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Riwayat Publikasi (Q2)

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  • 2026Tahun PublikasiQ2Scopus Quartile: 2 (Q2)JIF: 1.2Journal Impact Factor (JIF): 1.2SJR: 0.25SCImago Journal Rank (SJR): 0.25
    Machine-translating feminism: Generative AI, epistemic injustice, and the politics of gendered meaning in Indonesian feminist discourse
    N Nursaid, E Wulandari, & BM Ghaluh
    Abstrak:
    In transnational feminist knowledge circuits, generative AI has become a decisive yet insufficiently theorised mediator of gendered meaning across languages. Large language models do not simply transfer words between Indonesian and English; they set the moral, political, and gendered conditions under which feminist vocabularies become intelligible. This article intervenes at the intersection of feminist translation studies, epistemic injustice theory, and multilingual AI critique to theorise what we term machine-translating feminism: the process through which generative AI translates feminist language while reformatting feminism into a more manageable discursive object. Departing from English-centric AI bias scholarship, we turn to Indonesian feminist discourse as a Global South site where womanhood, sexuality, violence, and anti-feminist backlash are mediated through contested vocabularies. Through close readings of Ayu Utami's Saman and Leila S. Chudori's Laut Bercerita, alongside public feminist vocabularies, the article identifies four diagnostic patterns of translational distortion: lexical neutralisation, moral simplification, depoliticisation of structural violence, and patriarchal overcoding. The analysis shows how generative AI operates as a translation regime rather than a neutral tool, producing what we term patriarchal legibility: a mode of readability in which feminist conflict becomes internationally consumable by becoming less threatening to patriarchal, institutional, and Anglophone interpretive regimes.
  • 2026Tahun PublikasiQ2Scopus Quartile: 2 (Q2)JIF: 1.8Journal Impact Factor (JIF): 1.8SJR: 0.59SCImago Journal Rank (SJR): 0.59
    A teacher-mediated AI curriculum for oral language development: A design-based research in Indonesian kindergartens
    BM Ghaluh
    Abstrak:
    Although artificial intelligence (AI) is transforming language education, its application in early childhood oral language curricula remains underexplored. This design-based research developed and evaluated a teacher-mediated, AI-enhanced oral language curriculum and blended professional development programme for kindergarten teachers in Yogyakarta, Indonesia. Six play-based modules integrate Google Gemini for real-time story generation and NotebookLM for preparing structured materials; all AI interaction is teacher-mediated, with children engaging through guided play rather than direct screen use. A mixed-methods design involved 94 teachers and 186 children aged 4 to 6 across six schools over 16 weeks. Following intervention, teachers demonstrated significant self-efficacy gains (d = 0.87), implementation fidelity averaged 81.3%, and children showed significant oral language improvement (d = 0.46), with notable gains in narrative production (d = 0.43) and vocabulary (d = 0.38). These findings provide empirical evidence for a teacher-mediated AI curriculum in early childhood oral language education, offering a replicable, developmentally appropriate model for integrating generative AI into play-based curricula without direct child–screen interaction.
  • 2025Tahun PublikasiQ2Scopus Quartile: 2 (Q2)SJR: 0.40SCImago Journal Rank (SJR): 0.40
    Exploring the impact of adaptive real-time quiz platforms with differentiated learning features on student engagement and learning outcomes: A mixed-methods approach
    S Ramadhan, A Atmazaki, AG Ningsih, Y Hayati, MDF Henanggil, N Nursaid, F Rahman, & BM Ghaluh
    Abstrak:
    The integration of digital tools in education has significantly transformed traditional teaching and assessment practices, opening new pathways for enhanced engagement and learning. This study employs a mixed-methods approach to investigate the impact of a custom-developed adaptive real-time quiz platform, designed with differentiated learning features that tailor questions and feedback based on individual student needs to boost engagement and improve learning outcomes. A total of 100 high school students participated in the experiment, split into two groups: one group used traditional paper-based quizzes (n = 50), while the other engaged with the custom-developed adaptive digital quiz platform (n = 50) for real-time assessments. Quantitative data were collected via pre- and posttests to assess knowledge retention and academic performance, showing a marked improvement in the experimental group compared to the control group (p < 0.001). Additionally, qualitative data from student interviews and surveys provided valuable insights into motivation and engagement levels. Results indicate that students using the custom-developed adaptive real-time quiz platform, with its differentiated learning capabilities, exhibited increased engagement, faster response times, and improved performance relative to the control group. This study contributes to the expanding educational technology literature by demonstrating the effectiveness of adaptive interactive quiz platforms as engaging, impactful tools for real-time learning and assessment, with promising implications for enhancing student outcomes through personalized learning.
  • 2024Tahun PublikasiQ2Scopus Quartile: 2 (Q2)SJR: 0.40SCImago Journal Rank (SJR): 0.40
    Development of FonBi application: A phonetic transcription tool assisted by artificial intelligence for Indonesian language
    N Nursaid, BM Ghaluh, Y Hayati, MI Nasution, AG Ningsih, E Wulandari, & AT Harahap
    Abstrak:
    This study discusses the development of FonBi (Fonologi Bahasa Indonesia or Phonology of Indonesian Language in English), a phonetic transcription tool designed to aid foreign speakers learning Indonesian. The research highlights the challenge of ineffective language learning for BIPA (Bahasa Indonesia bagi Penutur Asing or Indonesian Language for Foreign Speakers) learners due to the complex phonological system of Indonesian. To address this issue, the study followed a development cycle model and created FonBi, aimed at improving learning effectiveness and accelerating the language mastery process. The application provides accurate and precise phonetic transcription to help learners better understand and master the Indonesian language. The validation results from material and media experts indicate that FonBi is “very feasible,” while limited testing by students suggests that it is also feasible. The study concludes that FonBi could provide an innovative solution for BIPA learners, bridging the gap between Indonesian speakers and foreign learners by enabling them to properly understand and pronounce the language. Overall, this study highlights the importance of phonetic transcription in language learning and the value of developing tools like FonBi to improve learning outcomes. By providing a user-friendly and accessible tool, FonBi has the potential to greatly enhance the learning experience of BIPA learners, and potentially serve as a model for developing similar tools for other languages and contexts.
  • 2024Tahun PublikasiQ2Scopus Quartile: 2 (Q2)SJR: 0.40SCImago Journal Rank (SJR): 0.40
    Development of an automated message writing tool assisted by artificial intelligence to facilitate communication between students and lecturers
    Y Hayati, BM Ghaluh, E Wulandari, N Nursaid, Y Rasyid, M Adek, & B Pratama
    Abstrak:
    This research project presents the development of Pesani, an innovative automatic messaging tool that harnesses Artificial Intelligence (AI) to offer suggestions on grammar, vocabulary, and writing style within messages. Pesani's primary objective is to enhance communication between students and lecturers participating in language literacy courses. The development process followed a well-structured Research and Development (R&D) methodology that incorporated iterative cycles to refine the tool. To ensure Pesani's efficacy and relevance, the research team collaborated closely with subject matter experts and media specialists. Their input was invaluable in shaping Pesani into a tool that not only functioned effectively but also met its users' specific needs. Validation conducted by subject matter and media experts has shown that Pesani is exceptionally well suited for its intended purpose. Furthermore, limited testing involving students has demonstrated that Pesani is highly effective in facilitating communication between students and professors. These results underscore Pesani's potential as an innovative solution to significantly enhance communication and improve student learning outcomes in language literacy courses.