AI RESEARCH
CodeGENCAT: Generative Computerized Adaptive Testing for Open-ended Coding Problems
arXiv CS.CL
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ArXi:2602.20020v2 Announce Type: replace Existing Computerized Adaptive Testing (CAT) frameworks typically select questions based on the predicted likelihood that the student will answer correctly. This design ignores information contained in students' open-ended responses, especially in domains such as programming education, where code structures and bugs contain rich information on student knowledge. In this work, we propose \textbf{Code} \textbf{GEN}erative \textbf{CAT} (\textbf{CodeGENCAT}), a generative CAT framework that selects questions using predicted student code responses.