University Students’ Engagement in AI-Assisted Learning: A Situated Expectancy-Value Theory (SEVT) Perspective

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  In Poster Session: Motivation, Mindset, and Self-Regulated Learning

Abstract

Artificial intelligence (AI) is reshaping higher education as students increasingly use AI tools for learning. However, research on student engagement in AI-assisted learning and its psychological mechanisms remains limited. Grounded in Situated Expectancy-Value Theory (SEVT), this study employed structural equation modeling (SEM) to explore the relationships among AI-supportive environment, expectancy-value beliefs, and student engagement in AI-assisted learning among 602 university students. Results showed an AI-supportive environment significantly enhanced student engagement via expectancy-value beliefs. Multi-group SEM confirmed the robustness of the finding across gender, academic discipline, and AI experience. These findings highlight the significance of supportive environments and expectancy-value beliefs in determining university student engagement in AI-Assisted Learning.

Authors

• TING TIAN, Chinese University of Hong Kong

• Jiajing LI, Beijing Normal University

• Vivian WY LEE, The Chinese University of Hong Kong

• Ronnel B. King, Chinese University of Hong Kong

• Ching Sing Chai, Chinese University of Hong Kong

• Ziqi Mao, The Chinese University of Hong Kong