Evidence map›Paper›PMID 41715251›Full record

ArticleBMC psychology2026

A configurational exploration of how personality traits influence GAI academic misconduct behaviors using fuzzy-set qualitative comparative analysis.

Haiying Liang, Michael J Reiss

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Article in BMC psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Haiying LiangSchool of Foreign Languages, Peking University, Beijing, China.ORCID http://orcid.org/0000-0002-8601-3763
Michael J ReissInstitute of Education, University College London, London, United Kingdom. m.reiss@ucl.ac.uk.ORCID http://orcid.org/0000-0003-1207-4229

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe rapid adoption of Generative Artificial Intelligence (GAI) in higher education has introduced new ethical challenges, particularly concerning students' academic misconduct. While prior research has linked personality traits to unethical behavior, little is known about how different combinations of personality traits shape students' misuse of GAI.

methodsThis study integrates the HEXACO model and the Dark Triad framework to examine the configurational effects of personality on GAI-related academic misconduct. A total of 864 university students completed questionnaires. Using fuzzy-set Qualitative Comparative Analysis, we identified multiple configurations leading to both high and low levels of GAI misconduct.

resultsNo single trait is sufficient to explain GAI-related academic misconduct. Rather, high misconduct consistently emerged from configurations characterized by low Honesty-Humility and Conscientiousness combined with high Machiavellianism or Psychopathy. In contrast, low misconduct was associated with configurations combining high Honesty-Humility, Conscientiousness, and Agreeableness with low levels of Dark Triad traits.

conclusionsThis study demonstrates that personality traits interact synergistically rather than independently to shape individuals' ethical or unethical engagement with AI technologies. Moral restraint is sustained by both the presence of virtues and the absence of exploitative tendencies. The findings support the idea that moral integrity and self-regulation constitute foundational safeguards against unethical use of technology. These findings align with self-regulatory theories of academic dishonesty, reinforcing that individuals high in honesty and conscientiousness are less likely to rationalize or justify academic misconduct even when new technological affordances make it easier. The study therefore advances theoretical understanding by integrating personality frameworks within a configurational paradigm and offers practical insights for developing personality-informed ethics education.

Indexed as

Generative Artificial IntelligencePersonalityStudentsAcademiaAdultFemaleFuzzy LogicHumansMaleMoralsUniversitiesYoung AdultAcademic integrityAcademic misconductDark triadFsQCAGenerative artificial intelligenceHEXACO modelPersonality traits

Identifiers

PMID41715251
PMCPMC13020306

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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.