Integrate GAI-based teaching into medical curricula with robust curricular integration, technological support, and systematic training for educators and students¶

Strategy · All strategies
Evidence · 6 claims (5 for, 1 mixed) · 2 studies (2 quant-synthesis), q3 · 2 of 2 report an effect size · 5 claims rest on one study

Description¶

The article recommends that GAI-based teaching be adopted as a complement to, not a replacement for, traditional medical teaching, and that implementation succeed only with deliberate support structures. It states that "successful implementation requires careful consideration of several factors: robust curricular integration, adequate technological support, and systematic training for both educators and students." It further suggests pairing GAI with pedagogical approaches such as flipped classrooms, case-based learning, and gamified instruction, and establishing a practical framework for AI literacy in medical education.

Design Implications¶

Context¶

Requirements¶

• Robust curricular integration of GAI within existing educational frameworks
• Adequate technological support
• Systematic training for both students and educators

Constraints¶

• The article states GAI currently functions primarily as a complementary educational tool rather than a replacement for traditional teaching; without robust curricular integration its impact on knowledge acquisition and skill development may be constrained

Target Learners¶

• undergraduate medical students
• medical educators

Target Learning Goals¶

• practical clinical skill development
• knowledge acquisition
• AI literacy

Claims¶

• Students report significantly higher satisfaction with GAI-based teaching than with traditional teaching [+W]
• GAI-based teaching improves knowledge scores when learning duration exceeds one week [+W]
• GAI-based teaching yields higher knowledge scores in practice courses but not theory courses [+W]
• Neither geographical location nor assessment timing moderated the comparative effectiveness of GAI-based teaching on knowledge scores [+W]
• GAI-based teaching outperforms traditional teaching in practical skill development [+W]
• The evidence base for GAI-based teaching in medical education is of low quality with substantial heterogeneity and reporting gaps [~W]

Related Strategies¶

• Integrate AI-driven simulation into dental curricula as a complement to traditional training, attending to domain-specific effectiveness and the lack of long-term validated outcomes
• Integrate simulation technologies with traditional teaching through blended approaches tailored to specific clinical tasks
• Position immersive technologies and virtual patients as supplementary components within blended learning rather than stand-alone replacements for established clinical communication training
• Incorporate VR-based simulation training into spine surgery residency curricula as an adjunct to traditional methods
• Tiered ECE implementation: virtual and case-based learning first, then simulated patient interaction, then supervised real patient contact
• Use virtual simulations to enhance cognitive learning while prioritizing in-person laboratory sessions for psychomotor and technical skills
• Integrate teachable agents as a complement to, not a replacement for, the adopted classroom curriculum
• Integrate MR training into existing first-responder curricula as a phased supplement, starting with scenarios that are hard to implement in routine training
• Integrate virtual and augmented reality as supplementary tools rather than full replacements for traditional learning methods
• Integrate e-learning as a supplement to rather than replacement of traditional teaching (blended learning)

Examples¶

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Key Sources¶

• Li Juan, Yin Kaiyu, Wang Yida, Jiang Xiaoqin, Chen Dongxu. (2025). Effectiveness of generative artificial intelligence-based teaching versus traditional teaching methods in medical education: a meta-analysis of randomized controlled trials. BMC Medical Education. https://doi.org/10.1186/s12909-025-07750-2