arrow_backKembali ke Beranda

Riwayat Publikasi

14

Kumpulan publikasi ilmiah dan hasil kolaborasi riset saya bersama peneliti lain

  • 2026Tahun PublikasiQ1Scopus Quartile: 1 (Q1)JIF: 6.7Journal Impact Factor (JIF): 6.7SJR: 2.24SCImago Journal Rank (SJR): 2.24
    Designing AI-generated multi-format reading assessment across Bloom’s cognitive levels: effects on L2 reading comprehension, cognitive engagement, and motivation
    A Kusmiatun & BM Ghaluh
    Abstrak:
    Artificial intelligence (AI) has advanced automated question generation, yet implementations remain single-format and English-dominant. This study examined an AI-generated multi-format reading assessment platform for Chinese-speaking learners of Indonesian as a second language (L2), focusing on reading comprehension, motivation, and cognitive engagement. The platform generated eight formats across Bloom's revised taxonomy levels (LOTS, MOTS, HOTS) and integrated a trilingual interface, retrieval-augmented chatbot, multiplayer assessment, learning analytics, instructor dashboard, and accessibility tools: text-to-speech, display settings, and visual filters. A quasi-experimental mixed-methods design involved 300 learners (n = 150 experimental; n = 150 control) at three universities over 12 weeks. Experimental learners used the integrated platform; controls read identical texts with multiple-choice-only quizzes. ANCOVA showed higher post-test comprehension, F(1, 297) = 26.14, p < .001, partial η² = .08; corrected contrasts showed larger effects at LOTS, MOTS, and HOTS (d = 0.35, 0.54, 0.89). Engagement increased, F(3.72, 554.28) = 18.67, p < .001, partial η² = .11, and motivation was higher, t(298) = 5.18, p < .001, d = 0.60. Interviews with 24 learners showed format diversity, taxonomy-based scaffolding, contextual chatbot support, gamification, and accessibility shaped usefulness and engagement. Findings support AI-generated multi-format assessment and Bloom-based item generation for less commonly taught L2s.
  • 2026Tahun PublikasiQ1Scopus Quartile: 1 (Q1)JIF: 4.7Journal Impact Factor (JIF): 4.7SJR: 2.05SCImago Journal Rank (SJR): 2.05
    New ways to teach and learn pragmatics for advancing academic communication competence: An AI-mediated multimodal platform
    T Tressyalina, BM Ghaluh, E Wulandari, E Noveria, & E Arief
    SAGE
    Language Teaching Research, 13621688261446189.
    Abstrak:
    Effective academic communication in multilingual higher education requires pragmatic competence, yet this dimension of language ability is often overlooked in instruction and insufficiently supported through feedback or authentic practice. Grounded in instructed pragmatics and technology-mediated learning frameworks, this study reports on the design, implementation, and empirical evaluation of an artificial intelligence (AI)-mediated multimodal platform developed to scaffold pragmatic competence in Indonesian within digital academic contexts. The platform integrates five interdependent features: automated text analysis to identify speech acts, politeness strategies, and violations of conversational principles; video analysis with multilingual transcription and theory-based annotation; conversation simulations providing context-specific academic interactions and real-time feedback; an AI consultation assistant offering explanations, examples, and targeted suggestions; and a digital politeness forum for peer and instructor exchange. A quasi-experimental mixed-methods design was employed with 240 undergraduates (120 in the experimental group, 120 in the control group) over a 5-week intervention. Pre- and postintervention assessments combined a validated pragmatic competence test, discourse completion tasks, and a self-report questionnaire on pragmatic awareness and confidence, complemented by student reflections and discussions. The experimental group demonstrated significantly greater gains in pragmatic test scores (M = 15.0, p < .001) compared with the control (M = 5.2, p < .001), alongside improvements on the discourse completion task (experimental = +14.1 vs. control = +4.8, both p < .001) and medium-to-large effects on awareness and confidence in the questionnaire. Regression analysis identified engagement with conversation simulations (β = 0.58, p < .001) plus AI consultation (β = 0.29, p = .012) as predictors of learning. Reflections and discussions underscored the platform’s role in enabling authentic practice, personalized feedback, and a safe environment for pragmatic experimentation. This study provides the first empirically validated model linking integrated AI-mediated scaffolding features to measurable and transferable gains in pragmatic competence, offering a scalable framework for multilingual and intercultural higher education.
