  Computerized Adaptive Testing (CAT) provides an efficient and tailored method
for assessing the proficiency of examinees, by dynamically adjusting test
questions based on their performance. Widely adopted across diverse fields like
education, healthcare, sports, and sociology, CAT has revolutionized testing
practices. 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 method. By examining the test question selection algorithm at the heart
of CAT's adaptivity, we shed light on its functionality. Furthermore, we delve
into cognitive diagnosis models, question 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.
