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Home Research Research Library Blueprinting the Future: Automatic Item Categorisation using Hierarchical Zero-Shot and Few-Shot Classifiers Blueprinting the Future: Automatic Item Categorisation using Hierarchical Zero-Shot and Few-Shot Classifiers 2025 Author(s) Wang, Ting, Stelter, Keith L, O’Neill, Thomas R, Hendrix, Nathaniel, Bazemore, Andrew W, and Newton, Warren P Topic(s) Family Medicine Certification Keyword(s) Psychometrics Volume Journal of Applied Testing Technology Source Journal of Applied Testing Technology Precise item categorisation is essential in aligning exam questions with content domains outlined in assessment blueprints. Traditional methods, such as manual classification or supervised machine learning, are often time-consuming, errorprone, or limited by the need for large training datasets. This study presents a novel approach using zero-shot and few-shot Generative Pretrained Transformer (GPT) models for hierarchical item categorisation. By leveraging human-readable language descriptions within a structured Python dictionary, the model navigates complex blueprint hierarchies without requiring extensive training data. An initial simulation with synthetic items demonstrated the method’s effectiveness, achieving an average F1 score of 92.91%. The approach was then applied to 200 real exam items from the 2022 In-Training Examination (ITE) by the American Board of Family Medicine (ABFM), reclassifying them according to a newly developed blueprint in just 15 minutes—a process that would typically take several days of expert review. This technique offers rapid, consistent, and scalable item categorisation, minimises human bias, and allows for iterative refinement through simple adjustments to category definitions, enhancing both efficiency and sustainability in assessment design. ABFM Research Read all 2014 Family Physicians’ Quality Interventions and Performance Improvement Through the ABFM Diabetes Performance in Practice Module Go to Family Physicians’ Quality Interventions and Performance Improvement Through the ABFM Diabetes Performance in Practice Module 2020 Characteristics of Family Medicine Residency Graduates, 1994-2017: An Update Go to Characteristics of Family Medicine Residency Graduates, 1994-2017: An Update 1989 Classifying the content of board certification examinations Go to Classifying the content of board certification examinations 2017 Improving Performance Improvement Go to Improving Performance Improvement
Author(s) Wang, Ting, Stelter, Keith L, O’Neill, Thomas R, Hendrix, Nathaniel, Bazemore, Andrew W, and Newton, Warren P Topic(s) Family Medicine Certification Keyword(s) Psychometrics Volume Journal of Applied Testing Technology Source Journal of Applied Testing Technology
ABFM Research Read all 2014 Family Physicians’ Quality Interventions and Performance Improvement Through the ABFM Diabetes Performance in Practice Module Go to Family Physicians’ Quality Interventions and Performance Improvement Through the ABFM Diabetes Performance in Practice Module 2020 Characteristics of Family Medicine Residency Graduates, 1994-2017: An Update Go to Characteristics of Family Medicine Residency Graduates, 1994-2017: An Update 1989 Classifying the content of board certification examinations Go to Classifying the content of board certification examinations 2017 Improving Performance Improvement Go to Improving Performance Improvement
2014 Family Physicians’ Quality Interventions and Performance Improvement Through the ABFM Diabetes Performance in Practice Module Go to Family Physicians’ Quality Interventions and Performance Improvement Through the ABFM Diabetes Performance in Practice Module
2020 Characteristics of Family Medicine Residency Graduates, 1994-2017: An Update Go to Characteristics of Family Medicine Residency Graduates, 1994-2017: An Update
1989 Classifying the content of board certification examinations Go to Classifying the content of board certification examinations