Evidence map›Paper›PMID 41145574›Full record

ArticleScientific reports2025

Primary teachers' acceptance and sustained adoption of AI powered learner corpora for writing instruction through TAM and ECM perspectives.

Na Zhang, Wei Lun Wong, Jessie Barrot

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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.

Na Zhang *Chengdu University, Chengdu, China.
Wei Lun Wong *Faculty of Education, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia. colinw@ukm.edu.my.
Jessie Barrot *College of Education, Arts and Sciences, National University, Manila, Philippines.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) offers significant potential to enhance writing instruction in primary education. However, its sustained adoption by English language teachers remains insufficiently understood. This study examines the factors influencing teachers' acceptance and continued use of AI-powered learner corpora, drawing on the Technology Acceptance Model (TAM) and Expectation Confirmation Model (ECM). A structured questionnaire was administered to 355 primary school English teachers in Malaysia to assess variables related to external support (e.g., facilitating conditions, teacher-learner interaction), individual characteristics (e.g., perceived self-efficacy, expectancy effects, growth mindset, interest) and technology perceptions (e.g., perceived ease of use, perceived usefulness, satisfaction, continuance intention). Data were analyzed using exploratory and confirmatory factor analysis, followed by Structural Equation Modeling. Results highlight perceived self-efficacy and interest as key predictors of perceived usefulness and continuance intention. They underscored the role of intrinsic motivation. In contrast, facilitating conditions did not significantly affect perceived ease of use, possibly due to increased digital familiarity among teachers. These findings validate the applicability of TAM and ECM in the primary education context and offer practical insights for designing AI tools that align with teachers' pedagogical needs, enhance user experience and support the sustainable integration of AI in writing instruction.

Indexed as

Artificial IntelligenceSchool TeachersWritingAdultFemaleHumansLearningMalaysiaMaleMiddle AgedMotivationSelf EfficacySurveys and QuestionnairesArtificial intelligenceExpectation confirmation modelLearner corporaTechnology acceptance modelWriting skill

Identifiers

PMID41145574
PMCPMC12559428

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.