Evidence map›Paper›PMID 42352694›Full record

ArticleBehavioral sciences (Basel, Switzerland)2026

How Generative AI Use Styles Shape Academic Engagement: The Roles of Academic Impostor Syndrome and AI Policy Clarity.

Yu Wang, Xiaoxue Mi, Wenwen Tang, Yawei Tang, Heyuan Gao

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Yu WangSchool of Business Administration, Dongbei University of Finance and Economics, Dalian 116025, China.
Xiaoxue MiSchool of Business Administration, Dongbei University of Finance and Economics, Dalian 116025, China.
Wenwen TangSchool of Urban Governance and Public Affairs, Suzhou City University, Suzhou 215000, China.
Yawei TangHumanities and Social Sciences Department, Aviation University of Air Force, Changchun 130022, China.
Heyuan GaoSchool of Business Administration, Dongbei University of Finance and Economics, Dalian 116025, China.

Funding

Educational Department of Liaoning Province LJ112410173010
6 · The paper itself

Abstract

Generative AI (GenAI) is increasingly embedded in higher education, yet evidence on its implications for students' academic engagement and psychological experiences remains mixed. One possible reason is that prior research has often focused on how much students use AI and their general confidence in task completion, while paying less attention to how they use AI and how they attribute AI-supported achievement. To address this gap, this study distinguishes reflective from thoughtless AI use, examines academic impostor syndrome as a self-evaluative mechanism linking AI use styles to academic engagement, and tests perceived AI policy clarity as a contextual moderator. A two-wave survey of 478 Chinese university students showed that reflective AI use was negatively associated with academic impostor syndrome, whereas thoughtless AI use showed the opposite pattern. Academic impostor syndrome, in turn, was negatively associated with engagement and mediated both pathways. Perceived AI policy clarity amplified these patterns. These findings suggest that GenAI integration should be understood not only as a question of adoption or efficiency, but also of interaction quality and competence attribution. The study highlights the importance of cultivating reflective AI literacy and developing institutional policies that are clear yet psychologically attuned to students' self-evaluative concerns.

Indexed as

academic engagementacademic impostor syndromeAI interaction stylesAI policy clarityAI use

Identifiers

PMID42352694
PMCPMC13295589

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.