Artificial Intelligence-Supported Teaching and Its Influence on Student Engagement and Learning Outcomes
International Online Journal of Education and Teaching, vol.13, no.3, pp.340-314, 2026 (Scopus)
- Publication Type: Article / Article
- Volume: 13 Issue: 3
- Publication Date: 2026
- Journal Name: International Online Journal of Education and Teaching
- Journal Indexes: Scopus, EBSCO Education Source, Educational research abstracts (ERA), MLA - Modern Language Association Database, MLA International Bibliography, Education Source Ultimate (EBSCO)
- Page Numbers: pp.340-314
- Keywords: AI in education, Learning outcomes, pedagogical scaffolding, Technology Acceptance Model, Technology adoption in education, Transitional education systems
- Bursa Uludag University Affiliated: Yes
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 composed of 250 people (188 students, 52 teachers, and 10 teaching assistants) who were selected via purposive sampling method. The data were collected using a questionnaire which 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 openended 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 show that, despite careful pedagogical scaffolding and institutional preparation, positive adoption of AI does not lead to concrete improvements in education, especially in transitional education systems. This null result suggests that adoption of AI does not automatically translate into measurable educational achievements and highlights the need for contextual expansion of the TAM framework in transitional education systems.