Andrias Yulianto
Jakarta International University

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INDONESIAN EFL UNDERGRADUATES MOTIVATION IN WRITING AND GENERATIVE ARTIFICIAL INTELLIGENCE: A CORRELATIONAL STUDY Benito Dira; Andrias Yulianto
SIGEH ELT : Journal of Literature and Linguistics Vol 6, No 1 (2026)
Publisher : University of Muhammadiyah Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36269/sigeh.v6i1.4736

Abstract

Generative AI (GenAI) is an enhanced fragment of Artificial Intelligence (AI). The emergence of GenAI has engendered numerous impacts on many sectors, including the English language learning and education industry. Motivation, one of the key aspects in language learning, has inspired many scholars as a subject of discussion within language education and second language learning and acquisition. This research sought to determine the correlation between GenAI and EFL Indonesian learners perceptions of using GenAI to motivate them in writing. An open-ended and close-ended questionnaire was used to collect quantitative and qualitative data from 30 Indonesian EFL undergraduate students through purposive sampling. The other group of 32 Indonesian EFL undergraduate students participated in the pilot study to test the credibility and validity of research instruments. The result suggests a moderate positive correlation between EFL undergraduate motivation in writing and GenAIs usage. Qualitative data analysis also demonstrated students positive perceptions of using GenAI technology in writing. They found it resourceful for boosting motivation, delivering immediate and personalized feedback, and encouraging self-directed learning. These findings indicate that integrating GenAI into language learning classes, particularly writing, can moderately enhance students' motivation and engagement, thus improving their overall writing skills. The primary limitation stemmed from the small scale and targeted group of participants. Future scholars and researchers are encouraged to explore the influence of a specific GenAI-driven app (e.g., Google Gemini), on another language's core skills, such as speaking or listening, throughout different educational environments and student demographics.