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Computerized Adaptive Testing System of Functional Assessment of Stroke

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Published on: January 7, 2019

Survey of Computerized Adaptive Testing: a Machine Learning Perspective.

Yan Zhuang, Qi Liu, Haoyang Bi

IEEE Transactions on Pattern Analysis and Machine Intelligence

|March 10, 2026

Summary

This summary is machine-generated.

Computerized Adaptive Testing (CAT) enhances assessment accuracy and efficiency by personalizing questions. This survey explores machine learning integration to optimize CAT systems for robust, fair, and efficient testing.

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Area of Science:

Psychometrics
Artificial Intelligence
Educational Technology

Background:

Computerized Adaptive Testing (CAT) provides personalized assessments, outperforming traditional methods in efficiency and accuracy.
CAT is widely used across education, healthcare, sports, sociology, and AI model evaluation.
Increasingly complex large-scale testing necessitates integrating machine learning (ML) with psychometric approaches.

Purpose of the Study:

To present a machine learning-focused survey of Computerized Adaptive Testing (CAT).
To offer a novel perspective on adaptive testing by highlighting ML's role.
To explore ML's optimization potential in CAT components like measurement models, question selection, bank construction, and test control.

Main Methods:

Literature review and analysis of existing CAT methods.
Focus on machine learning techniques applied to adaptive testing components.
Examination of strengths, limitations, and challenges in current CAT systems.

Main Results:

Machine learning offers significant potential to optimize various aspects of CAT.
Integration of ML can lead to more robust, fair, and efficient adaptive testing systems.
Current research highlights the benefits of combining psychometric principles with ML.

Conclusions:

A machine learning-driven approach can significantly advance CAT.
Interdisciplinary collaboration between psychometrics and ML is crucial for future CAT development.
This survey advocates for a more inclusive approach to adaptive testing research.