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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Use of Large Language Models for Rapid Quantitative Feedback in Case-Based Learning: A Pilot Study.

Carolyn Qian1, Christina Gao2, Sang-O Park1

  • 1Harvard Medical School, Harvard University, Boston, MA 02138 USA.

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Large language models (LLMs) can score medical student case interactions effectively. GPT-4o showed high correlation with expert evaluation, with calibration improving accuracy.

Keywords:
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Area of Science:

  • Medical education technology
  • Artificial intelligence in healthcare

Background:

  • Interactive case-based learning is valuable for medical students.
  • Automated scoring of student interactions can enhance feedback.
  • Large language models (LLMs) offer potential for educational assessment.

Purpose of the Study:

  • To evaluate the efficacy of GPT-4o in scoring medical student interactions with virtual cases.
  • To assess the correlation between LLM-generated scores and expert evaluations.
  • To determine if calibration can improve LLM scoring accuracy.

Main Methods:

  • Medical students interacted with virtual cases presented by an LLM.
  • GPT-4o scored student interactions.
  • LLM scores were compared against expert human scorers.
  • Calibration methods were applied to address discrepancies.

Main Results:

  • GPT-4o demonstrated a high correlation with expert scorer evaluations.
  • A notable agreement was observed between LLM and expert scoring.
  • Calibration effectively reduced the difference between LLM and expert scores.

Conclusions:

  • LLMs, specifically GPT-4o, show significant promise for evaluating medical student performance in case-based scenarios.
  • Automated scoring by LLMs can be a reliable adjunct to traditional expert assessment.
  • Calibration is a viable strategy to enhance the accuracy of LLM-based educational scoring.