Prioritize prospective, in-curriculum evaluations of AI governance interventions over additional isolated benchmark studies¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends reallocating scholarly attention from capability benchmarking to testing governance interventions in real curricula. It names "honor-code adaptations, AI-disclosure norms, faculty training programs" as examples that should be "tested in real curricula rather than proposed in commentary." Future research should test disclosure policies, faculty-development programs, learner-use standards, assessment safeguards, equity-oriented frameworks, and human-oversight mechanisms across diverse medical-education environments.

Design Implications¶

Context¶

Requirements¶

• Interventions must be evaluated prospectively in authentic educational settings rather than proposed in commentary.

Constraints¶

• Governance models developed at well-resourced centers may not generalize to institutions with fewer technological, financial, and faculty-development resources.

Target Learners¶

• Medical students
• Residents and trainees in health professions education

Target Learning Goals¶

• Responsible and ethical use of AI in learning and assessment
• Preservation of independent clinical reasoning

Related Strategies¶

• Extend educational LLM benchmarking toward multilingual contexts, real classroom interaction data, and targeted training for identified capability gaps
• Research agenda for generative AI in teaching and learning: identify needed skills, resources and capabilities
• Test whether student learning concepts and findings hold in online environments, and relate higher education curricula to secondary curricula

Examples¶

-

Key Sources¶

• Sriram A, Nichols E, Ganti L, Gue S. (2026). Ethical and responsible use of artificial intelligence in medical education. International Journal of Emergency Medicine. https://doi.org/10.1186/s12245-026-01389-6