Antecedents Influencing the Intention of Artificial Intelligence (AI) Adoption in Learning among University Students

Lui, Wen Hui (2025) Antecedents Influencing the Intention of Artificial Intelligence (AI) Adoption in Learning among University Students. Masters thesis, Tunku Abdul Rahman University of Management and Technology.

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Abstract

The rapid growth of artificial intelligence (AI) has created new opportunities in higher education, where AI technologies are increasingly applied to improve learning outcomes. Yet, the extent to which students are willing to adopt AI in learning remains uncertain. This study investigates the antecedents influencing students’ intention to adopt AI in Malaysian universities, drawing on the Technology–Organisation–Environment (TOE) framework and Social Cognitive Theory (SCT). Six antecedents were examined—relative advantage, compatibility, organisational support, resource availability, social influence, and government regulation—with self-efficacy as a mediator and cost concern as a moderator. Using a quantitative and cross-sectional design, data were collected from 320 valid responses through convenience and snowball sampling. The results show that all six antecedents significantly influence self-efficacy, which in turn positively predicts intention to adopt AI. Mediation analysis confirmed that self-efficacy transmits the effects of these antecedents to adoption intention, highlighting its role as a psychological mechanism that links contextual factors to behavioural outcomes. However, cost concern did not significantly moderate the relationship between self-efficacy and intention, suggesting that financial considerations are less critical in academic contexts where AI tools are institutionally supported. Overall, the integrated TOE–SCT model explained 50.8 percent of the variance in adoption intention, demonstrating strong predictive power. The findings contribute theoretically by extending the TOE framework with self-efficacy and practically by offering guidance for universities and policymakers. Universities are encouraged to emphasise AI’s advantages, ensure compatibility with learning practices, provide resources and training, and foster supportive environments to strengthen students’ confidence and adoption intentions.