  • 2026Tahun PublikasiQ1Scopus Quartile: 1 (Q1)JIF: 6.7Journal Impact Factor (JIF): 6.7SJR: 2.24SCImago Journal Rank (SJR): 2.24
    Enhancing students’ critical thinking in criminal case solving: An AI-based pragmatic application for analyzing authentic Indonesian texts and videos
    T Tressyalina, BM Ghaluh, E Wulandari, E Arief, & E Noveria
    Abstrak:
    This study evaluates the impact of Pragmatika, an AI-enhanced pragmatic application designed to analyze authentic Indonesian texts and videos, on the development of critical thinking in criminal justice education. A mixed-methods design was used, involving 100 undergraduates randomly assigned to an experimental group (n = 50) using Pragmatika or a control group (n = 50) using traditional methods. Baseline scores were comparable. Post-intervention, on a 30-point scale, the experimental group’s mean score rose from 15.2 (SD = 4.1) to 23.4 (SD = 5.0; t(49) = -9.12, p < .001), with notable gains in evidence evaluation and argument analysis. The control group showed a smaller increase from 15.1 to 18.3 (p < .01). Qualitative findings supported these results, with students reporting greater motivation, engagement, and confidence in linking textual evidence to suspect behavior, aided by Pragmatika’s interactive features. These results demonstrate the tool's effectiveness in enhancing analytical reasoning and bridging theoretical learning with practical case-solving. The study offers actionable strategies for integrating AI into case-based instruction to improve educational outcomes and highlights Pragmatika's potential as a scalable solution for critical thinking development in legal education.
  • 2026Tahun PublikasiQ1Scopus Quartile: 1 (Q1)JIF: 1.9Journal Impact Factor (JIF): 1.9SJR: 0.92SCImago Journal Rank (SJR): 0.92
    Enhancing digital and multimodal literacy in Indonesia's language education: A strategic policy framework for twenty-first-century engagement
    BM Ghaluh, E Wulandari, E Swatika Sari, & M Suryaman
    Abstrak:
    In the digital era, multimodal literacy – the ability to interpret and create meaning through various communication modes such as text, images, audio, and video – is essential for developing twenty-first-century skills. This study presents a strategic framework to integrate multimodal literacy into Indonesia's language education policy to enhance digital literacy and student engagement. Building on contemporary educational theories, the framework emphasizes incorporating digital tools into language curricula to accommodate different learning styles and promote critical thinking. Employing a mixed-methods approach, the research conducts a policy analysis to identify gaps and opportunities for multimodal integration within Indonesia's language education system. The framework was piloted in selected schools through curriculum redesign, professional development for educators, and digital infrastructure investment. Pre- and post-intervention assessments indicate significant improvements in students' digital literacy skills and engagement. Qualitative insights from interviews and focus groups support these findings, highlighting enhanced instructional practices and increased proficiency in utilizing multimodal resources. The study underscores the critical role of strategic policy integration in advancing multimodal literacy within language education, offering implications for policymakers, educators, and stakeholders. By promoting an inclusive learning environment, the framework serves as a catalyst for transformative practices that prepare students for success in a digitally interconnected world.
  • 2026Tahun PublikasiQ1Scopus Quartile: 1 (Q1)JIF: 1.9Journal Impact Factor (JIF): 1.9SJR: 0.92SCImago Journal Rank (SJR): 0.92
    Innovative language education policies for literacy development in Indonesia’s 3T regions: Addressing access and equity challenges
    E Wulandari, BM Ghaluh, M Suryaman, & ES Sari
    Abstrak:
    Indonesia's 3T regions – Terluar (Outermost), Terdepan (Frontier), and Tertinggal (Underdeveloped) – are areas where educational disparities are most acute, shaped by geographic isolation, socio-economic deprivation, and significant linguistic diversity. This study examines barriers to literacy development in three 3T-designated districts in West Sumatra Province (Mentawai Islands, Solok Selatan, and Pasaman Barat), evaluates current language education policies, and proposes context-sensitive approaches to improve literacy outcomes. Using an explanatory sequential mixed-methods design, the research integrates quantitative data from literacy assessments and surveys administered to 160 participants with qualitative insights from interviews and field observations. The findings reveal five compounding barriers: mismatches between students' mother tongues and the Bahasa Indonesia medium of instruction, socio-economic pressures reducing school attendance, chronic infrastructure deficits, shortages of teachers trained in multilingual pedagogy, and diminished student motivation. A disconnect between uniform national language education policies and the realities of extreme-periphery settings exacerbates these barriers. In response, the study proposes bilingual and mother-tongue-based instruction, conditional financial support, infrastructure investment, teacher capacity-building in culturally responsive pedagogy, and community-driven curriculum development. It contributes to Language Policy and Planning scholarship by arguing that prevailing frameworks require a structural-capacity dimension accounting for material conditions shaping policy implementation in contexts of compounded marginalization.
  • 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.
  • 2026Tahun PublikasiSINTA 2Science and Technology Index (SINTA): 2 (S2)
    An application for paraphrasing assisted by artificial intelligence to reduce plagiarism levels in multilingual academic writing
    N Nursaid, Y Hayati, T Tressyalina, E Wulandari, & BM Ghaluh
    Universitas Ahmad Dahlan
    BAHASTRA, 46 (2).
