Andi Mukarramah Nagauleng
Tadris Bahasa Inggris, Institut Agama Islam Negeri (IAIN) Manado, Indonesia

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Exploring EFL students’ perceptions of AI integrations through the brain-to-brainware lens Ahmad Mustamir Waris; Abdul Rahman; Andi Mukarramah Nagauleng
JEELS (Journal of English Education and Linguistics Studies) Vol. 13 No. 2 (2026): JEELS November 2026
Publisher : Universitas Islam Negeri (UIN) Syekh Wasil, Kediri, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30762/jeels.v13i2.8641

Abstract

Artificial intelligence (AI) has become increasingly integrated into English language learning. However, students’ perceptions particularly in Islamic university on the AI's pedagogical and ethical implications need further elaboration. This study addresses EFL students' perceptions of the use of artificial intelligence in a state Islamic university in Manado, Indonesia through the conceptual lens of brain-to-brainware, which refers to the shift from independent human cognition to AI-supported learning processes. Using a convergent mixed-methods design, data were collected through an online questionnaire containing closed and open-ended items. Quantitative data were analyzed using descriptive statistics, while qualitative responses were identified thematically. The findings indicate that students perceived AI as a practical and flexible learning support, particularly for understanding difficult material, writing and editing, translation, vocabulary development, and speaking practice. However, their acceptance was conditional because the students also reported concerns about inaccurate outputs, shallow explanations, technical barriers, overdependence, reduced critical engagement, and ethical misuse. The ethical and pedagogical dimension received the strongest endorsement, indicating students’ perceived need for responsible use, output verification, lecturer guidance, and clear institutional policies. Thus, this study extends current discussion on AI in English learning context by interpreting students' perceptions as a shift toward AI-assisted learning within the brain-to-brainware framework.