Evidence map›Paper›PMID 42272713›Full record

ArticleFrontiers in psychology2026

Teachers' competence, self-efficacy, and their attitudes toward generative AI in education: a correlational study.

Emmanuel Nana Kwesi Ofori Darko, Zhanyong Qi, Zhiyuan Wang

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

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

3 authors.

Emmanuel Nana Kwesi Ofori DarkoSchool of Education, Shaanxi Normal University, Xi'an, China.
Zhanyong QiSchool of Education, Shaanxi Normal University, Xi'an, China.
Zhiyuan WangSchool of Education, Shaanxi Normal University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The rapid integration of Generative Artificial Intelligence (GenAI) offers new opportunities and challenges for education, with teacher adoption being a key factor for successful implementation. However, the psychological factors influencing teachers' attitudes toward these tools remain poorly understood. This study explores the relationship between K-12 teachers' digital competence, their self-efficacy for AI integration, and their attitudes toward using GenAI in education. Methods: A quantitative, cross-sectional survey design was used. Data were collected from 352 K-12 teachers in a large urban school district in Indonesia through an online questionnaire. The survey assessed four constructs: General Digital Competence (GDC), AI-Specific Competence (AISC), Teacher Self-Efficacy for AI Integration (TSE-AI), and Attitudes Toward Generative AI in Education (ATGAI-E). Data analysis involved Pearson's correlations and hierarchical multiple regression. Results: The analysis showed strong positive correlations among all variables. Attitudes toward GenAI had the strongest correlation with TSE-AI ( Discussion: The findings indicate that while both general and AI-specific skills are important, a teacher's self-efficacy, their confidence in their ability to use GenAI effectively, is the most powerful predictor of their attitude. This suggests that successful GenAI adoption depends more on psychological empowerment than on technical training alone. Professional development programs should therefore focus on building teachers' confidence through hands-on practice, peer modeling, and supportive feedback to encourage positive engagement with the GenAI classroom.

Indexed as

digital competenceeducational technologygenerative AIself-efficacyteacher attitudes

Identifiers

PMID42272713
PMCPMC13247872

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.