Conference Agenda
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02 SES 03 A: AI and Digitalisation II
Paper Session
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| Presentations | ||
02. Vocational Education and Training (VETNET)
Paper Digital Transformation in Service Professions - Professionalisation of Teachers Technical University Munich, Germany Presenting Author:Digital transformation poses a challenge for European society. This transformation is rapidly reshaping the professional landscape and affecting all industries. Service professions such as the hospitality and catering industry are facing challenges such as a shortage of skilled workers, a high diversity of languages among employees, and a sharp increase in digitalisation since the Covid-19 pandemic. Networked kitchen systems, robotics, and the use of (AI-based) data analysis for personalised offers are having a far-reaching impact on all areas of personal services in the food industry (Demary & Goecke, 2021; Heindl et al., 2025). The integration of digital tools and technologies is changing traditional working methods and processes, thereby altering the skill profiles of skilled workers. The dynamics of digitalisation thus present vocational education and training with further development tasks in the areas of personal and instructional growth. The integration of digital technologies in companies requires adapted teaching scenarios in vocational training institutions. In order to teach the transformation processes, teachers need suitable skills to reflect the evolving professional landscape. Regarding the integration of digital technologies at work, the professionalisation of teachers at vocational schools requires the promotion of practical skills for designing teaching scenarios. The required professionalisation encompasses the use and design of digital work and media technologies as well as an awareness of data management and protection (Miesera, 2021). Similarly, adapted teaching and learning scenarios at vocational schools are essential for reflecting current changes in the professional world within the classroom and for promoting expanded competence profiles. Studies indicate a demand for vocational training staff to strengthen their professional skills; at the same time, only a few subject-specific training opportunities are available (Kastrup & Brutzer, 2021). This research project is part of the ‘KoKon’ project network (learning:digital). It develops training modules that promote the professional competence development of teachers. It specifically provides training for the design of digitally supported teaching, especially in the areas of digital transformation and digital media didactics. The focus is on raising teachers' awareness of the requirements of digital change in the professional field and strengthening their skills in the effective use of digital media for learning. Through professional field-specific and cross-professional field cooperation, teaching and staff development at vocational schools is promoted in a sustainable manner. This supports the professional development of collaborative, feedback-based problem-solving strategies among teachers. The evidence-based teacher training courses offered by the Technical University of Munich are based on the competence areas of the European DigCompEdu (Redecker, 2017) and the further developed DigCompEduObserve (Özsoy et al., 2024), and the classification categories for vocational training in the digital transformation according to Wittmann & Weyland (2020). The research question, “What impact can teacher training have on vocational schools, specialising in the hospitality and catering industry?”,focuses on teachers' knowledge, attitudes, and self-efficacy regarding digital media in the classroom, and their ability to integrate profession-related digital technologies into their teaching. The aim is for teachers to gain a deeper understanding of digital media didactics and digital transformation in the professional field and to strengthen their ability to further develop and critically reflect on teaching at the curricular level. The results of the study on the training courses conducted will be presented, and the implications for evidence-based teacher training will be discussed. Methodology, Methods, Research Instruments or Sources Used The mixed-method research design of this research project provides for data collection at three measurement points. The teacher training as the intervention is delivered through face-to-face or synchronous online sessions. The teacher training courses concentrate on determining the quality characteristics of professional teaching and learning. In particular, the focus is on identifying the skills being promoted and professional action orientation, considering the phases of the whole process. To subsequently develop didactic approaches for media-supported teaching linked to digital transformation, a category system for digital transformation and a model for media didactics are developed together with the training participants. Great importance is attached to cooperation and the exchange of experiences between the training participants. In addition, practical examples are presented to the teachers in order to convey the content more clearly. Quantitative data