March 3, 2026Open Access

Using Artificial Intelligence in Test Construction: A Practical Guide

Key Points

Validity, reliability, and fairness are critical for effective test development using artificial intelligence.
Specific guidelines link each challenge to practices that promote responsible implementation of AI in tests.
The approach emphasizes the importance of calibration to maintain quality in AI-generated tests.
Implementing these practical guidelines can improve overall test integrity and stakeholder confidence.

Abstract

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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Authors

Javier Suárez‐Álvarez

University of Massachusetts Amherst

Qiwei He

Nigel Guenole

Universidad de Londres

Journals

Psicothema

Actions

Institutions

University of Massachusetts Amherst

Georgetown University

Universidad de Londres

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

Using Artificial Intelligence in Test Construction: A Practical Guide

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

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