https://doi.org/10.1007/s11528-022-00715-y Kasneci, E., et al. (2023). ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences, 103, 102274. Manojlovic, H., & Bükki, E. (forthcoming). Artificial Intelligence in VET: Interests and concerns by VET teachers. In: International Conference on Interactive Collaborative Learning 2025, Springer. Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO Digital Library. https://unesdoc.unesco.org/ark:/48223/pf0000391104 Pelletier, K., McCormack, M., Reeves, J., Robert, J., Arbino, N., Dickson-Deane, C., & Stine, J. (2022). 2022 EDUCAUSE Horizon Report Teaching and Learning Edition. Boulder, CO.: EDUCAUSE https://library.educause.edu/-/media/files/library/2022/4/2022hrteachinglearning.pdf?la=en&hash=6F6B51DFF485A06DF6BDA8F88A0894EF9938D50B Oregon State University: Bloom’s taxonomy revisited. https://ecampus.oregonstate.edu/faculty/artificial-intelligence-tools/blooms-taxonomy-revisited/ (2024), accessed 26 January 2026. UNESCO (2023). Guidance for generative AI in education and research. https://doi.org/10.54675/EWZM9535 02. Vocational Education and Training (VETNET)
Paper Embodied AI Customer Role-Play in VR for Retail Apprentices 1: Pädagogische Hochschule Zürich, Switzerland; 2: RMIT University Presenting Author:Communication competence is a key transversal skill for employability in vocational education and training (VET) (Calero López & Rodríguez-López, 2020) and a domain-specific competence in occupations with intensive customer contact. In the Swiss dual VET system, retail apprentices are expected to handle challenging complaint situations professionally, a competence anchored in the occupational ordinance and educational plan (SBFI, 2021; Bildung Detailhandel Schweiz, 2021). Despite this curricular emphasis, opportunities for repeated, high-quality practice remain scarce at classroom scale. Authentic role-play typically requires trained actors, dedicated rooms, and expert coaching to structure reflection. These resource requirements limit frequency and equity of access and constrain vocational schools’ capacity to offer deliberate practice with timely, task-specific feedback. Immersive roleplay with embodied conversational agents offers a plausible response to this gap. Virtual role-playing can provide repeated, situated practice with standardised starting conditions and comparable difficulty levels, while VR can increase experiential fidelity through embodiment, social presence, and contextual cues (Othlinghaus-Wulhorst et al., 2019). Recent advances in generative AI extend this potential by enabling unscripted, adaptive dialogue without predefined branching scripts and by supporting structured post-simulation coaching dialogues grounded in the preceding interaction (Okonkwo & Ade-Ibijola, 2021; Li et al., 2025). Conversational agents are also increasingly used to scaffold post-simulation debriefing, where structured reflection and transfer are consolidated. Recent evidence from a VR counselling simulation suggests that chatbot-guided debriefing can yield learning gains comparable to human-led debriefing (Evangelou et al., 2026). This paper presents an AI-mediated communication training and coaching system for retail apprentices, implemented across three modalities (audio-only, desktop, XR). The training unfolds in three pedagogically sequenced phases supported by two distinct AI personas. The coach persona, Gisela Dacosta, is an AI coach and expert in retail sales communication who conducts the introduction and reflection phases. The customer persona, Klaus Trubel, is an adversarial AI customer (a middle-aged man with biases against Generation Z) who drives the complaint-handling simulation.