collection on teachers' digital skills, in the form of self-assessments before and after the intervention (training), is conducted using scales. The instrument for teachers' self-efficacy with regard to the integration of digital technology into teaching (SWIT) comprises twelve items with a six-point Likert scale (Doll & Meyer, 2021); the utility value scale comprises four items with a four-point Likert scale (Fütterer et al., 2023), and the adapted TK-TPACK model in the field of nutrition and home economics (Miesera et al., 2021) comprises seventeen items with a four-point Likert scale. The quantitative data is evaluated using SPSS. The data set was first checked for completeness and consistency, cleaned up, and then sorted into pre- and post-tests. Descriptive calculations and regression analysis were then performed. The qualitative survey is conducted using guided interviews approximately six months after the teacher training courses (categories: teachers' digital skills and lesson planning with digital elements, implementation of training elements, and cooperation in lesson planning). The interviews were transcribed using AI-based open-source software, then revised and pseudonymised. The qualitative data was coded and evaluated using MAXQDA. A category system was created to code the interviews, which was then used to do so. Conclusions, Expected Outcomes or Findings The pre-test evaluation (n = 182) already shows a high level of agreement regarding the introduction of digital media and technologies in the classroom (M = 3.39, SD = .64) (four-point Likert scale). Technology Knowledge (TK) items were rated more positively than the job-related skills (TPACK), confirming the pilot study results by Miesera and colleagues (Miesera et al., 2021). The regression analysis to clarify the variance of dependent variables based on personal parameters provides a differentiated picture. The regression analysis of the dependent variable SWIT shows that the independent personal variables age, professional field, and experience with digital media are predictors. According to this, younger participants and teachers with more experience in digital media have significantly higher SWIT values. The analysis of the dependent variable utility value scale shows a positive correlation with increasing experience with digital media. However, the perceived added value of digital media and technologies in the classroom decreases with increasing professional experience. No significant correlations were found for the other predictors. The guided interviews (n=30) were coded using MAXQDA. Initial results show that the use of AI tools in the classroom plays a particularly important role for teachers and currently overshadows other digital tools. The teachers interviewed stated that they generally perceived many of the contents taught in the training course and the tools and methods presented as relevant and beneficial. They also expressed a desire to make greater use of these methods and tools in the classroom, but reported that their use and the implementation of the content are currently possible only to a limited extent due to time constraints and insufficient technical equipment in schools. Teachers often cite support from colleagues and school management as conducive conditions. Quantitative and qualitative research results are presented and discussed. References Demary, V., & Goecke, H. (2021). Digitalisierung der Branchen in Deutschland — eine empirische Erhebung. Wirtschaftsdienst, 101(3), 181–185. Doll, J. & Meyer, D. (2021). SWIT. Selbstwirksamkeit von Lehrerinnen und Lehrern im Hinblick auf die unterrichtliche Integration digitaler Technologie, In Leibniz-Institut für Psychologie (ZPID) (Hrsg.), Open Test Archive. Trier: ZPID. Fütterer, T.; Scherer, R.; Scheiter, K.; Stürmer, K.; Lachner, A. (2023). Will, skills, or conscientiousness: What predicts teachers’ intentions to participate in technology-related professional development? Computers & Education, 198, 104756. Heindl, J., Bley, S., & Miesera, S. (2025). Digital transformation in the hospitality sector – effects on professional competence profiles. In C. Nägele, B. E. Stalder, F. Kaiser, M. Malloch, & N. Kersh (Eds.), Trends in vocational education and training research (Vol. 8, pp. 123–137). Kastrup, J. & Brutzer, A. (2021). Digitalisierung im Berufsfeld Ernährung und Hauswirtschaft - eine Analyse aktueller Diskurse. In M. Friese (Hrsg.), Care Work 4.0 (S. 199–213). wbv Publikation. Miesera, S. (2021). Digitalisierung der beruflichen Bildung - Gestaltung von Lehr- und Lernarrangements in der Lehrkräftebildung - berufliches Lehramt Berufsfeld Ernährung und Hauswirtschaft. didacticum, 3(1), 39–52. Miesera, S., Torggler, C. & Nerdel, C. (2021). Erfassung des Professionswissens angehender Berufsschullehrkräfte im Berufsfeld Ernährung und Hauswirtschaft – Adaption des TPACK-Modells. HiBiFo - Haushalt in Bildung und Forschung, 10(3), 81–96. Özsoy, Melissa & Murböck, Julia & Schultz-Pernice, Florian & Gräsel, Cornelia & Sailer, Michael & Fischer, Frank. (2024). Lehrkräftekooperation im Kontext digitaler Schulentwicklung - Kokon DigCompEduObserve -Ein Beobachtungsinstrument zur Förderung digitaler Kompetenzen von Lehrenden (Version 1.0). Redecker, C. (2017). European framework for the digital competence of educators: DigCompEdu. Publications Office. https://doi.org/10.2760/159770 Wittmann, E. & Weyland, U. (2020). Berufliche Bildung im Kontext der digitalen Transformation. Zeitschrift für Berufs- und Wirtschaftspädagogik, 117(2). 02. Vocational Education and Training (VETNET)
