mlsanityData to 7 Oct 2026

Survey of Computerized Adaptive Testing: A Machine Learning Perspective

arXiv 2404.00712 ↗First posted 31 Mar 2024cs.LG cs.AI cs.CYcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 42 of its 217 references that are in the map. First posted on arXiv 31 Mar 2024.

Builds on

42 of 217 references.

References · 42

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 3.

6 papers.

  1. 2025Adaptive Elicitation of Latent Information Using Natural Languagecited by 7
  2. 2025Adaptive political surveys and GPT-4: Tackling the cold start problem with simulated user interactionscited by 3
  3. 2026Rich Insights from Cheap Signals: Efficient Evaluations via Tensor Factorizationcited by 1
  4. 2024Prompt Transfer for Dual-Aspect Cross Domain Cognitive Diagnosiscited by 0
  5. 2026Trustworthy Intelligent Education: A Systematic Perspective on Progress, Challenges, and Future Directionscited by 0
  6. 2026Leveraging Computerized Adaptive Testing for Cost-effective Evaluation of Large Language Models in Medical Benchmarkingcited by 0