The prototype was developed as a design-based research (DBR) implementation study (Design-Based Research Collective, 2003; McKenney & Reeves, 2012) with two aims: (a) to lay the groundwork for sustainable classroom implementation through field-based development in close collaboration with practitioners, including a prospective educational publisher as practice partner; and (b) to derive transferable design characteristics for integrating AI-driven VR communication training into everyday VET teaching. Methodology, Methods, Research Instruments or Sources Used The XR prototype (Meta Quest 3 with colour passthrough) was developed and refined through iterative DBR-oriented work that combined context analysis, co-design with practitioners and apprentices, field-based pilots, and redesign (Design-Based Research Collective, 2003; McKenney & Reeves, 2012). A context analysis with vocational practitioners informed the selection of an assessment-proximate complaint scenario and the translation of Swiss retail VET standards into explicit learning goals, success criteria, and boundary conditions for classroom use (time, setup effort, orchestration). Co-design activities with vocational teachers, retail experts, apprentices, and developers specified scenario cues, escalation dynamics, persona safeguards (role stability), and reflection scaffolds, aligning pedagogical requirements with AI and VR constraints. Successive prototypes were then piloted in vocational school settings (including formative pilots with n = 16 apprentices), informing revisions to scenario structure, customer and coach behaviour, and the reflection prompt and feedback format, with a sustained focus on practicability under authentic classroom conditions. A final summative field evaluation examined classroom-scale practicability and perceived learning value of the XR variant. In a counterbalanced within-subject study with a 3×3 Latin-square order (N = 18), apprentices completed the same task in three modalities (audio, desktop, XR); this paper reports the XR condition in detail. Quantitative baseline and post-session measures were embedded in the procedure. A baseline questionnaire (Q1, plenary) captured participant characteristics (e.g., prior VR experience and self-reported technical ability). After each modality, participants completed a post-condition questionnaire (Q2) comprising the NASA Task Load Index (NASA-TLX) and a Presence Questionnaire (0–100 ratings) to capture workload and modality-sensitive experience (e.g., naturalness and perceived control). At the end, a final questionnaire (Q3, plenary) captured overall training evaluation and adoption preferences, including modality-specific ratings, a preferred modality ranking for classroom use, and open comments. Quantitative data were analysed descriptively. Qualitative data were collected via a semi-structured group interview with all participants and a focus group (n = 6) focusing on the coaching reflection. System traces (e.g., interaction duration, interruptions, termination points) supported triangulation, including breakdowns observed in practice (e.g., reduced speech-recognition accuracy in noisier moments). Conclusions, Expected Outcomes or Findings The study provides implementation-relevant evidence on where AI-driven XR role-play adds value in VET and which design decisions support classroom adoption. Across iterations, apprentices described the unscripted customer encounter as engaging and close to vocational routines when the customer persona escalated credibly and remained role-consistent. In the summative study, post-condition ratings suggested that XR can strengthen perceived interaction naturalness and control, while workload ratings helped characterise the practical trade-offs of XR use under classroom constraints. A salient limitation for authentic lessons is robustness. In real vocational school environments, background noise and time pressure sometimes degraded speech recognition and turn-taking, which could interrupt the simulation, reduce perceived authenticity, and lead learners to discount the coach’s feedback. Learners also noted that the coaching reflection was less helpful when feedback became generic or repetitive, particularly when parts of the preceding dialogue were not captured reliably. Synthesising learner feedback, quantitative indicators (NASA-TLX and presence ratings), observations, and trace data, we derive three design principles that closely reflect the project’s original design strategies: 1. Align scenario complexity with vocational practice: Use assessment-aligned scenarios and legible task structure; prioritise persona coherence and meaningful behavioural variability over additional complexity. 2. Embed structured, contextualised AI-coach reflection immediately after practice: Anchor feedback in concrete interaction events (for example via turn-referenced cues) and provide goal-specific, criterion-linked suggestions while avoiding redundant prompts. 3. Enhance authenticity and robustness of conversational dynamics: Reduce repetition, latency, and implausible responses and provide error-tolerant fallbacks, as technical instability in XR can disproportionately undermine perceived usefulness and scalability in classrooms. References Bildung Detailhandel Schweiz (BDS). (2021). Bildungsplan zur Verordnung über die berufliche Grundbildung für Detailhandelsfachfrau EFZ / Detailhandelsfachmann EFZ. Bern. Calero López, I., & Rodríguez-López, B. (2020). The relevance of transversal competences in vocational education and training: A bibliometric analysis. Empirical Research in Vocational Education and Training, 12, Article 12. https://doi.org/10.1186/s40461-020-00100-0 Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 5–8. https://doi.org/10.3102/0013189X032001005 Evangelou, D., Mulders, M., & Träg, K. H. (2026). Debriefing in virtual reality simulations for the development of counseling competences: Human-led or AI-guided? Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-025-09941-8 Hübsch, T., Vogel-Adham, E., Vogt, A., & Wilhelm-Weidner, A. (2024). Articulating tomorrow: Large language models in the service of professional training. VDI/VDE Innovation + Technik. https://doi.org/10.25656/01:29036 Li, Z., Babar, P. P., & Peiris, R. L. (2025). Generative role-play communication training in virtual reality for autistic individuals: A study on job coach experiences in vocational training programs. In Proceedings of CHI 2025. ACM. https://doi.org/10.1145/3706598.3713507 McKenney, S., & Reeves, T. C. (2012). Conducting educational design research. Routledge. Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2, 100033. https://doi.org/10.1016/j.caeai.2021.100033 Othlinghaus-Wulhorst, J., Mainz, A., & Hoppe, H. U. (2019). Training customer complaint management in a virtual role-playing game: A user study. In M. Scheffel et al. (Eds.), Transforming Learning with Meaningful Technologies (EC-TEL 2019), LNCS 11722 (pp. 436–449). Springer. https://doi.org/10.1007/978-3-030-29736-7_33 Peters, M., Tschöpe, T., Konheiser, S., Raecke, J., & Schnitzler, A. (2023). Development of a digital training for social and emotional competences for medical assistants in vocational education and training in Germany. Empirical Research in Vocational Education and Training, 15, Article 4. https://doi.org/10.1186/s40461-023-00143-z Staatssekretariat für Bildung, Forschung und Innovation (SBFI). (2021). Verordnung über die berufliche Grundbildung für Detailhandelsfachfrau/Detailhandelsfachmann EFZ (SR 412.101.221.40). Fedlex. 02. Vocational Education and Training (VETNET)
Paper Designing Microlearning Modules on AI for University Teachers in Vocational Teacher Education: A Design-Based Research Study University of Education Upper Austria, Austria Presenting Author:A survey of 1558 university students from Germany, the US and the UK in 2023 suggests that students have a fairly high level of interest in AI and a positive attitude towards it, but relatively low levels of AI literacy (Hornberger et al., 2025). These findings are consistent with those of a survey of 236 teacher training students in vocational education in Austria at the beginning of 2024. This survey revealed that over 80% of respondents expressed a desire to learn more about the application of AI in university teaching. However, many respondents were unsure about the rules governing the use of AI in preparing theses, assignments and examinations at university (Cechovsky & Malli-Voglhuber, 2025). In response to the rapid development of AI, universities need to create conditions that will enable AI to be integrated into everyday teaching and examination practices. Nevertheless, steps must be taken to equip university lecturers to teach AI-related competencies to their students. An international survey conducted by UNESCO in September 2025 shows that almost two-thirds of higher education institutions have guidance on using AI, or are in the process of developing it. Additionally, the majority of participants use AI in their professional lives. However, more than half of the respondents are uncertain about its educational or research implications, have limited technical knowledge and are largely unaware of its ethical implications (UNESCO, 2025). The use of AI in higher education is therefore a highly relevant topic, but one that places significant demands on higher education teachers. They are also affected by the EU's AI Act. The Act stipulates that employees using AI systems must receive training to ensure they can use these systems competently and responsibly (Aschemann & Klampferer, 2024). According to Article 3(56) of the AI Act (AIA), AI literacy encompasses skills, knowledge and understanding that allow providers, deployers and affected persons, taking into account their respective rights and obligations in the context of this Regulation, to make an informed deployment of AI systems, as well as to gain awareness about the opportunities and risks of AI and possible harm it can cause. This research and development project addresses the following guiding research question in order to develop an offering for university lecturers that enables them to further develop their AI literacy in a targeted manner in areas relevant to vocational teacher education at our university: How can a design-based research approach be applied to develop microlearning modules on AI-related topics for university lecturers in vocational teacher education? Adopting a design-based research approach, we developed, tested and iteratively optimised microlearning modules that address current challenges in the intersection of AI and university teaching in vocational teacher education. Within the framework of the project, microlearning is defined as media-supported learning in small steps and units, characterised by the clear presentation of content, short processing times, and the ability to be used at any time and in any location. Microlearning is based on cognitive load theory, with the aim of minimising extrinsic load through optimal module design to generate capacity for learning-related processing (Krieglstein et al., 2023). Methodology, Methods, Research Instruments or Sources Used We chose the design-based research approach as our methodological framework because it is well-suited to solving a current problem in university teaching practice – the lack of skills in dealing with AI – by means of an intervention, namely the development of microlearning modules, and to producing sustainable innovations (Reinmann, 2005). Our research is based on the cyclical process for design-based research projects proposed by Schmiedebach and Wegner (2021), which comprises four main phases. First, a preliminary examination is carried out, analysing the