Survey of Computerized Adaptive Testing: A Machine Learning Perspective
Abstract
Computerized Adaptive Testing (CAT) offers an efficient and personalized method for assessing examinee proficiency by dynamically adjusting test questions based on individual performance. Compared to traditional, non-personalized testing methods, CAT requires fewer questions and provides more accurate assessments. As a result, CAT has been widely adopted across various fields, including education, healthcare, sports, sociology, and the evaluation of AI models. While traditional methods rely on psychometrics and statistics, the increasing complexity of large-scale testing has spurred the integration of machine learning techniques. This paper aims to provide a machine learning-focused survey on CAT, presenting a fresh perspective on this adaptive testing paradigm. We delve into measurement models, question selection algorithm, bank construction, and test control within CAT, exploring how machine learning can optimize these components. Through an analysis of current methods, strengths, limitations, and challenges, we strive to develop robust, fair, and efficient CAT systems. By bridging psychometric-driven CAT research with machine learning, this survey advocates for a more inclusive and interdisciplinary approach to the future of adaptive testing.
Cite
@article{arxiv.2404.00712,
title = {Survey of Computerized Adaptive Testing: A Machine Learning Perspective},
author = {Yan Zhuang and Qi Liu and Haoyang Bi and Zhenya Huang and Weizhe Huang and Jiatong Li and Junhao Yu and Zirui Liu and Zirui Hu and Yuting Hong and Zachary A. Pardos and Haiping Ma and Mengxiao Zhu and Shijin Wang and Enhong Chen},
journal= {arXiv preprint arXiv:2404.00712},
year = {2026}
}
Comments
accepted by IEEE TPAMI 2026