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Artificial intelligence (AI) in medical education is under-explored for learner assessment. AI usage positively correlates with favorable attitudes toward AI in assessing medical students, regardless of institutional factors.

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Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Healthcare
  • Competency-Based Assessment

Background:

  • Current AI integration in medical education primarily targets clinical applications.
  • Limited research exists on AI's role in assessing medical learners.
  • This study investigates AI usage and perceptions among family medicine clerkship directors.

Purpose of the Study:

  • To explore the current use of artificial intelligence (AI) by family medicine clerkship directors.
  • To assess director perspectives on AI's potential for competency-based medical education assessment.
  • To identify factors influencing the adoption of AI in medical learner evaluation.

Main Methods:

  • Survey data collected from 173 family medicine clerkship directors in 2024.
  • A 52.6% response rate was achieved.
  • Multivariable linear regression analyzed the association between AI usage and favorability towards AI in student assessment.

Main Results:

  • Physician directors leading mandatory clerkships participated.
  • Female and underrepresented minority directors reported less AI use.
  • AI usage showed a significant positive association with favorable attitudes towards AI in learner assessment (P<.001).
  • Organizational support and policies did not significantly influence favorability.

Conclusions:

  • AI exposure is linked to more favorable views on AI for learner assessment.
  • Organizational factors did not significantly impact these attitudes.
  • Further research with larger, longitudinal studies is needed to understand AI adoption dynamics in medical education.