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Examining the association between AI-enhanced education and medical students' self-directed learning using an
Jin Zhu1, Chongyuan Guan1, Hao Zhang1
1School of Public Health, Dalian Medical University, Dalian, China.
Frontiers in Medicine
|May 11, 2026
Summary
Artificial intelligence (AI) in medical education enhances self-directed learning. Motivation and attitude are key drivers, influencing technology adoption and learning behaviors among students.
Area of Science:
- Medical Education
- Educational Technology
- Artificial Intelligence
Background:
- Artificial intelligence (AI)-assisted education is a growing instructional model in medical education.
- Its impact on students' self-directed learning and technology adoption requires investigation.
Purpose of the Study:
- To examine factors influencing self-directed learning and AI behavioral intention in medical students.
- To test an integrated model of self-directed learning, TAM, and UTAUT2 constructs.
Main Methods:
- Cross-sectional survey design with 600 medical students.
- Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4.
- Integrated model incorporating self-directed learning, TAM, and UTAUT2.
Main Results:
- Motivation was the strongest predictor, significantly associated with attitude and self-directed learning dimensions.
- Perceived ease of use and usefulness predicted behavioral intention, which predicted actual behavior.
- The integrated model explained substantial variance in self-management (69.9%) and actual behavior (47.6%).
Conclusions:
- Motivational support, structured planning, and digital infrastructure are crucial for AI integration in health sciences education.
- The integrated model provides preliminary evidence for explaining AI-assisted learning behavior.
- Longitudinal research is needed to confirm causal relationships.
