STRUCTURE AND CONTENT OF FUTURE VOCATIONAL TRAINING TEACHERS' READINESS TO USE AI-ASSISTED TECHNOLOGIES IN THE INCLUSIVE ENVIRONMENT OF VET INSTITUTIONS: A THEORETICAL AND METHODOLOGICAL ANALYSIS
Abstract
DOI: https://doi.org/10.26565/2074-8922-2026-86-07
Purpose. The article presents a theoretical and methodological analysis of the structure and content of future vocational training teachers' readiness to use artificial intelligence (AI)-assisted technologies and adaptive digital tools in the educational process of vocational education and training (VET) institutions within inclusive environments. The study aims to clarify the essence and structural components of the investigated phenomenon on the basis of a systematic analysis of scholarly approaches; to construct an original structural-component model of the target readiness; and to substantiate pedagogical conditions for its development in higher education institutions that prepare future VET teachers.
Methods. The research employed a set of theoretical methods: analysis and synthesis of domestic and international scholarly literature on teacher readiness, inclusive education, AI in education, and digital pedagogical competence; comparative analysis of conceptual frameworks and policy documents (DigCompEdu (2017), UNESCO AI Competency Framework for Teachers 2024, CAST UDL Guidelines 3.0); systematization and generalization of scientific views on the structure of the investigated phenomenon; and conceptual modelling as the primary method for constructing the authorial structural-component model. The methodological foundation integrates the competence-based, systemic, and inclusive approaches.
Results. Drawing on a systematic analysis of recent scholarly literature, the essence of the concept «future VET teacher's readiness to use AI-assisted technologies in an inclusive environment» was clarified as an integrated personal-professional quality that cannot be reduced either to general digital competence or to general inclusive competence. A four-component structural model of this readiness was developed: (1) motivational-axiological component (inclusive values, motivation to master AI tools); (2) cognitive-content component (knowledge of special educational needs categories, AI tool types, and regulatory frameworks); (3) operational-technological component (practical skills of tool selection and application in both theoretical and vocational practical training); and (4) reflective-evaluative component (capacity for critical self-analysis and professional development orientation). The model is aligned with the DigCompEdu, UNESCO AI Competency Framework, and UDL frameworks. A comparative analysis revealed that the inclusive educational process in VET institutions presents qualitatively distinct challenges compared to general secondary education. These challenges — rooted in the industrial character of vocational training, sector-specific occupational safety regulations, and qualification standards — are systematized in Table 1 and define a unique dual role for AI-assisted technologies: not merely as didactic tools, but as instruments of ergonomic adaptation of the learning environment to the needs of students with special educational needs. This dimension of VET-specific inclusive practice constitutes the principal novelty of the study.
Conclusions. The theoretical analysis confirms that future VET teachers' readiness to use AI-assisted technologies in inclusive settings constitutes a distinct and insufficiently studied scientific construct. The existing system of VET teacher preparation does not ensure the systematic development of this readiness — neither a theoretical framework nor practical pedagogical tools are currently in place. The substantiated structural-component model and three theoretically grounded pedagogical conditions (enrichment of professional training content with a specialized module «AI in Inclusive VET»; case-based learning with simulation of real-life inclusive vocational training situations; development of a reflective professional stance through digital portfolio and peer-review technologies) provide a theoretical foundation for subsequent empirical research — a diagnostic survey and a formative experiment.
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References
Dubina, L., Zabarovska, S., Poklad, S. (2022). Inclusive educational environment for education of persons with special educational needs. Professional education, 2, 32–34. https://lib.iitta.gov.ua/id/eprint/734221/1/PTO_2_2022.pdf (in Ukrainian).
Kolupaieva, A. A., Taranchenko, O. M. (2016). Inclusive education: from basics to practice : monohraf. Kyiv : Atopol. http://lib.iitta.gov.ua/708170/ ([in Ukrainian).
Morze, N. V., Kocharian, A. B. (2014). ICT competence standards for higher educators and quality assurance in education. Information Technologies and Learning Tools, 43(5), 27–39. https://doi.org/10.33407/itlt.v43i5.1132 (in Ukrainian).
Petrenko, L. M., Yahupov, V. V., Kravets, S. H. (2019). Distance learning in the system of vocational education. Zhytomyr : Polissia. https://lib.iitta.gov.ua/id/eprint/721757 (in Ukrainian).
Rudenok, A., Hulyak, I. (2021). Components of professional readiness of future psychologists for work with children with special educational needs. Psychology Travelogs, (1), 72–82. https://doi.org/10.31891/PT-2021-1-7 (in Ukrainian).
Tsykin, D. S., Dashkov, A. O., Azhazha, M. A. (2025). Inclusive education as a tool for social cohesion under martial law: the communicative aspect. Theoretical and applied issues of state-building, 33. https://doi.org/10.35432/tisb332025332703 (in Ukrainian).
CAST. (2024). Universal Design for Learning Guidelines version 3.0. Lynnfield, MA. https://udlguidelines.cast.org
Chen, L., Chen, P., Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Crompton, H., Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00392-8
Florian, L., Camedda, D. (2020). Enhancing teacher education for inclusion. European Journal of Teacher Education, 43(1), 4–8. https://doi.org/10.1080/02619768.2020.1707579
Holmes, W., Porayska-Pomst,a K., Holstein, K. et al. (2022). Ethics of AI in Education: Towards a Community-Wide Framework . International Journal of Artificial Intelligence in Education, 32, 504–526. https://doi.org/10.1007/s40593-021-00239-1
Hwang, G. J., Xie, H., Wah, B. W., Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
Miao, F., Holmes, W., Huang, R., Zhang, H. (2021). AI and education: guidance for policy-makers. Paris : UNESCO Publishing. https://doi.org/10.54675/PCSP7350 https://unesdoc.unesco.org/ark:/48223/pf0000376709
Salas-Pilco, S. Z., Xiao, K., Hu, X. (2022). Artificial intelligence and learning analytics in teacher education: A systematic review. Education Sciences, 12(8), 569. https://doi.org/10.3390/educsci12080569
Samuelsson, M., Samuelsson, J., Thorsten, A. (2022). Simulation training: A boost for pre-service teachers’ efficacy beliefs. Computers and Education Open, 3, 100074. https://doi.org/10.1016/j.caeo.2022.100074
Su, J., Yang, W. (2022). Artificial intelligence in early childhood education: A scoping review. Computers and Education: Artificial Intelligence, 3, 100049. https://doi.org/10.1016/j.caeai.2022.100049
UNESCO. (2024). AI Competency Framework for Teachers. Paris. https://www.unesco.org/en/digital-education/ai-competency-framework-teachers
Vuorikari, R., Kluzer, S., Punie, Y. (2022). DigComp 2.2: The Digital Competence Framework for Citizens - With new examples of knowledge, skills and attitudes, EUR 31006 EN. Publications Office of the European Union, Luxembourg, 2022. https://doi.org/10.2760/115376
Watters, J., Hill, A. A., Weinrich, M., Supalo, C., Jiang, F. (2021). An artificial intelligence tool for accessible science education. Journal of Science Education for Students with Disabilities, 24(1), 1–14. https://doi.org/10.14448/jsesd.13.0010
Zawacki-Richter, O., Marín, V. I., Bond, M., Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education — where are the educators? International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0
