Evidence map›Paper›PMID 40863199›Full record

ReviewJournal of Intelligence2025

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

Markus Sommer, Martin Arendasy

Abstract readReview
In one paragraph

Review in Journal of Intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Markus SommerDepartment of Psychology, University of Graz, Universitätsplatz 2, 8010 Graz, Austria.
Martin ArendasyDepartment of Psychology, University of Graz, Universitätsplatz 2, 8010 Graz, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article provides a critical review of conceptually different approaches to automatic and transformer-based automatic item generation. Based on a discussion of the current challenges that have arisen due to changes in the use of psychometric tests in recent decades, we outline the requirements that these approaches should ideally fulfill. Subsequently, each approach is examined individually to determine the extent to which it can contribute to meeting the challenges. In doing so, we will focus on the cost savings during the actual item construction phase, the extent to which they may contribute to enhancing test validity, and potential cost savings in the item calibration phase due to either a reduction in the sample size required for item calibration or a reduction in the item loss due to insufficient psychometric characteristics. In addition, the article also aims to outline common recurring themes across these conceptually different approaches and outline areas within each approach that warrant further scientific research.

Indexed as

automatic item generationcomputerized adaptive testinglarge language modelslinear on-the-fly testingtest constructiontransformer-based automatic item generation

Identifiers

PMID40863199
PMCPMC12387605

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.