ArticlePloS one2025
Empowering EFL teachers' perceptions of generative AI-mediated self-professionalism.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Teachers' competence, self-efficacy, and their attitudes toward generative AI in education: a correlational study.Frontiers in psychology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study intends to empower English as a Foreign Language (EFL) teachers' perceptions of generative artificial intelligence (AI)-mediated self-professionalism in engagement, attitudes, constraints, and solutions. Employing the mixed methods research design, the researchers collected data from male and female teachers (N = 278) of eight public universities, utilizing convenience sampling and a set of instruments: a questionnaire and a semi-structured interview. The data analysis combined quantitative and qualitative methods, using SPSS version 26 for statistical analysis (Pearson correlation, Cronbach's alpha, means, standard deviations), and thematic analysis for qualitative data, with data triangulation employed to compare questionnaire and interview responses for a comprehensive understanding of EFL teachers' engagement with generative AI. The results revealed that the study sample engaged in self-professionalism at a medium level, yet they hold high attitudes toward generative AI-mediated self-professionalism. In addition, the content analysis exhibited several constraints, including technological competence and AI literacy, AI-generated content reliability and accuracy, ethical issues, and encroachment on professional autonomy. Moreover, the respondents proposed solutions such as offering AI-driven training programs, establishing clear ethical guidelines and protocols, emphasizing AI as a supplementary tool rather than a substitute, and implementing impartial access mechanisms for AI content to strengthen EFL teachers' self-professionalism mediated by generative AI. Studies in the context of generative AI-driven self-professionalism appear limited, particularly in the context of Arab higher education institutions. This dearth of research presents an opportunity for the current study to make significant improvements in contributing innovative insights to the EFL teachers' self-professionalism landscape.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.