Motivation and Perceived Learning Benefits in the Use of AI-Assisted Learning Tools: Evidence from Higher Education in Malaysia

Abstract

This study investigates the reciprocal relationship between student motivation and perceived learning benefits (PLB) of AI-assisted learning tools in higher education. Building on the Technology Acceptance Model (TAM) and motivational theories (ARCS and Self-Determination Theory), the research develops a novel reciprocal model that distinguishes PLB from broader constructs such as engagement. A structured survey of 325 Malaysian undergraduates was analysed using correlation and regression analyses. Motivation was measured through critical thinking and understanding complex topics, while PLB captured helpfulness, assignment quality, subject understanding, and academic performance. Reliability results indicated acceptable consistency for motivation (α = .660) and moderate consistency for PLB (α = .604). Findings revealed a moderate, positive correlation between motivation and PLB (r = .428, p < .001). Regression analyses confirmed significant reciprocal effects (β = .428, p < .001), with both models explaining 18.3% of the variance (R² = .183). While the explained variance is modest, it reflects typical effect sizes in behavioural research and highlights the reinforcing cycle between motivation and PLB. The study contributes theoretically by refining PLB as a distinct construct and empirically demonstrating its reciprocal link with motivation in the context of Malaysian higher education. Practically, the findings suggest that integrating AI tools strategically can enhance both motivation and learning benefits, while also underscoring the need for responsible and ethical adoption. Future studies should expand item sets and adopt longitudinal and cross-cultural designs to strengthen construct validity and explanatory power.