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Evaluating NAPLEX Preparation Exam Questions Generated by a Large Language Model.

Christopher Edwards1, Brian Erstad1, Bernadette Cornelison1

  • 1University of Arizona R. Ken Coit College of Pharmacy, Tucson, AZ, USA.

American Journal of Pharmaceutical Education
|April 6, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) can generate high-quality North American Pharmacist Licensure Examination (NAPLEX) practice questions. Over 90% of ChatGPT-4.0 generated questions were usable, demonstrating LLM potential for pharmacy education.

Keywords:
Artificial intelligenceLarge language modelsNAPLEXPharmacyTest development

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Area of Science:

  • Pharmacy Education
  • Artificial Intelligence in Education

Background:

  • The North American Pharmacist Licensure Examination (NAPLEX) is crucial for pharmacy licensure.
  • Developing high-quality practice questions is essential for student preparation.
  • Large language models (LLMs) offer potential for automated content generation.

Purpose of the Study:

  • To evaluate the quality of NAPLEX preparation questions generated by a large language model (LLM).

Main Methods:

  • ChatGPT-4.0 was prompted to create 200 NAPLEX practice questions based on the 2025 NAPLEX Content Outline.
  • Three pharmacy faculty members independently assessed question correctness, usability, and other quality attributes.
  • Descriptive statistics and Gwet's AC1 statistic were used for analysis.

Main Results:

  • 181-191 (91-96%) of generated questions were correct.
  • 150-164 (75-82%) were usable as written, and 188-190 (94-95%) were usable with minor modifications.
  • High inter-rater reliability (Gwet's AC1 = 0.9291) was observed for usability ratings.

Conclusions:

  • ChatGPT-4.0 generated NAPLEX questions that were largely correct and usable.
  • LLMs show promise for creating practice questions for pharmacy students.
  • Faculty oversight is recommended for optimal question development and validation.