ARTIFICIAL INTELLIGENCE-SUPPORTED TEACHING AND ITS INFLUENCE ON STUDENT ENGAGEMENT AND LEARNING OUTCOMES

Abstract

Abstract

There is a growing recognition of the potential of artificial intelligence (AI) in higher education, but there are few empirical studies in the context of transition, and teachers' roles are under-represented across the current literature. A dual-informant, cross-sectional mixed-methods design was used for the present study to explore the impact of AI-assisted teaching on student engagement and learning outcomes in higher education institutions in Azerbaijan. The participants of this study were 250 people comprising 188 students, 52 teachers, and 10 teaching assistants who were selected from purposive sampling of who were suitable; a questionnaire was administered on the basis of a Technology Acceptance Model (TAM). Quantitative data were analysed using descriptive statistics, Mann–Whitney U tests, Spearman correlations and Kruskal–Wallis tests, and open-ended responses were analysed using Braun and Clarke's (2006) thematic analysis. The constructs were significantly higher than the neutral score. Students reported significantly lower cognitive engagement (p = .047, r = 0.178) and overall engagement (p = .011, r = 0.229) than teachers. No significant relationships were found between the TAM constructs and engagement or learning outcomes. Four themes emerged from the qualitative analysis: enhancing engagement, accuracy and integrity issues, improving conditional learning, and policy needs for implementation. In particular, 29.6% of the participants mentioned a lack of critical thinking as their primary issue. The above trends indicate that positive attitudinal acceptance of AI does not necessarily translate to concrete improvements in education, despite careful pedagogical scaffolding and institutional preparedness, especially in transitional education systems. This null result suggests that attitudinal acceptance of AI does not automatically translate into measurable educational gains, pointing to the need for contextual extensions of the TAM framework in transitional education systems.