Exploratory Assessment of GPT-4′s Effectiveness in Generating Valid Exam Items in Pharmacy Education

Objective

To evaluate the effectiveness of GPT-4 in generating valid multiple-choice exam items for assessing therapeutic knowledge in pharmacy education.

Methods

A custom GPT application was developed to create 60 case-based items from a pharmacotherapy textbook. Nine subject matter experts reviewed items for content validity, difficulty, and quality. Valid items were compiled into a 38-question exam administered to 46 fourth-year pharmacy students. Classical test theory and Rasch analysis were used to assess psychometric properties.

Results

Of 60 generated items, 38 met content validity requirements, with only 6 accepted without revisions. The exam demonstrated moderate reliability and correlated well with a prior cumulative therapeutics exam. Classical item analysis revealed that most items had acceptable point biserial correlations, though fewer than half fell within the recommended difficulty range. Rasch analysis indicated potential multidimensionality and suboptimal targeting of item difficulty to student ability.

Conclusion

GPT-4 offers a preliminary step toward generating exam content in pharmacy education but has clear limitations that require further investigation and validation. Substantial human oversight and psychometric evaluation are necessary to ensure clinical realism and appropriate difficulty. Future research with larger samples is needed to further validate the effectiveness of artificial intelligence in item generation for high-stakes assessments in pharmacy education.