Meta-Analysis of Generative AI-Assisted Teaching Methods in Medical Education

78 studies involving 7,566 medical students were analyzed in this systematic review and meta-analysis. The research, authored by Xingming Ma and published on figshare in April 2026, evaluated the effectiveness of generative artificial intelligence (GAI)-assisted teaching methods on educational outcomes. The pooled results revealed significant improvements in academic performance, student satisfaction, self-efficacy, and various skills compared to traditional teaching.

Use Cases

• Evaluate the impact of AI-assisted teaching on academic performance based on knowledge and practical scores
• Assess improvements in student satisfaction and self-efficacy based on reported standardized mean differences
• Analyze enhancements in clinical thinking and problem-solving skills based on meta-analysis results

Strengths

• 78 studies were included, providing a substantial evidence base
• The analysis covers 7,566 medical students (3,635 in GAI-assisted groups and 3,931 in control groups)
• The study followed PRISMA guidelines and was registered in PROSPERO (CRD420251173150)
• Multiple outcome measures were analyzed, including knowledge, practical scores, satisfaction, and several skills

Limitations

• The dataset is a single PDF document (883.7 KB), not a structured data table; column-level documentation is absent
• Row count is unknown, which may limit suitability assessment for direct data analysis
• The data is a review of existing literature; the underlying primary data from the 78 studies is not directly accessible

Provenance

Source

figshare

Collection Method

Systematic review and meta-analysis conducted following PRISMA guidelines.

Freshness

Last updated 2026-04-22 05:46:36