Paper Vocational Teachers as AI Entrepreneurs: A Study of Professional AI Competence in the Vocational Field of Information Technology and Media Production 1: Oslo Metropolitan University (OsloMet); 2: University of Oslo (UiO) Presenting Author:It is well accepted that artificial intelligence (AI) will have a disruptive impact on working life and skill requirements, but experts differ in their estimations of the magnitude and the dynamics of these transformations (Humlum & Vestergaard, 2025). The release of ChatGPT provided AI accessibility to the general public and has impacted the educational sector to such an extent that scholars are asking for a paradigm shift in curriculum design, instructional practices and evaluation methods (articles in UNESCO, 2025). A widespread concern in the growing research field of AI in education (AIED) is the gap between the proliferation of studies on technological advancements and the lack of critical research on their implementation in pedagogical practice (Ifenthaler et al., 2024). This state of the art is particularly true for vocational education and training (VET), which on the one hand, is more exposed to AI-infused transformations in working life (Wuttke et al., 2020) while on the other hand, represents an under-researched field in AIED (Bekiaridis & Attwell, 2024; Petridou & Lao, 2024). To counter this knowledge gap, more research is needed to develop a timely AI vocational pedagogy that is grounded in VET teachers’ AI-mediated practices and professional competences and that provides well-founded guidelines (a didactic) for vocational training quality and teachers’ competence development in an AI era. Reviews of policy-oriented and research models of teachers’ professional AI competences (Mikeladze et al., 2024) have concluded that the abstractness of such frameworks may seriously reduce their relevance in practice. Thus, Filo and Mor (2024) propose a turnaround strategy that complements the theory-driven and normative top-down approaches with bottom-up contextual studies of teaching practices in AI contexts. The present exploratory study follows this suggested redirection that seems especially valid given the complexity of VET. We study how in-service VET teachers in the Norwegian education programme Information Technology and Media Production (ITMP) in upper secondary school integrate AI in the two-year school-based part of VET. The students (age 15–18) learn about user support, IT networks, IT security and programming, storytelling, photography, film, and visual communication, and in the school-based part, they engage in shorter projects with relevant companies. The school-based education prepares the students for two years of apprenticeship in training establishments within five recognised trades. Our main research questions were: How does the use of AI in the ITMP programme impact the subject-specific and general didactical practices of vocational teachers? How are the professional digital competencies of vocational teachers transformed in the context of AI-mediated VET? The objective is to examine changes in teaching practices, teacher roles and student progression associated with AI use and to document teachers’ pathways for competence development and identify institutional barriers and enablers. An ultimate objective is to provide empirically grounded recommendations for developing VET-specific AI didactics and policy. The theoretical framework for this study is grounded in a socio-material perspective on learning and training, focusing on the co-constitution of individual cognition, social interaction, and performativity with artefacts understood as tools designed for human purposes (Säljö, 2019). As AI reshapes teaching by automating assessment and feedback, supporting personalized learning, and reconfiguring teachers’ roles as facilitators, orchestrators, and co-learners, these transformations are interacting with teachers’ professional identities and institutional norms. The framework of boundary-crossing and connectivity (Kyndt et al., 2022) provides a dynamic understanding of the transfer of knowledge and skills across the divide between work and school in VET that foregrounds the entanglement of technology, knowledge, and practice. This lens helps explain how teacher roles and didactic practices co-evolve with AI affordances rather than change in isolation. Methodology, Methods, Research Instruments or Sources Used In order to capture rich, practice-driven insights from AI-active practitioners, this exploratory study purposively oversampled pioneering teachers in the ITMP programmes of two schools. These served as sub-cases within a broader networked-case setting (schools and industry partners in the ITMP-field). In line with designs of multiple case studies (Stake, 2013), we collected data using multiple methods: (1) semi-structured personal interviews with teachers in the two schools (N= 4), transcribed, (2) researchers’ field notes from observations in the schools and two network meetings, and (3) the collection of documentary data (lesson plans, presentations from teachers and companies in the two network meetings. Interview and observation guides were informed by prior research on AI-integrated teaching and VET teachers’ professional digital competences, and organised around three analytic categories: (1) subject-specific AI use, (2) teachers’ professional roles and pedagogy in an AI context, and (3) professional development of pioneering teachers. Data analysis followed an abductive thematic approach (Thompson, 2022). Researchers first identified patterns across datasets, performed tentative coding aligned with the three categories, and grouped codes into candidate themes by seeking cross-cutting concepts and explanatory links (e.g., subject-specific digital competencies as drivers of innovative practice). Themes were iteratively reviewed across all data sources to triangulate consistent and divergent findings. Generative AI (ChatGPT-like) tools were used as a complementary aid in qualitative analysis (Yang & Ma, 2025): researchers iteratively prompted the model to summarise initial interview themes and to surface possible relationships. To mitigate hallucination and bias risks, outputs were cross-checked against manual analytic notes; AI-derived suggestions were treated as prompts for further scrutiny rather than as standalone interpretations. This assisted transparency in coding criteria and provided alternative patterns to test against researchers’ domain knowledge. Analytic distinctions were explicitly maintained between observed AI practices and participants’ stated views on AI and professional digital competencies. The study is intended to be a design base for planned replications in other vocational fields (health, construction/technology), emphasizing generalisability through comparative future work. Conclusions, Expected Outcomes or Findings The study addresses three interrelated themes: vocational didactics integrating AI, general pedagogical transformations in AI-practices and teacher competence development in an AI-context. Vocational didactic integration. AI is experienced as embedded in occupational tools and practices (e.g. code-generation aids). It supports the development of new vocational competences by reducing routine manual tasks, enabling higher-order creative work, and linking theory to practice (e.g., “Startup with AI” projects). Successful use depends on subject-specific knowledge, precise prompting skills, and critical interpretation of AI outputs. Ethical concerns (privacy, licenses) are addressed pragmatically but reveal equity issues when students rely on privately funded AI licenses. General pedagogical effects. Our analyses show, in line with previous studies (Olivier and Weilbach, 2024; Prieto et al., 2025) that AI functions as an “assistant teacher” across planning, in-class support, and formative assessment—facilitating immediate feedback, differentiated pathways, and to some extent enhancing student self-regulation. Assessment practices shift toward process documentation and reflection to guard against superficial AI use. AI also enhances inclusion (e.g., supports learners with dyslexia) while posing risks of widening achievement gaps. Teacher competence development. Competence growth is largely self-directed—driven by motivation, informal networks, industry contacts, and experimentation. Teachers increasingly act as co-learners with students, yet institutional support and formalised professional development were limited and may reflect an inertia in the face of rapid technological changes in schools and work. Our study calls for policy and resource responses to ensure equitable access, integrate ethics and critical thinking in AI pedagogy, and to formalise AI competence frameworks across vocational fields. Comparative and longitudinal studies are recommended to validate and extend these provisional patterns of vocational teachers in the field of ITMP as AI entrepreneurs. We are presently outlining a proposal with such extensions including international research collaboration with other European countries. References Bekiaridis, G. & Attwell, G. 2024. Integrating Artificial Intelligence in Vocational and Adult Education: A Supplement to the DigCompEdu Framework. UbiquityProceedings, 4(1), 20. https://doi.org/10.5334/uproc.142 Filo, Y. & Mor, Y. (2024) An Artificial Intelligence Competency Framework for Teachers and Students: Co-created With Teachers. European Journal of Open, Distance and E-Learning, 26. https://doi.org/10.2478/eurodl-2024-0012 Ifenthaler, D. et al. (2024) Artificial Intelligence in Education: Implications for Policymakers, Researchers, and Practitioners. Technology, Knowledge and Learning, 29, 1693–1710. https://doi.org/10.1007/s10758-024-09747-0 Humlum, A. and Vestergaard, E. (2025) Large Language Models, Small Labor Market Effects . Cambridge. National Bureau of Economic Research (NBER). https://doi.org/10.3386/w33777 Kyndt, E. Beausaert, S.