GPT-4’s capabilities for formative and summative assessments in Norwegian medicine exams—an intrinsic case study in the early phase of intervention - Krumsvik, Rune Johan (Frontiers in Medicine, 2025)
The growing integration of artificial intelligence (AI) in education has paved the way for innovative assessment methods. This study explores the capabilities of GPT-4, which is a large language model (LLM), on a medicine exam and for formative and summative assessments in Norwegian educational settings. This research builds on our previous work to explore how AI, specifically GPT-4, can enhance assessment practices by evaluating its performance on a full-scale medical multiple-choice exam. Prior studies have revealed that LLM’s can have certain potential in medical education but have not specifically examined how GPT-4 can enhance formative and summative assessments in medical education. Therefore, my study contributes to filling gaps in the current knowledge by examining GPT-4’s capabilities for formative and summative assessment in medical education in Norway. For this purpose, a case study design was employed, and the primary data sources were 110 exam questions, 10 blinded exam questions, and 2 patient cases within medicine. The findings from this intrinsic case study revealed that GPT-4 performed well on the summative assessment, with a robust handling of the Norwegian medical language. Further, GPT-4 demonstrated a reliable evaluation of comprehensive student exams, such as patient cases, and, thus, aligned closely with human assessments. The findings suggest that GPT-4 can improve formative assessment by providing timely, personalized feedback to support student learning. This study highlights the importance of both an empirical and theoretical understanding of the gap between traditional assessment methods and educational practices and AI-enhanced approaches—particularly the importance of the ability of chain-of-thought prompting, how AI can scaffold tutoring, and assessment practices. However, continuous refinement and human oversight remain crucial to ensure the effective and responsible integration of LLM’s like GPT-4 into educational settings.