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All Journal Seloka: Jurnal Pendidikan Bahasa dan Sastra Indonesia Devotion: Journal of Research and Community Service Indo-MathEdu Intellectuals Journal Edunity: Kajian Ilmu Sosial dan Pendidikan Pustaka: Jurnal Bahasa dan Pendidikan INJURITY: Journal of Interdisciplinary Studies Jurnal Pendidikan Indonesia (Japendi) Jurnal Pendidikan Bahasa dan Sastra Indonesia Literature Research Journal (LRJ)
Ifutya Warnisa
Universitas Bina Bangsa Getsempena
Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences

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Artificial Intelligence and the Future of Language Education in Indonesia: A Critical Review of Scopus-Indexed Deep Learning Applications Across Anxiety, Literacy, and Assessment Bagaskara Nur Rochmansyah; Ifutya Warnisa; Nur Amelia; Indrya Mulyaningsih
Devotion : Journal of Research and Community Service Vol. 7 No. 9 (2026): Devotion: Journal of Community Research
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/devotion.v7i9.25780

Abstract

This article presents a critical narrative review of Scopus-indexed deep learning applications in Indonesian language education, organized around three pedagogically significant domains: language anxiety, literacy development, and language assessment. The study examined 134 Scopus-indexed publications (2022–2026) that applied deep learning techniques, including convolutional neural networks, recurrent architectures, Transformer-based models such as IndoBERT, and multimodal vision–language systems, to Indonesian and regional languages in Indonesia. Through the lens of second-language acquisition (SLA) and learner-centered pedagogy, the review synthesized empirical evidence across the three domains, identified recurring pedagogical potentials (early affective detection, inclusive literacy access, and real-time formative feedback), and highlighted persistent challenges, including construct reductionism, algorithmic bias against non-standard language varieties, data privacy concerns, and academic integrity issues. The findings revealed that although deep learning research on Indonesian languages has advanced rapidly from a technical perspective, its pedagogical foundation remains underdeveloped, as most studies prioritize engineering performance metrics rather than SLA-oriented learning outcomes. This article proposes an SLA-informed evaluation framework encompassing input quality, interaction authenticity, output opportunities, affective filters, and learner agency, while articulating a learner-centered, multilingual, and ethically governed agenda for AI-mediated language education in Indonesia. The findings suggest that the future of language education in Indonesia will depend not only on model sophistication but also on the pedagogical and ethical frameworks that guide their implementation. Quantitative analysis of the 134 studies revealed three key findings: (1) 89% of the reviewed studies reported only engineering metrics without addressing learning outcomes; (2) none of the studies reported formal ethics review procedures or subgroup validity analyses for non-standard language varieties; and (3) although 23 studies demonstrated technical capabilities applicable to language learning, only 7 positioned their work within SLA or pedagogical frameworks.
Co-Authors Alin Nida Millatina Aliyah, Himatun Asep Purwo Yudi Utomo Bagaskara Nur Rochmansyah Belia Putri Kunsiyanto Desy Rufaidah Fajry, Khairul Indrya Mulyaningsih Inung Lufiana Melly Eka Putpri Najwa Khalisa Azahra Nur Amelia Nur Amelia Sari, Ratna Intan Septian Fatianda Supriyanto, RM. Teguh Tiara Apriliani Widiatmoko Tiara Apriliani Widiatmoko Tiara Apriliani Widiatomo Yusro Edy Nugroho