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Chatbot Underperformance in Biology and Image-Based Questions in Medical Education.

Joyce Santana Rizzi1, Lorraine Silva Requena1, Angelica Maria Bicudo2

  • 1Laboratory of Muscle Biology, Department of Structural and Functional Biology, Institute of Bioscience of Botucatu, Sao Paulo State University (UNESP), Botucatu, Brazil.

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|December 8, 2025
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Summary

AI chatbots show promise in medical education but struggle with image-based and aggression-related biology questions. Their accuracy varies, highlighting the need for further development in these areas.

Keywords:
Undergraduate medical educationartificial intelligencebiological science disciplineslarge language modelprogress test

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Biology

Background:

  • AI chatbots exhibit variable performance in medical education assessments.
  • Their effectiveness in biology, especially with image-based and discipline-specific questions, is largely unexamined.

Purpose of the Study:

  • To evaluate the accuracy and reliability of AI chatbots in answering biological questions from a medical assessment.
  • To identify performance patterns related to question type and content.

Main Methods:

  • An observational cross-sectional study involving 180 biological questions from the Progress Test.
  • Categorization of questions by morphology, function, and aggression, with assessment across multiple chatbot attempts.
  • Application of logistic regression and hierarchical clustering to analyze performance patterns.

Main Results:

  • Chatbots achieved high accuracy (85-91.7%) for morphology and function questions.
  • Accuracy significantly decreased for aggression-related and image-based biological questions.
  • Image-based questions reduced correct answer odds by up to 17.6% (ChatGPT-4), and chatbot response agreement was weak.

Conclusions:

  • AI chatbots have potential in medical education but demonstrate limitations with visual and aggression-related biological content.
  • Further development is needed to improve chatbot reliability for complex biological assessments.
  • Chatbot performance varies significantly based on question type and visual elements.