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Medical students could not distinguish between artificial intelligence and human-created multiple-choice questions. They opposed using AI-generated questions in assessments without expert review due to concerns about accuracy and relevance.

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Assessment Methods

Background:

  • The integration of Generative Artificial Intelligence (GenAI) into medical education presents novel opportunities and challenges.
  • Medical students' perceptions are crucial for the effective and ethical implementation of new assessment technologies.

Purpose of the Study:

  • To investigate medical students' ability to differentiate between GenAI-generated and clinician-generated multiple-choice questions (MCQs).
  • To explore student perspectives on the use of GenAI-generated MCQs in formal assessments.

Main Methods:

  • A survey was administered to 86 MBBS students.
  • Participants were asked to differentiate between MCQs generated by ChatGPT and those created by clinical educators.
  • Student perceptions regarding the use of GenAI in assessments were collected.

Main Results:

  • Students' performance in distinguishing between ChatGPT-set and clinician-set questions was not statistically significant, performing at chance levels (p = 0.46).
  • A strong opposition to the use of unvetted GenAI-generated questions was observed among participants.
  • Key concerns cited by students included accuracy, relevance, and educational value of AI-generated content.

Conclusions:

  • Medical students are unable to reliably differentiate between GenAI-generated and human-expert-generated assessment items.
  • The adoption of GenAI in medical assessments necessitates careful consideration of student feedback and the educational validity of the tools.
  • Expert vetting and a focus on educational value are paramount for the responsible integration of GenAI in student evaluations.