ORIGINAL RESEARCH article
Front. Behav. Neurosci.
Sec. Learning and Memory
This article is part of the Research TopicArtificial Intelligence for Behavioral Neuroscience: Unlocking mechanisms, modeling behavior, and advancing predictionView all articles
AI-Enhanced Adaptive Testing with Cognitive Diagnostic Feedback and its Association with Performance in Undergraduate Surgical Education: A Pilot Study
Provisionally accepted
- 1University of Minho, Braga, Portugal
- 2Universidade do Minho Instituto de Investigacao em Ciencias da Vida e Saude, Braga, Portugal
- 3Laboratorio Associado ICVS 3B's, Guimaraes, Portugal
- 4European Board of Medical Assessors, Cardiff, United Kingdom
- 5Inspirali Educação SA, Sao Paulo, Brazil
- 6Faculdades Pequeno Principe, Curitiba, Brazil
- 7Universidade do Algarve Faculdade de Medicina e Ciencias Biomedicas, Faro, Portugal
- 8iCognitus4ALL – IT Solutions, Porto, Portugal
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Background: Effective feedback in the cognitive domain is essential for surgical education but often limited by resource constraints and traditional assessment formats. Artificial Intelligence (AI) has emerged as a catalyst for innovation, enabling automated feedback, real-time cognitive diagnostics, and scalable item generation, thereby transforming how future surgeons learn and are assessed. Methods: An item bank of 150 multiple-choice questions was developed using AI-assisted item generation and difficulty estimation. A formative Computerized Adaptive Testing (CAT), balanced across three cognitive domains (memory, analysis, and decision) and surgical topics, was delivered via QuizOne® three to five days before the summative Progress Test. A total of 147 students participated, of whom 116 completed the formative CAT. Performance correlations, group comparisons, analysis of covariance (ANCOVA), and regression analyses were conducted. Results: Students who voluntarily completed CAT showed higher Progress Test scores, though causality cannot be established due to self-selection bias (p = .021), with the effect persisting after adjusting for prior academic performance (ANCOVA p = .041). Memory skills were the strongest predictors of summative outcomes (R² = 0.180, β = 0.425), followed by analysis (R² = 0.080, β = 0.283); decision was not significant (R² = 0.029, β = 0.170). Conclusions: AI-enhanced CAT–Cognitive Diagnostic Modeling (CDM) represents a promising formative approach in undergraduate surgical education, being associated with higher summative performance and providing individualized diagnostic feedback. Refining feedback presentation and enhancing decision-making assessment could further optimize its educational impact.
Keywords: artificial intelligence, computerized adaptive testing, Cognitive diagnostic modeling, surgical education, Feedback, cognitive skills, Assessment innovation, Educational Technology
Received: 29 Oct 2025; Accepted: 26 Nov 2025.
Copyright: © 2025 Gonçalves, Collares and Pêgo. 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: Nuno Gabriel Silva Gonçalves
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