Survey of Computerized Adaptive Testing: A Machine Learning Perspective

TL;DR

This survey reviews how machine learning techniques are transforming Computerized Adaptive Testing by enhancing measurement models, question selection, and test control for more efficient and personalized assessments across various fields.

Contribution

It provides a comprehensive overview of machine learning applications in CAT, highlighting new methods, challenges, and future directions for integrating AI with psychometric testing.

Findings

Machine learning improves question selection algorithms.

Integration of ML enhances test fairness and robustness.

Current methods face challenges in scalability and interpretability.

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.