& I. Zitter (Eds) (2022). Developing connectivity between education and work. Routledge. https://doi.org/10.4324/9781003091219-2 Mikeladze, T., Meijer, P. C., & Verhoeff, R. P. (2024). A comprehensive exploration of artificial intelligence competence frameworks for educators: A critical review. European Journal of Education, 59(3). https://doi.org/10.1111/ejed.12663 Olivier, C. & Weilbach, L. (2024). Enhancing Online Learning Experiences: A Systematic Review on Integrating GenAI Chatbots into the Community of Inquiry Framework. Lecture Notes in Computer Science, 14907. https://doi.org/10.1007/978-3-031-72234-9_7 Petridou; E. & Lao, L. (2024) Identifying challenges and best practices for implementing AI additional qualifications in vocational and continuing education: a mixed methods analysis, International Journal of Lifelong Education, 43(4), 385-400 https://doi.org/10.1080/02601370.2024.2351076 Prieto, A., Romero, A. & Bellas, F. (2025). Advancing Robotics Education: Integrating Large Language Models for Natural Language Programming in VET. Lecture Notes in Computer Science, 15347. https://doi-org.ezproxy.uio.no/10.1007/978-3-031-77738-7_44 Shen, Y., Liu, Q., Zhang, K. & Zou, R. (2023) The Application of Artificial Intelligence Technology in Personalized Teaching of Vocational Education. IEEE. https://doi.org/10.1109/ITME60234.2023.00172 Stake, R.E. (2013). Multiple Case Study Analysis. Guilford Press. ISBN 9781593852481 Säljö, R. (2019). Materiality, Learning, and Cognitive Practices: Artifacts as Instruments of Thinking. In: Cerratto et al (eds) Emergent Practices and Material Conditions in Learning and Teaching with Technologies. Springer, Cham. https://doi.org/10.1007/978-3-030-10764-2_2 Thompson, J. (2022). A Guide to Abductive Thematic Analysis. The Qualitative Report, 27(5), 1410-1421. https://doi.org/10.46743/2160-3715/2022.5340 UNESCO (2025) AI and the future of education. Disruptions, dilemmas and directions. UNESCO. https://doi.org/10.54675/KECK1261 Wuttke, E., Seifried, J., & Niegemann, H. (Red.). (2020).Vocational Education and Training in the Age of Digitization. Verlag Barbara Budrich. https://doi.org/10.3224/84742432 Yang, Y. & Ma, L. (2025) Artificial intelligence in qualitative analysis: a practical guide and reflections based on results from using GPT to analyze interview data in a substance use program. Qual Quant. https://doi.org/10.1007/s11135-025-02066-1 02. Vocational Education and Training (VETNET)
Paper Artificial Intelligence in Vocational Education and Training: A Meta-Review Using Thematic Synthesis University of Bremen, Germany Presenting Author:Topic and Problem Statement Artificial intelligence (AI) has garnered significant attention in vocational education and training (VET), resulting in a rapidly expanding body of review literature synthesising empirical findings on AI applications, opportunities, and challenges. However, this landscape is characterised by fragmentation. Existing reviews vary markedly in scope - ranging from technical analyses of adaptive learning systems to broad surveys of emerging technologies - and often lack theoretical integration (Fatokun & Gumbo, 2025; Ghosh & Ravichandran, 2024). Consequently, although the volume of literature continues to increase, the knowledge base remains disjointed, offering limited cumulative understanding of how AI interacts with the institutional logics that shape vocational systems. Objective and Research Questions The objective of this study is to conduct a qualitative meta-review of review literature on AI in VET. By synthesising review-level evidence, the study moves beyond descriptive summaries to develop an integrative understanding of how AI is conceptualised and evaluated within vocational contexts. The inquiry is guided by three research questions:
Theoretical Framework To address the observed fragmentation, the study adopts a Three-Level Framework derived from the Multi-Level Perspective on socio-technical transitions developed by Geels (2002). This framework organises the evidence across three analytical dimensions:
The framework structures the identification of dominant themes (RQ1) and patterns of convergence and divergence (RQ2) across levels. It also supports the development of explanatory mechanisms linking technological potential to educational outcomes: didactic mediation, organisational translation, and governance framing. International Dimension The study places strong emphasis on the international dimension of VET. Comparative evidence highlights that AI effectiveness is highly context-dependent. Vocational institutions in resource-constrained settings are particularly vulnerable to widening inequalities due to infrastructure deficits and policy vacuums. This perspective is essential for understanding divergent AI adoption pathways between the Global North and Global South (Gao & Tan, 2025; Rabiu et al., 2025). Methodology, Methods, Research Instruments or Sources Used This study employs a qualitative meta-review design (review of reviews). Unlike a quantitative meta-analysis, it does not aggregate primary data statistically. Instead, it synthesises review-level findings to identify cross-cutting patterns, conceptual tensions, and theoretical gaps across a heterogeneous body of literature. Corpus Selection The corpus consists of review publications explicitly addressing AI in VET, TVET, or vocational higher education. A broad search strategy was used to ensure comprehensive coverage. Searches were conducted in curated databases (Scopus and Web of Science) as well as crawler-based academic search engines (Google Scholar and Semantic Scholar). The final corpus includes 26 review studies, encompassing systematic reviews (with and without PRISMA) and also narrative reviews. Analytic Approach Data analysis followed a second-order thematic synthesis based on the method developed by Thomas and Harden (2008). Instead of primary studies, the analytic units were the findings, results, and discussion sections of the included review articles. The process comprised three stages: 1. Line-by-line coding of review findings to capture key meanings and concepts. 