Using Artificial Intelligence in Test Construction: A Practical Guide

Abstract

BACKGROUND: Artificial Intelligence (AI) is increasingly used to enhance traditional assessment practices by improving efficiency, reducing costs, and enabling greater scalability. However, its use has largely been confined to large corporations, with limited uptake by researchers and practitioners. This study aims to critically review current AI-based applications in test construction and propose practical guidelines to help maximize their benefits while addressing potential risks. METHOD: A comprehensive literature review was conducted to examine recent advances in AI-based test construction, focusing on item development and calibration, with real-world examples to demonstrate practical implementation. RESULTS: Best practices for AI in test development are evolving, but responsible use requires ongoing human oversight. Effective AI-based item generation depends on quality training data, alignment with intended use, model comparison, and output validation. For calibration, essential steps include defining construct validity, applying prompt engineering, checking semantic alignment, conducting pseudo factor analysis, and evaluating model fit with exploratory methods. CONCLUSIONS: We propose a practical guide for using generative AI in test development and calibration, targeting challenges related to validity, reliability, and fairness by linking each issue to specific guidelines that promote responsible, effective implementation.

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