Artificial Intelligence (AI) is widely regarded as having the potential to revolutionise education, especially since the widespread availability of generative AI tools following ChatGPT’s release in November 2022. Prior studies (Bekiaridis, 2024, Celik, 2022) suggest that AI can make the teaching and learning process more efficient, personalised and accessible, assuming various possible roles such as direct mediator between teacher and learner, supplementary teacher assistant, or learning partner for students. It can assist and improve pedagogical planning and differentiation by leveraging advanced analytics to identify patterns in learner data, create or adapt educational content, support collaborative learning, streamline assessment, provide personalised feedback, and foster students’ self-efficacy. By automating routine and administrative tasks, it allows teachers to focus on fostering meaningful interactions with students and adopt more personalised teaching approaches. In the specific context of vocational education and training (VET), AI is also a critical content as it is increasingly being integrated into all occupational sectors and vocations, thus it holds a dual significance for VET (Attwell et al., 2020).

Despite its potential and the current hype around it, integrating generative AI tools in education is a multifaceted challenge, influenced by numerous individual, organisational, technological, and societal factors. Several ethical, legal, and societal concerns must be addressed. Key challenges include unequal access to AI technologies, which exacerbates the digital divide; ensuring the safe and ethical use of student data; mitigating risks associated with biases and the misuse of AI; addressing technical complexities; and overcoming resistance to change and ensuring the pedagogically meaningful application of AI  (UNESCO, 2023, Pelletier et al., 2022).

Our short survey of VET teachers in Hungary, Serbia and Slovakia conducted in February 2024 as part of a needs analysis for a project proposal (Bükki and Manojlovic, 2024, Papp et al., 2024) confirmed research from other countries, which found that while teachers generally have a positive attitude toward AI in education, only a minority of them have actually incorporated AI tools into their teaching. Particular differences were found by level of education and subject (Diliberti et al., 2024, Galindo-Domínguez et al., 2024): while primary and secondary school teachers mainly used AI for content creation, without emphasizing student engagement with AI tools, lecturers in higher education exploited AI more for academic-technical purposes, enabling their students to experiment with AI tools.

The actual use cases of AI in education and especially in VET are still rather rare or at least little known (Chiu, 2023, Deitmer et al., 2024), and the pedagogically meaningful use of AI in VET needs to be further explored. Our study, implemented as part of the VETAssIst - Artificial Intelligence as a teaching assistant for VET teachers project co-funded by the European Union’s Erasmus+ programme (No 2024-1-HU01-KA220-VET-000253387), aims to contribute to that by identifying and analysing AI use as well as the factors behind non-use by VET teachers in the project’s partner countries: Germany, Hungary, Serbia and Slovakia.

Our study aims to answer the following research questions: Q1: What is AI currently used for by VET teachers and how in the partner countries? Q2: What motivates VET teachers to use AI in their work? Q3: What supports/hinders VET teachers to use AI in their work? Q4: How is the use of AI influenced by socio-demographic and organisational factors? We collect data through semi-structured interviews conducted offline or online, based on a guide developed by the project partners, adapting the guide used in the AI Pioneers Erasmus+ project and incorporating findings from our literature review regarding use cases and factors that may influence the integration of AI in education. Individual interviews will be conducted with so-called AI pioneers who already use AI in some notable manner in their VET teaching, while “non-pioneers” will be asked in individual and/or focus group interviews. Interviewees are identified partly through the researchers’ personal contacts (convenience sampling) and the snowball method, partly through a VET teacher survey that will supplement the interview study in some partner countries. At least 10 VET teachers will be interviewed in each country, but the exact numbers will depend on when we reach a certain saturation with no additional new information expected. The interviews are recorded and then transcribed. We provide rich descriptions of use cases, and employ thematic coding (both deductive and inductive) and analysis to explore the similarities and differences across the cases and the perceptions of VET teachers by country, sector/vocation and selected socio-demographic and organisational factors (age/teaching experience, gender, professional background, programme type and geographical location, etc.).

Our study sheds light on the current state of artificial intelligence integration in VET in the VETAssIst partner countries and the general and country-specific individual and organisational factors that encourage or impede this integration. By examining diverse perspectives, we aim to advance understanding of both how AI is being utilised and why it remains underutilised despite its widely acknowledged potential. While AI pioneers demonstrate innovative use cases that enhance pedagogy and streamline teaching workflows, the majority of VET teachers remain cautious, constrained by barriers such as limited familiarity with AI applications, lack of institutional support, and technical or infrastructural challenges. The three key themes of accessibility, readiness, and meaningful application remain crucial but our findings provide invaluable data for designing targeted policy interventions focusing on VET teachers. They also contribute to the selection and development of content for the e-learning course that will be created as part of the VETAssIst project and piloted during the autumn semester of the 2025/2026 academic year.

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. (2024). Supplement to the DigCompEdu Framework. Outlining the skills and competences of educators related to AI in education. AI Pioneers – Work Package 3 https://aipioneers.org/supplement-to-the-digcompedu-framework/ Bükki, E., Manojlovic, H. (forthcoming). Eager but Cautious. Hungarian VET Teachers About Employing Artificial Intelligence in VET. In: Futureproofing Engineering Education for Global Responsibility Proceedings of the 27th International Conference on Interactive Collaborative Learning (ICL2024). Springer. 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 (2022). https://doi.org/10.1007/s11528-022-00715-y Chiu, T. K. F. (2023). The impact of Generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney. Interactive Learning Environments. https://doi.org/10.1080/10494820.2023.2253861 Deitmer, L., Dersch, I., Meyne, L., Siemer, C. (2024). How do Technical Vocational School Teachers Deal with Artificial Intelligence in the Classroom? An Attempt to Analyze AI Use at German VET Schools. Ubiquity Proceedings, 4(1), 21. https://doi.org/10.5334/uproc.143 Diliberti, M. K., Schwartz, H. L., Doan, S. Y., Shapiro, A., Rainey, L. R., & Lake, R. J. (2024). Using Artificial Intelligence Tools in K-12 Classrooms. RAND Research Report. https://www.rand.org/pubs/research_reports/RRA956-21.html Galindo-Domínguez, H., Delgado, N., Losada, D., & Etxabe, J.M. (2024). An analysis of the use of artificial intelligence in education in Spain: the in-service teacher's perspective. Journal of Digital Learning in Teacher Education, 40(1), 41-56. http://dx.doi.org/10.1080/21532974.2023.2284726 Papp, Z., Manojlovic, H., Bükki, E., Kovács, E. (forthcoming). Artificial Intelligence in VET: Teacher Competencies, Challenges, and Development Needs in the Hungarian and Serbian Contexts. In Competences. Proceedings of the 13th International Methodological Conference. University of Novi Sad, Hungarian Language Teacher Training Faculty. 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 UNESCO (2023). Guidance for generative AI in education and research. https://doi.org/10.54675/EWZM9535