Evidence map›Paper›PMID 41646889›Full record

ArticleFrontiers in psychology2025

Explaining adoption of AI tools in education: a dual-path model of ethical concern and functional value.

Tao Yu, Yihuan Tian, Qianghong Huang, Zuling Cheng, Ru Zhang

Abstract read
In one paragraph

Article in Frontiers in psychology, 2025. 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

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

1 citing paper in PubMed.

  1. Article
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.

Tao YuCollege of Art & Design, Nanning University, Nanning, China.
Yihuan TianCulture Design Lab, Graduate School of Techno Design, Kookmin University, Seoul, Republic of Korea.
Qianghong HuangDepartment of Smart Experience Design, Graduate School of Techno Design, Kookmin University, Seoul, Republic of Korea.
Zuling ChengDepartment of Global Convergence, Kangwon National University, Chuncheon-si, Republic of Korea.
Ru ZhangCollege of Art & Design, Nanning University, Nanning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: As Artificial Intelligence-Generated Content (AIGC) tools (e.g., ChatGPT for writing assistance, Midjourney for image generation) diffuse into educational settings, their adoption reflects a psychological interplay between functional appraisals and ethical concerns. This study proposes and tests a dual-path model integrating the Technology Acceptance Model (TAM) with Protection Motivation Theory (PMT), incorporating Perceived Ethical Concern (PEC) and Moral Sensitivity (MS). Methods: Ten latent constructs were modeled: Perceived Severity (PS), Perceived Vulnerability (PV), Self-Efficacy (SE), Response Efficacy (RE), Perceived Ease of Use (PEOU), Perceived Usefulness (PU), PEC, MS, Behavioral Intention (BI), and Continuance Intention (CI). Using structural equation modeling based on data from 589 respondents with prior AIGC experience, we evaluated 14 hypotheses. Results: The results support the TAM pathway: PU and PEOU positively predict BI and CI. Meanwhile, PMT components operate indirectly; RE and SE influence appraisals by elevating PU and mitigating PEC, whereas PS and PV elevate PEC. PEC shows a significant negative effect on BI and an indirect negative impact on CI. Notably, this negative PEC-BI association is more pronounced among individuals with higher MS. Discussion: The findings extend psychological accounts of AI tool adoption by jointly modeling moral appraisal and functional value in educational contexts. Furthermore, the study offers actionable implications for platform design and policy, suggesting that improving usability and efficacy cues while increasing ethical transparency can foster responsible, sustained use.

Indexed as

AIGCbehavioral intentioneducational technologyperceived ethical concernprotection motivation theorytechnology acceptance model

Identifiers

PMID41646889
PMCPMC12868169

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.