Sample Characteristics: Survey on Generative AI Use in Undergraduate Thesis Work

934 participants, including 684 graduating students and 250 thesis teachers, were surveyed on Generative AI engagement and thesis quality perceptions. The dataset, created by Guangjian Yan and last updated in April 2026, contains structured scale measurements for variables like Extent of GenAI Involvement and Perceived Effect on Thesis Quality. A five-step hierarchical regression analysis was employed to estimate main effects and test interactions across institutional tiers and disciplines.

Use Cases

• Analyze associations between Generative AI use and perceived thesis quality based on the measured variables EX, EF, AB, AT, and PR.
• Investigate heterogeneity in teacher-student evaluations across different institutional tiers and disciplinary categories.
• Examine the role of institutional policy presence as a potential moderator in the relationship between AI use and perceived quality.

Strengths

• Dataset includes 934 participants from a stratified random sample across institutional tiers and disciplines.
• Key variables were measured using structured scales, and the analysis achieved a final model explanatory power of R² = .510.
• Data is openly licensed under CC-BY-4.0.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Data is cross-sectional and self-reported, capturing perceived rather than objective thesis quality.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare, author Guangjian Yan.

Collection Method

Data gathered via a survey using a stratified random sampling method across institutional tiers and disciplines.

Time Range

Survey period not specified; dataset metadata updated April 2026.

Freshness

Last updated 2026-04-28 17:50:04; freshness should be verified.

Geography

Institutional context is mentioned, but specific geographic coverage is not detailed.