Evidence map›Paper›PMID 41062632›Full record

ArticleScientific reports2025

GPT-4 shows comparable performance to human examiners in ranking open-text answers.

Abdullah Al Zubaer, Michael Granitzer, Stephan Geschwind, Johann Graf Lambsdorff, Deborah Voss

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Abdullah Al ZubaerDepartment of Computer Science and Mathematics, University of Passau, Passau, Germany. abdullahal.zubaer@uni-passau.de.
Michael GranitzerDepartment of Computer Science and Mathematics, University of Passau, Passau, Germany.
Stephan GeschwindDepartment of Business Administration, Economics and Business Information Systems, University of Passau, Passau, Germany.
Johann Graf LambsdorffDepartment of Business Administration, Economics and Business Information Systems, University of Passau, Passau, Germany.
Deborah VossDepartment of Business Administration, Economics and Business Information Systems, University of Passau, Passau, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Can GPT-4 replace human examiners? To address this question we explore the performance of GPT-4 as an examiner of answers to open-text questions. We formulate questions and sample solutions in the field of macroeconomics and collect answers from cohorts of undergraduate students. We then conduct a fair competition between GPT-4 and human experts, employing their expertise to assess the quality of the answers. We observe that the substitution of GPT-4 for a human examiner does not decrease inter-rater reliability on tasks that rank the quality of answers. We run checks on potential biases (whether GPT-4 prefers AI-generated or lengthy answers). We find no consistent evidence of such biases. Our findings are robust to tilting the competition to one side's advantage, by using inferior or advanced prompting strategies. Our results are more attenuated on tasks where GPT-4 assigns points to student answers. Here, GPT-4 shows a bias towards longer answers. Overall, our study cautiously supports the utilization of GPT-4 as an assistant for automated grading systems, particularly those where answers are ranked according to their quality.

Indexed as

Educational MeasurementHumansReproducibility of ResultsAutomated grading systemsExamination tasksGPT-4Inter-rater reliabilityOpen-text answers

Identifiers

PMID41062632
PMCPMC12508078

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.