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All Journal Journal Of Academia In English Educations (JADEs)
Dewi Novita
Universitas Tanjungpura
Arts Education Languange, Linguistic, Communication & Media Social Sciences Other

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Pre-Service EFL Teachers’ Attitudes towards CRT-GenAI Integration: A Predictive Correlational Study on Demographics and Experiences Eka Fajar Rahmani; Dewi Novita
JADEs Journal of Academia in English Education Vol. 7 No. 1 (2026): JADEs Journal of Academia in English Education
Publisher : Tadris Pendidikan Bahasa Inggris IAIN Langsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32505/jades.v7i1.14352

Abstract

This study investigates pre-service EFL teachers' attitudes toward integrating Culturally Responsive Teaching (CRT) and Generative AI (GenAI), examining whether demographics (age, gender) and prior experience predict these attitudes. Employing a correlational-predictive design, data were collected from 35 seventh-semester students who had completed a teaching practicum. Participants completed demographic items and a 4-point Likert scale measuring attitudes toward CRT-GenAI integration. Descriptive findings revealed highly positive attitudes, particularly regarding GenAI's pedagogical benefits and its potential to address diverse learner needs. However, ANOVA results indicated no significant differences in attitudes across varying levels of prior experience. Furthermore, multiple regression analysis demonstrated that age, gender, and prior experience were not significant predictors, accounting for only 4.5% of the variance in attitudes. These results suggest that positive attitudes are shaped more by conceptual understanding of CRT-GenAI's pedagogical relevance than by demographic or experiential factors. The study contributes a novel predictive framework examining these constructs together. Pedagogically, it underscores the need for teacher education programs to provide integrated training in culturally responsive pedagogy and AI literacy. Limitations include the small, single-institution sample, suggesting future research should explore broader populations and additional variables like AI literacy or self-efficacy.
Co-Authors Eka Fajar Rahmani