Survey on Generative AI in Chinese Higher Education, 207 Student Responses

207 valid questionnaires from Chinese university students, analyzed via structural equation modeling. The dataset investigates behavioral mechanisms and pathways influencing learning outcomes among students engaged in Generative AI-assisted learning. It was authored by Yixuan Zeng and last updated on April 28, 2026.

Use Cases

• Analyzing the relationship between role adaptation and exploration/exploitation behaviors based on the triadic framework described.
• Modeling the influence of institutional support and task-technology fit on learning outcomes as described in the study.
• Investigating the comparative effects of exploration versus exploitation behaviors on learning effectiveness as highlighted in the findings.

Strengths

• Dataset is based on 207 valid questionnaires, providing a specific sample size for analysis.
• The model demonstrates good fit and significant path relationships among variables, as stated in the description.
• Data is shared under a permissive CC-BY-4.0 license, facilitating reuse.

Limitations

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

Provenance

Source

Yixuan Zeng

Collection Method

Empirical study using questionnaires from Chinese university students, analyzed via structural equation modeling.

Time Range

Survey collection date is unspecified; dataset metadata updated in 2026.

Freshness

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

Geography

Data collected from Chinese university students.