Rethinking the Integration of AI in Learning and Assessment in Health Professions Education.

Area of Science:

• Medical Education
• Health Professions Education (HPE)
• Artificial Intelligence (AI) in Education

Background:

• Current approaches to AI in HPE are reactive and enforcement-driven, fostering suspicion and potentially undermining learning.
• Existing assessment models are ill-equipped for the AI era, risking fairness and student trust.
• Unreliable AI detection technologies exacerbate challenges in academic integrity.

Purpose of the Study:

• To advocate for a paradigm shift in how health professions education engages with artificial intelligence.
• To propose a move from a punitive, detection-focused stance to a proactive, educationally sound strategy.
• To emphasize the need for assessment redesign and enhanced AI literacy.

Main Methods:

• This perspective paper analyzes the current landscape of AI in health professions education.
• It reviews the limitations of traditional assessment models in the context of AI.
• It proposes strategies for fostering AI literacy and redesigning educational approaches.

Main Results:

• A reactive, enforcement-driven approach to AI in HPE is detrimental to meaningful learning and trust.
• Current assessment methods are inadequate and require redesign to focus on higher-order skills and authentic competencies.
• Developing robust AI literacy for students and faculty is essential for responsible AI integration.

Conclusions:

• Health professions education must transition from policing AI to empowering students and faculty.
• Assessment redesign prioritizing real-time competence and authentic learning is imperative.
• Embracing AI with a clear educational purpose will ensure credible assessments and relevant learning in the future clinical environment.