Evidence map›Paper›PMID 42143147›Full record

ArticleScientific reports2026

AI literacy and school tier moderate educators' ethical coping mechanisms through sedative and reverse Matthew effects.

Jun Wang, Yang Hu, Huilin Tang

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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
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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

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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

3 authors.

Jun WangFaculty of Humanities and Social Sciences, City University of Macau, Macau SAR, China.ORCID http://orcid.org/0009-0009-3320-699X
Yang HuSchool of Foreign Languages and Foreign Trade, Guilin University of Aerospace Technology, Guilin, China.
Huilin TangFaculty of Humanities and Social Sciences, City University of Macau, Macau SAR, China. h24092110255@cityu.edu.mo.ORCID http://orcid.org/0009-0005-9339-8330

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid penetration of artificial intelligence in education has triggered unprecedented ethical risk perception and response challenges for educators, but existing research lacks systematic exploration of deep ethical cognitive mechanisms. To address this, this study operationalizes macro level sociotechnical imaginaries into micro level psychological perceptions to construct and test a theoretical model that integrates dual-dimensional risk perception, AI Literacy, and situational factors. PLS-SEM analysis of 502 Chinese educators reveals that: both Inhibitory Ethical Risk and Promotional Professional Risk are significantly and positively associated with Defensive and Constructive Coping strategies simultaneously, challenging the traditional one dimensional risk hypothesis; AI Literacy significantly attenuated the positive association between Inhibitory Ethical Risk and Constructive Coping, supporting the hypothesis of the Literacy Sedative Effect; The strength of the positive association between Promotional Professional Risk and Constructive Coping in lower tier schools is 2.78 times stronger than that in first tier schools, providing empirical evidence for the Reverse Matthew Effect. This study proposes a dual-dimensional risk-coping framework, revealing the cognitive decoupling mechanism of AI Literacy and challenging the classical Matthew Effect, providing a new perspective for understanding individual behavior in disruptive technological change; at the same time, it also provides practical guidance for cultivating cautious and proactive adopters and developing differentiated AI Literacy training strategies.

Indexed as

Adaptation, PsychologicalArtificial IntelligenceLiteracyChinaCoping SkillsHumansSchoolsAI ethicsAI literacyDual-dimension risk–coping frameworkEducatorsPLS-SEMReverse Matthew Effect

Identifiers

PMID42143147
PMCPMC13369881

What OpenQuestion holds

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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.