Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Evaluating the Accuracy and Educational Potential of Generative AI Models in Pharmacy Education: A Comparative Analysis of ChatGPT and Gemini Across Bloom’s Taxonomy
0
Zitationen
3
Autoren
2025
Jahr
Abstract
This study evaluated the accuracy and educational potential of three generative AI models, ChatGPT 3.5, ChatGPT 4o, and Gemini 2.5, by addressing pharmacy-related content across three key areas: biostatistics, pharmaceutical calculations, and therapeutics. A total of 120 exam-style questions, categorized by Bloom's Taxonomy levels (Remember, Understand, Apply, and Analyze), were administered to each model. Overall, the AI models achieved a combined accuracy rate of 77.5%, with ChatGPT 4o consistently outperforming ChatGPT 3.5 and Gemini 2.5. The highest accuracy was observed in therapeutics (83.3%), followed by biostatistics (81.7%) and calculations (67.5%). Performance was strongest at lower Bloom levels, reflecting proficiency in recall and conceptual understanding, but declined at higher levels requiring analytical reasoning. These findings suggest that generative AI tools can serve as effective supplementary aids for pharmacy education, particularly for conceptual learning and review. However, their limitations in quantitative and higher-order reasoning highlight the need for guided use and faculty oversight. Future research should expand to additional subject areas and assess longitudinal learning outcomes to better understand AI's role in improving critical thinking and professional competence among pharmacy students.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.292 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.143 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.539 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.452 Zit.