Integrating AI-Driven Adaptive Learning and Self-Determination Theory to Enhance Employability Skills: Evidence from Vocational Higher Education - Muhammad Hasim; Ahmad Ardillah Rahman; Iris Sumariyanto; Samnur Samnur; Misita Anwar; Aminuddin Aminuddin (The Faculty of Teacher Training and Education (FKIP), Universitas Sebelas Maret, Indonesia, 2026)
This study tests an integrated structural model linking Artificial Intelligence (AI) use in learning, psychological factors derived from Self-Determination Theory (SDT), and employability skills among vocational education students. Specifically, AI is positioned as a contextual support associated with the fulfillment of competence, autonomy, and relatedness. A quantitative, cross-sectional design was employed using self-reported data from 263 vocational students in Indonesia, which were analyzed through Structural Equation Modeling (SEM). The results show that competence, autonomy, and relatedness are significantly associated with employability skills, with competence emerging as the strongest predictor. AI use demonstrates a significant indirect effect on employability skills, primarily through competence. In contrast, burnout, learning anxiety, and learning motivation do not show significant direct associations with employability skills, suggesting that psychological need fulfillment plays a more central role in the model. This study contributes to the literature by situating AI within the SDT framework as a contextual enabler of psychological need fulfillment in vocational education. The findings indicate that AI-supported learning environments are associated with stronger fulfillment of competence, autonomy, and relatedness, which in turn are linked to employability development. Practically, the results highlight the importance of designing AI-assisted learning environments that support competence, autonomy, and relatedness