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When Assessment Theory Meets Generative AI: Reimagining SBA Design in Medical Education.

Nora Al-Shawee¹, Gerry McElvaney², Judith Strawbridge³

¹Department of medicine, School of Medicine, Royal College of Surgeons in Ireland, IE.

Perspectives on Medical Education

|March 16, 2026

Summary

This study introduces the Co-Created SBA Design (CCSD) framework to enhance generative artificial intelligence (GenAI) use in medical education item-writing. It balances assessment quality, efficiency, and educational value using Van der Vleuten's Utility Index.

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Area of Science:

Medical Education
Artificial Intelligence in Education
Assessment Theory

Background:

Current evaluations of generative artificial intelligence (GenAI) in medical education item-writing focus narrowly on validity and reliability.
This limited scope overlooks the theoretical underpinnings of trustworthy assessment design and the complex interplay of quality, efficiency, and educational value.
Existing approaches fail to adequately address how GenAI adoption reshapes these critical assessment dimensions.

Purpose of the Study:

To present the Co-Created SBA Design (CCSD) framework, reconceptualizing assessment theory for the GenAI era.
To provide a structure for integrating GenAI into Single Best Answer (SBA) development within medical education.
To maintain equilibrium across Van der Vleuten's Utility Index dimensions while redefining collaboration in item writing.

Main Methods:

The study proposes the CCSD framework, integrating GenAI into the Single Best Answer (SBA) item-writing process.
It utilizes Van der Vleuten's Utility Index as a theoretical lens to guide the framework's development.
The framework establishes a triadic collaboration model involving educators, institutions, and GenAI.

Main Results:

The CCSD framework offers a coherent structure for GenAI integration in SBA development.
It redefines the collaborative roles of educators, institutions, and GenAI in the item-writing process.
The framework ensures a balance across validity, reliability, educational impact, acceptability, and cost-efficiency.

Conclusions:

The CCSD framework supports educational integrity and sustainable innovation in medical education assessment.
It addresses the complexity of GenAI adoption by balancing quality, efficiency, and educational value.
This triadic model ensures each contributor plays a distinct role in sustaining high-quality assessment design.