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ORIGINAL RESEARCH article

Front. Pharmacol.
Sec. Drugs Outcomes Research and Policies
Volume 16 - 2025 | doi: 10.3389/fphar.2025.1516381
This article is part of the Research Topic Digital Health Innovations in Africa: Harnessing AI, Telemedicine, and Personalized Medicine for Improved Healthcare View all articles

Challenging the Curve: Can ChatGPT-Generated MCQs Reduce Grade Inflation in Pharmacy Education

Provisionally accepted
Dalia Almaghaslah Dalia Almaghaslah *
  • King Khalid University, Abha, Saudi Arabia

The final, formatted version of the article will be published soon.

    This quasi-experimental study explores the impact of AI-generated multiple-choice questions (MCQs) on reducing grade inflation in a pharmacy management course at a Saudi university. The 2024 midterm exam, featuring ChatGPT-generated MCQs, was compared with the 2023 exam, which used human-generated questions. The study aimed to assess whether AI-generated questions could enhance exam difficulty and improve the reliability of student evaluations. Key findings indicate that the 2024 exam exhibited improved reliability (KR-20 = 0.83 vs. 0.78) and a more balanced distribution of question difficulty. While the 2023 exam had mostly easy questions (93.3%), the 2024 exam included more moderate questions (30%) and one difficult question (3.3%). This shift led to lower mean scores (17.75 vs. 21.53, p < 0.001) and a higher discrimination index (0.35 vs. 0.25, p = 0.007), suggesting a reduction in grade inflation. Despite the timesaving advantages of AI-generated questions, the study emphasizes the importance of carefully reviewing these questions to ensure accuracy and suitability. These findings highlight the potential of AI tools such as ChatGPT to enhance fairness in assessments by generating more rigorous exams that accurately evaluate student performance.

    Keywords: AI, ChatGPT4, MCQ, Pharmacy course, grade inflation, AI-generated MCQs

    Received: 24 Oct 2024; Accepted: 09 Jan 2025.

    Copyright: © 2025 Almaghaslah. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Dalia Almaghaslah, King Khalid University, Abha, Saudi Arabia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.