Medical Student Survey on AI-Assisted Education and Self-Directed Learning

A cross-sectional survey of 600 medical students examining factors associated with self-directed learning and AI-related behavioral intention. The data was analyzed using partial least squares structural equation modeling (PLS-SEM) by author Jin Zhu and uploaded to figshare in April 2026. The study tests an integrated model combining self-directed learning dimensions with TAM and UTAUT2 constructs.

Use Cases

• Modeling technology acceptance pathways based on perceived ease of use and usefulness constructs mentioned in the description
• Analyzing predictors of self-directed learning dimensions like self-planning and self-management
• Studying the relationship between motivational support and AI-assisted learning behavior in medical education

Strengths

• Contains 600 valid questionnaire responses, providing a substantive sample size for analysis
• Model explained substantial variance in key constructs, ranging from 47.6% to 69.9%
• Results supported 21 of 24 hypothesized paths, indicating a well-tested theoretical framework

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment
• Data is from a single cross-sectional study; the description notes the need for longitudinal research to clarify directionality

Provenance

Source

figshare

Collection Method

Cross-sectional survey design

Time Range

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Freshness

Last updated 2026-04-24 14:57:55; freshness should be verified

Geography

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