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

A cross-sectional survey dataset of 600 valid questionnaires from medical students. The data examines factors associated with self-directed learning and AI-related behavioral intention using an integrated TAM-UTAUT2 model, analyzed via PLS-SEM. The dataset was authored by Jin Zhu and last updated in April 2026.

Use Cases

• Modeling technology acceptance pathways for AI in education based on constructs like perceived ease of use and usefulness.
• Analyzing predictors of self-directed learning dimensions (self-planning, self-management, self-monitoring) based on motivational and attitudinal factors.
• Testing structural equation models integrating educational psychology and technology acceptance theories.
• Investigating the role of facilitating conditions and social influence on behavioral intention towards AI tools.

Strengths

• Contains 600 valid survey 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

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is from a single cross-sectional study, limiting causal inference as noted by the authors.

Provenance

Source

figshare

Collection Method

Cross-sectional survey design, analyzed using partial least squares structural equation modeling (PLS-SEM).

Time Range

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Freshness

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

Geography

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