problem, reviewing the state of research and defining development principles. The next step involves developing prototypes based on the results of the preliminary examination. This is followed by an assessment phase in which the prototypes are evaluated. The feedback received serves as the basis for revisions and interventions, making the development process iterative. In the final solution phase, the optimised microlearning modules are implemented. We began in 2024 with the preliminary examination, which was prompted by a questionnaire study conducted by our teacher training students in the field of vocational education. Further explorations and conversations with university teachers then led to the decision to base the development of microlearning modules on research using microlearning modules and cognitive load theory to improve the AI literacy of university teachers in vocational teacher education. We then developed prototypes for the microlearning modules. Content-wise, we started with scientific writing and competence-based assessment, as these are central aspects of vocational teacher education. In October 2025, the assessment phase began with an online workshop for interested university teachers. We conducted focus group interviews to gain insights into the participants' experience of the microlearning modules, as well as to identify design principles relevant to the target group. We recorded, transcribed and analysed the interviews using qualitative content analysis according to Mayring (2015). The microlearning modules were then adapted based on the findings. Both theoretical findings and empirical methods, particularly focus groups, were employed for this purpose (Reinmann, Herzberg & Brase, 2024). In December 2025, another online workshop was held with university teachers to address remaining questions on design principles. We are now in the final phase of revising the two microlearning modules. Conclusions, Expected Outcomes or Findings The design principles derived from the literature on microlearning modules and cognitive load theory were valuable in developing prototypes of two microlearning modules aimed at improving the AI literacy of university lecturers at our institution. The first focus group resulted in positive feedback on the microlearning design principles concerning the modules' compact format and clear structure. From a methodological perspective, decision trees and case studies were identified as particularly helpful. However, participants also encountered technical challenges that could increase cognitive load. Several of these issues were resolved, and some were discussed further in the second focus group. Our presentation will offer insights into key design principles for microlearning modules for university lecturers and illustrate how a design-based research approach can support the systematic development of continuing education programs in the context of the AI Act. The aim of this design-based research project is not to get generalized findings but rather, it represents an example for a bottom-up approach guided by a research methodology that enables the identification of practice-oriented problems while taking into account the perspectives of those affected. References Aschemann, B., & Klampferer, M. (2024). AI-Act verlangt ab 2025 Sicherung von KI-Kompetenz [AI Act requires safeguarding AI competence from 2025]. Erwachsenenbildung.at. Retrieved April 1, 2025, from https://erwachsenenbildung.at/digiprof/neuigkeiten/19851-ai-act-fordert-ki-kompetenz.php Cechovsky, N., & Malli-Voglhuber, C. (2025). Von der Hochschule ins Klassenzimmer: Die Rolle der KI in der Lehrer:innenbildung [From university to the classroom: The role of AI in teacher education]. Zeitschrift für Hochschulentwicklung, 20(SH-KI-2), 143–164. https://doi.org/10.21240/zfhe/SH-KI-2/08 Hornberger, M., Bewersdorff, A., Schiff, D. S., & Nerdel, C. (2025). A multinational assessment of AI literacy among university students in Germany, the UK, and the US. Computers in Human Behavior: Artificial Humans, 4, 100132. https://doi.org/10.1016/j.chbah.2025.100132 Krieglstein, F., Beege, M., Rey, G. D., Sanchez-Stockhammer, C., & Schneider, S. (2023). Development and validation of a theory-based questionnaire to measure different types of cognitive load. Educational Psychology Review, 35(1), 9. https://doi.org/10.1007/s10648-023-09738-0 Mayring, P. (2015). Qualitative Inhaltsanalyse: Grundlagen und Techniken [Qualitative content analysis: Foundations and techniques] (12th ed.). Beltz. Reinmann, G., Herzberg, D., & Brase, A. (2024). Forschendes Entwerfen: Design-Based Research in der Hochschuldidaktik [Research-based design: Design-based research in higher education didactics]. transcript Verlag. Reinmann, G. (2005). Innovation ohne Forschung? Ein Plädoyer für den Design-Based-Research-Ansatz in der Lehr-Lernforschung [Innovation without research? A plea for the design-based research approach in teaching and learning research]. Unterrichtswissenschaft, 33(1), 52–69. https://doi.org/10.25656/01:5787 Schall, M. (2020). Entstehung und Verwendung von Microlearning im Kontext des beruflichen Lernens [Development and use of microlearning in the context of vocational learning]. Zeitschrift für Berufs- und Wirtschaftspädagogik, 116(2), 214–249. https://doi.org/10.25162/zbw-2020-0010 Schmiedebach, M., & Wegner, C. (2021). Design-Based Research als Ansatz zur Lösung praxisrelevanter Probleme in der fachdidaktischen Forschung [Design-based research as an approach to solving practice-relevant problems in subject-matter didactic research]. Bildungsforschung, (2), 1–10. UNESCO. (2025, September 2). UNESCO survey: Two thirds of higher education institutions have or are developing guidance on AI use. https://www.unesco.org/en/articles/unesco-survey-two-thirds-higher-education-institutions-have-or-are-developing-guidance-ai-use | ||