2. Development of descriptive themes through clustering related codes across studies. 3. Generation of analytical themes that extend beyond the original reviews to develop higher-order interpretations aligned with the three-level framework. Conclusions, Expected Outcomes or Findings The meta-review identifies strong convergence in framing AI as a pedagogical support technology, particularly in relation to adaptive learning and automated feedback. However, effectiveness is consistently described as conditional rather than inherent. Educational outcomes are mediated by: Organisational Readiness : Successful implementation depends on leadership, infrastructure, and a supportive digital workplace culture. Teacher adaptation is shaped by technology acceptance and change readiness (Rahayu, 2025). Contextual Vulnerabilities: VET systems are especially susceptible to equity risks. Without targeted investment, AI adoption may exacerbate the digital divide in resource-constrained contexts (Gao & Tan, 2025). The analysis also reveals persistent structural tensions, including trade-offs between efficiency and pedagogical judgement, and between personalisation and the standardisation embedded in vocational qualification frameworks. Additionally, tensions arise between rapid technological innovation and slower-moving governance systems, limiting scalability when regulatory frameworks lag behind institutional experimentation (Rabiu et al., 2025). Implications The primary challenge in this field is not the absence of empirical research, but the lack of theoretical integration. Future research should adopt cross-level designs that connect classroom applications with organisational and governance structures. For policymakers, the findings underscore the importance of robust ethical and regulatory frameworks to prevent policy vacuums and ensure equitable access to AI-enhanced vocational education and training (Rabiu et al., 2025; Solak Berigel et al., 2025). References Fatokun, J. O., & Gumbo, M. T. (2025). A systematic review of the impact of emerging technologies in shaping technical and vocational education and training students’ learning styles. Educational Challenges, 30(2). https://doi.org/10.34142/2709-7986.2025.30.2.17 Gao, H., & Tan, Y. (2025). AI differences in vocational and undergraduate education: Differential applications of artificial intelligence in undergraduate and vocational higher education: A systematic review. Higher Education Studies, 15(4), 398. https://doi.org/10.5539/hes.v15n4p398 Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study. Research Policy, 31(8-9), 1257–1274. https://doi.org/10.1016/S0048-7333(02)00062-8 Ghosh, L., & Ravichandran, R. (2024). Emerging technologies in vocational education and training. Journal of Digital Learning and Education, 4(1), 41-49. https://doi.org/10.52562/jdle.v4i1.975 Hamdan, A., Elmunsyah, H., Maula, P. I., Sari, H. V., Abdullah, M. F., & Septianingsih, D. (2025). Systematic evaluation of adaptive assessment in e-learning: Contribution to vocational education. Proceedings of the 9th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), 1-6. https://doi.org/10.1109/ICEEIE66203.2025.11254863 Mustakim, W., Giatman, Maksum, H., Abdullah, R., & Refdinal. (2024). Integration of policy and technology in vocational education leadership: A systematic literature review. Jurnal Pendidikan Teknologi Informasi dan Vokasional. https://doi.org/10.23960/jptiv.v6i2.33016 Rabiu, A., Bawa, K., & Saminu, S. (2025). Artificial intelligence adoption for skills development in Nigeria: A systematic review and roadmap for TVET transformation. International Journal of Research and Innovation in Social Science, IX(VIII). https://doi.org/10.47772/IJRISS.2025.908000051 Rahayu, S. (2025). AI readiness, virtual leadership, and digital workplace culture in vocational education: A systematic review on how technology acceptance and change readiness shape future teaching skills. Proceedings of the International Conference on Economic Business Management, and Accounting (ICOEMA) 2025. https://e-conf.usd.ac.id/index.php/icre/ICRE2025/paper/download/5569/924 Ranuharja, F., Ganefri, Rizal, F., Langeveldt, D., Ejjami, R., Torres-Toukoumidis, A., & Jalinus, N. (2025). Relevance and impact of generative AI in vocational instructional material design: A systematic literature review. Salud, Ciencia y Tecnología, 5, 1336. https://doi.org/10.56294/saludcyt20251336 Solak Berigel, D., Şilbir, L., & Şilbir, G. M. (2025). Integrating artificial intelligence (AI) into technical and vocational education and training (TVET): A PRISMA-based systematic review. Calitatea Vieții, 36(1), 83-108. https://doi.org/10.46841/RCV.2025.01.05 Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8(45), 1–10. https://doi.org/10.1186/1471-2288-8-45 | ||