Assessing Student's Behavioral Intentions Towards AI Based Learning Tools

Abstract

Integrating Artificial Intelligence (AI) in education has revolutionized learning environments, offering personalized, adaptive, and automated academic assistance. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating trust, perceived risk, moral obligation, hedonic motivation, and habit to provide a comprehensive understanding of AI adoption among university students in Pakistan. Employing a quantitative, cross-sectional survey approach, data were collected from students across various disciplines and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and SPSS. The findings reveal that habit is the strongest predictor of AI adoption. This demonstrates that students engage with AI-based learning tools primarily through repeated exposure and routine usage rather than external encouragement. Unlike traditional UTAUT predictors, such as performance expectancy and social influence, which were not statistically significant, habit formation emerged as the dominant driver of AI engagement. Additionally, trust and perceived risk exhibited a positive correlation, indicating that while students trust AI tools, they simultaneously acknowledge risks related to data privacy, misinformation, and ethical concerns. The study challenges conventional technology acceptance models, highlighting that self-directed learning behaviors and habitual engagement play a more significant role in AI adoption than previously assumed. These findings have important theoretical and practical implications for educational policymakers, AI developers, and institutions seeking to enhance AI-driven learning experiences. The study suggests that institutions should focus on seamless AI integration, improving user engagement, and promoting responsible AI usage rather than relying on external motivational factors.