Evidence map›Paper›PMID 42564084›Full record

ArticleFrontiers in psychology2026

Understanding university students' AI ethical decision-making in academic contexts: a SOR - social cognitive perspective.

Hao Deng, Minli Yang, Liling Huang, Zhixin Yang, Nan Jiang

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. 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

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
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.

Hao DengFaculty of Education, Guangxi Normal University, Guilin, Guangxi, China.
Minli YangFine Arts College, Guangxi Arts University, Nanning, Guangxi, China.
Liling HuangSchool of Management, Guangxi Minzu University, Nanning, Guangxi, China.
Zhixin YangSchool of Management, Guangxi Minzu University, Nanning, Guangxi, China.
Nan JiangSchool of Management, Guangxi Minzu University, Nanning, Guangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The integration of generative artificial intelligence into higher education has reshaped students' academic practices and blurred normative boundaries around academic integrity. While prior research has focused mainly on attitudes and usage intentions, limited attention has been paid to students' AI ethical decision-making in academic scenarios and its psychological mechanisms. This study examined the associations among AI ethics guidance, academic ethics course experience, moral cognition, cognitive complexity, and AI ethical decision-making. It tested whether moral cognition mediated the relationships between normative inputs and AI ethical decision-making and whether cognitive complexity strengthened these relationships. The study integrated the Stimulus-Organism-Response framework with Social Cognitive Theory and combined net-effect, configurational, and importance-performance analyses. Methods: Survey data were collected from 1,106 undergraduates at Chinese universities. Partial least squares structural equation modeling was used to test direct, mediating, and moderating relationships, complemented by fuzzy-set qualitative comparative analysis to identify configurational pathways to high AI ethical decision-making. Importance-performance matrix analysis identified intervention priorities. Results: AI ethics guidance ( Discussion: The findings advance academic integrity research by shifting attention from attitudes to scenario-based decision quality and clarifying the internalization mechanism through moral cognition. Practically, they highlight the complementary roles of technological prompts and academic ethics course experience in fostering students' AI ethical decision-making.

Indexed as

academic integrityAI ethical decision-makingcognitive complexitymoral cognitionPLS-SEM

Identifiers

PMID42564084
PMCPMC13442383

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