Survey Data on AI Learning Behaviors of 184 Health Management Students

184 valid survey responses from health management students at Zaozhuang University, collected to study AI learning and usage behaviors. The dataset underpins a predictive model built with XGBoost and SHAP interpretability, developed by Jinsong Du and last updated in April 2026. It is shared under a CC-BY-4.0 license.

Use Cases

• Predicting student engagement with AI tools based on factors like knowledge mastery and ethical awareness.
• Identifying key behavioral drivers for AI learning using SHAP interpretability analysis.
• Developing personalized educational intervention systems based on model predictions.
• Analyzing the relationship between educational background and AI application behavior.

Strengths

• Model performance metrics (accuracy, precision, recall, F1-score) all exceeded 0.698, indicating strong predictive capability.
• Dataset is explicitly tied to a theoretical framework, the 'Knowledge-Belief-Action' model.
• Includes interpretable model results via SHAP analysis, highlighting key factors like ethical awareness.

Limitations

• Sample data is from a single university, which may limit the generalizability of findings.
• Row count and column-level documentation are unknown, requiring inspection after download.
• The primary file is a 14.0 MB DOCX document, which may not be in a standard data format for direct analysis.

Provenance

Source

Survey conducted at the School of Health Management, Zaozhuang University.

Collection Method

Questionnaire based on the 'Knowledge-Belief-Action' model.

Time Range

null

Freshness

Last updated 2026-04-22 05:45:58

Geography

China (Zaozhuang University)