Learning Autonomy and Effectiveness in AI-Supported Engineering Education Integrating Technology Acceptance and Motivation

Purpose – This study examines the influence of learning autonomy on learning effectiveness in artificial intelligence supported learning among engineering students by extending the Technology Acceptance Model with motivational and psychological factors.Design/methods/approach – A quantitative cross-sectional survey was conducted involving 90 engineering students from a public university in Indonesia who had experience using artificial intelligence tools for academic learning. Data were analyzed using partial least squares structural equation modeling to examine the relationships among perceived usefulness, self-efficacy, willingness for autonomous learning, and learning effectiveness and autonomy.Findings – The results indicate that perceived usefulness, self-efficacy, and willingness for autonomous learning all have significant positive effects on learning effectiveness and autonomy. Willingness for autonomous learning emerged as the strongest predictor, highlighting the central role of students’ internal motivation and readiness to manage their own learning processes in AI-supported environments.Research implications/limitations – The study is limited by its cross-sectional design, reliance on self-reported data, and a sample restricted to engineering students from a single institution, which may limit generalizability.Originality/value – This study extends the Technology Acceptance Model by integrating learning autonomy and motivational factors within an artificial intelligence supported learning context, offering empirical evidence to inform the design of balanced and student-centered AI-enhanced learning in higher education.