AI-Enabled Learning Support and Student Learning Performance in Malaysian Higher Education: The Mediating Roles of Self-Regulated Learning and Academic Engagement
Objective: To develop a conceptual model examining the relationships between AI learning support, digital competence, teacher AI guidance, and learning performance among students in Malaysian higher education institutions. Methods: The proposed study adopts a quantitative cross-sectional design. Data will be collected through a survey of Malaysian university students, and the hypothesised relationships will be tested using structural equation modelling. The model includes academic engagement and self-regulated learning as sequential mediators and academic integrity concern as a moderator of the relationship between academic engagement and learning performance. Results: Drawing on self-regulated learning theory, student engagement theory, social cognitive theory, AI literacy, digital competence, and academic integrity literature, the proposed model suggests that AI learning support does not automatically improve students’ learning performance. Its effectiveness depends on students’ ability to use AI strategically, critically, and ethically, together with appropriate guidance from lecturers. Conclusion: The proposed model contributes to the AI education literature by shifting attention from students’ intention to adopt AI towards the learning mechanisms through which AI use may influence academic outcomes. It also provides a framework for understanding the roles of academic engagement, self-regulated learning, teacher guidance, digital competence, and academic integrity in AI-supported learning.