Faculty-Student Negotiation Data on Generative AI Trust in Chinese Universities

600 teachers and students from three universities of different institutional levels in Central China participated in this study. The dataset, created by Hehan Zhang and last updated in April 2026, contains empirical data on negotiation mechanisms and trust in generative AI systems within educational settings. It was collected using questionnaires, behavioral logs, and controlled experiments.

Use Cases

• Analyze the correlation between disciplinary_background and reported trust_in_AI scores from survey responses.
• Model the moderating effect of institutional_level and AI_usage_experience on negotiation outcomes using behavioral_log data.
• Compare trust metrics from democratic_negotiation groups versus teacher_led_approach groups in the controlled_experiment data.

Strengths

• Data from 600 participants provides a substantive sample for analysis.
• Mixed-methods design incorporates questionnaire, behavioral log, and controlled experiment data.
• Explicit CC-BY-4.0 license permits open sharing and adaptation.

Limitations

• The 270.8 KB file size indicates a limited dataset, likely containing aggregated or summary statistics rather than raw individual responses.
• Geographic scope is limited to three universities in Central China, limiting generalizability.
• Specific column names and data structure are unknown, complicating immediate analysis.

Provenance

Source

Hehan Zhang via figshare.

Collection Method

Collected via questionnaires, behavioral logs, and controlled experiments.

Time Range

Study period not specified; dataset published in 2026.

Freshness

Data is current, last updated in April 2026.

Geography

Three universities in Central China.