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

An empirical study from 2026 analyzes the behavioral mechanisms of university students using Generative AI for learning. The dataset contains 207 valid questionnaire responses from Chinese university students, analyzed via structural equation modeling. It was authored by Yixuan Zeng and shared under a CC-BY-4.0 license.

Use Cases

• Modeling the relationship between student self-efficacy and AI exploration/exploitation behaviors based on survey constructs.
• Analyzing the impact of institutional support and task-technology fit on learning outcomes based on the described framework.
• Investigating the role of student role adaptation in AI-assisted learning environments based on the triadic framework.

Strengths

• Dataset is based on 207 valid questionnaire responses, providing a specific sample size.
• Analysis was performed using structural equation modeling, suggesting a structured quantitative approach.
• The model demonstrates good fit and significant path relationships among variables, as stated in the description.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The description notes the results reflect correlational evidence and do not establish a causal mechanism.
• The dataset is very small at 13.5 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Survey questionnaire distributed to Chinese university students.

Time Range

2026

Freshness

Last updated 2026-04-28 17:42:01; freshness should be verified.

Geography

China