56 first-year master's students participated in a pilot study at Kunming Medical University evaluating an AI-assisted World Café teaching model in a clinical pharmacology course. The study employed a multi-method assessment strategy including a questionnaire, knowledge test, and analysis of AI-generated scores. Results show improved post-test accuracy on depression etiology knowledge and high concordance between AI and instructor ratings.
Use Cases
- Analyze teaching effectiveness perception based on questionnaire data mentioned in the description
- Compare AI-generated and instructor-generated assessment scores based on case discussion records
- Evaluate changes in clinical decision-making competencies based on self-rated scores
- Assess knowledge acquisition improvements based on pre- and post-test accuracy on depression etiology
Strengths
- Includes results from 56 first-year master's students
- Post-test accuracy on depression etiology improved significantly from a median of 60.71% to 78.43%
- AI-based scoring demonstrated high concordance with instructor ratings (p > 0.05)
- Student agreement rates exceeded 90% across all items assessing the learning experience
Limitations
- Row count is unknown, which may limit suitability assessment
- Column-level documentation is absent; field semantics must be inferred after download
- The dataset is 10.8 KB, indicating a very limited scope of data
Provenance
- Source
- figshare
- Collection Method
- A pilot study involving a structured five-stage seminar using the World Café format, supported by AI tools.
- Freshness
- Last updated 2026-04-24 05:40:26; freshness should be verified
- Geography
- Kunming Medical University