<p>Convergent validity test table.</p>

Chinese university students provided 207 valid questionnaires for a study on Generative AI-assisted learning. The data, analyzed via structural equation modeling, examines how role adaptation, self-efficacy, task-technology fit, and institutional support influence exploration and exploitation behaviors and learning outcomes. Author Yixuan Zeng published the dataset on figshare in April 2026 under a CC-BY-4.0 license.

Use Cases

• Testing structural equation models based on the described triadic framework of Exploration–Exploitation–Adaptation.
• Analyzing correlations between internal drivers (role adaptation, self-efficacy) and learning behaviors mentioned in the description.
• Investigating the influence of external enablers (institutional support, task-technology fit) on student engagement with AI tools.
• Comparing the relative impact of exploration versus exploitation behaviors on learning outcomes as indicated by the study.

Strengths

• Dataset is based on 207 valid questionnaires, providing a specific sample size.
• The underlying study model demonstrates good fit and significant path relationships among variables.
• Data is published under a permissive CC-BY-4.0 license, facilitating reuse.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The study results reflect correlational evidence and do not establish a causal mechanism, as noted in the description.

Provenance

Source

figshare

Collection Method

Survey data collected via questionnaires from Chinese university students.

Time Range

null

Freshness

Last updated 2026-04-28 17:42:02; freshness should be verified.

Geography

China