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Automatic- and Transformer-Based Automatic Item Generation: A Critical Review.

Markus Sommer¹, Martin Arendasy¹

¹Department of Psychology, University of Graz, Universitätsplatz 2, 8010 Graz, Austria.

Journal of Intelligence

|August 27, 2025

Summary

This summary is machine-generated.

This review examines automatic item generation (AIG) methods, including transformer-based approaches, to address challenges in modern psychometric testing. It assesses their potential for cost savings and improved test validity.

Keywords:

automatic item generation computerized adaptive testing large language models linear on-the-fly testing test construction transformer-based automatic item generation

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Area of Science:

Psychometrics
Artificial Intelligence
Educational Measurement

Background:

Modern psychometric tests face challenges due to evolving usage patterns.
Traditional item development is time-consuming and costly.
Need for efficient and valid item generation methods is increasing.

Purpose of the Study:

Critically review different automatic item generation (AIG) approaches.
Evaluate AIG methods against current psychometric testing challenges.
Identify requirements for ideal AIG systems.

Main Methods:

Review of conceptually different automatic and transformer-based AIG approaches.
Individual examination of each approach's contribution to challenges.
Focus on cost savings (construction, calibration) and test validity enhancement.

Main Results:

Analysis of how AIG methods meet challenges in item construction and calibration.
Assessment of AIG's impact on test validity.
Identification of common themes and research gaps across AIG approaches.

Conclusions:

AIG, particularly transformer-based methods, shows promise in addressing psychometric test challenges.
Further research is needed to optimize AIG for cost-efficiency and validity.
Standardized evaluation frameworks are crucial for AIG development.