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.

翻译：计算机化自适应测试（CAT）通过根据个体表现动态调整测试题目，提供了一种高效且个性化的受试者能力评估方法。相较于传统的非个性化测试方法，CAT 所需题目更少，并能提供更准确的评估。因此，CAT 已被广泛应用于教育、医疗保健、体育、社会学以及人工智能模型评估等多个领域。传统方法依赖于心理测量学和统计学，而大规模测试日益增长的复杂性推动了机器学习技术的融合。本文旨在提供一份聚焦于机器学习的 CAT 综述，为这一自适应测试范式提供一个全新的视角。我们深入探讨了 CAT 中的测量模型、题目选择算法、题库构建和测试控制，探索机器学习如何优化这些组成部分。通过对现有方法、优势、局限性和挑战的分析，我们致力于开发鲁棒、公平且高效的 CAT 系统。通过连接心理测量学驱动的 CAT 研究与机器学习，本综述倡导一种更具包容性和跨学科性的自适应测试未来发展路径。