ArticlePerspectives on medical education2026
When Assessment Theory Meets Generative AI: Reimagining SBA Design in Medical Education.
Article in Perspectives on medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
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Abstract
Current evaluations of generative artificial intelligence (GenAI) in item-writing within medical education often concentrate on isolated dimensions such as validity or reliability, overlooking the broader theoretical foundations that underpin a trustworthy assessment design. This narrow emphasis risks oversimplifying GenAI's role and obscuring how its adoption reshapes the relationship between quality, efficiency, and educational value. To address this complexity, this paper presents the Co-Created SBA Design (CCSD) framework, which reconceptualises assessment theory for the GenAI era through the lens of Van der Vleuten's Utility Index. The framework offers a coherent structure for integrating GenAI into Single Best Answer development, maintaining equilibrium across the Utility Index dimensions while redefining collaboration among educators, higher education institutions, and GenAI, a technological partner that enriches the item-writing process. Within this triadic model, each contributor plays a distinct yet complementary role in sustaining assessment quality. Collectively, their interaction ensures that validity, reliability, educational impact, acceptability, and cost-efficiency remain balanced, supporting both educational integrity and sustainable innovation in medical education.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.