    Abstrak:
    This research focuses on creating and evaluating the efficacy of Languafrasa aimed at decreasing plagiarism rates in student essays. Beyond lowering similarity scores, the study positions Languafrasa as an educational writing companion that helps students compare alternative expressions, preserve meaning, and practise responsible paraphrasing in Indonesian academic writing. Languafrasa is designed to incorporate AI-powered paraphrasing functionalities in Indonesian and 102 other languages. The study followed an experimental design involving 30 students who were divided into two groups. One group used conventional essay writing methods, while the other utilized the Languafrasa application. The experimental outcomes revealed a significant decrease in plagiarism levels among students using the Languafrasa application. Additionally, students in the experimental group demonstrated higher efficiency than those in the control group. Feedback from the questionnaire administered to all participants corroborated these results, with most students favoring the application over traditional writing methods. Users found Languafrasa enjoyable, user-friendly, and an effective aid in essay-writing instruction. These findings suggest that Languafrasa contributes not only to textual originality but also to students' confidence and engagement in learning how to paraphrase.
    Kata kunci:
    artificial intelligenceparaphrasingplagiarismacademic writingLanguafrasamultilingual
  • 2026Tahun PublikasiSINTA 2Science and Technology Index (SINTA): 2 (S2)
    Developing a dialogic digital reading learning environment for Indonesian literacy: Evidence from annotation, discussion, and comprehension traces
    N Nursaid, M Hafrison, MI Nasution, & BM Ghaluh
    CV. IMRECS
    Pedagogy Review, 5 (3), 301-317.
    Abstrak:
    Digital reading environments make students' reading processes more visible because they capture annotations, discussion posts, comprehension attempts, and activity logs. However, these traces are often reduced to participation totals rather than used to inform the design of reading pedagogy. This study developed and evaluated a dialogic digital reading learning environment for Indonesian literacy. Using a design-based mixed-methods approach, 100 undergraduate students engaged with genre-based Indonesian texts over six weeks through interactive annotation, threaded discussion, audio support, and comprehension practice. The dataset comprised 1,842 annotations, 1,126 discussion comments, pretest and posttest reading comprehension scores, platform activity logs, and 20 follow-up interviews. The learning environment was organized around five dialogic engagement dimensions: text-grounded noticing, inferential linking, evaluative stance, dialogic uptake, and metacognitive reflection. Coding showed strong interrater reliability (Cohen's kappa = .86). Reading comprehension increased from pretest (M = 69.84, SD = 9.18) to posttest (M = 83.72, SD = 7.64), t(99) = 15.99, p < .001, with a repeated-measures effect size of dz = 1.60. Dialogic uptake and inferential linking were the strongest predictors of comprehension gain, and the regression model explained 65% of the variance. Interview findings indicated that annotations helped students locate textual difficulty, discussion threads supported peer-mediated interpretation, and comprehension tasks prompted students to return to the text rather than respond superficially. The study offers a pedagogical model for designing Indonesian digital reading environments as social, reflective, and evidence-informed learning spaces.
    Kata kunci:
    digital readingdialogic engagementlearning environmentsocial annotationIndonesian literacy
  • 2025Tahun PublikasiQ1Scopus Quartile: 1 (Q1)JIF: 4.7Journal Impact Factor (JIF): 4.7SJR: 2.05SCImago Journal Rank (SJR): 2.05
    An artificial intelligence assistant to reader response theory: Pioneering novel analysis in the digital age
    N Nursaid, BM Ghaluh, & E Wulandari
    SAGE
    Language Teaching Research, 13621688251368636.
    Abstrak:
    This study investigates the impact of an artificial intelligence (AI) assistant on reader response theory in novel analysis using a mixed-methods approach. It examines how AI-generated real-time feedback, powered by advanced machine learning and natural language processing, enhances interpretive possibilities beyond conventional methods, aligning with reader response theory's emphasis on reader-text interaction. The AI assistant, designed with Real-Time Theme Identification, Character Relationship Mapping, Symbolism Detection, and Interactive Literary Simulation, supports nuanced interpretations, uncovers underlying patterns, and fosters deeper engagement with literary texts. Participants (n = 100), aged 15–18 years, were divided into an experimental group (n = 50), which used the AI assistant for novel analysis, and a control group (n = 50), which relied on traditional literary analysis methods. Quantitative data were collected through pre- and post-study assessments of participants' interpretive skills, measured on a 100-point scale, while qualitative insights were gathered via in-depth interviews and focus groups. The AI's effectiveness in interpretive skills and comprehension was evaluated by comparing outcomes between groups. The results show that the experimental group markedly outperformed the control group, with a mean increase in interpretation scores from 70.5 (SD = 6.1) to 85.2 (SD = 5.8; t(49) = 5.23, p < .001), reflecting a 20.8% improvement in identifying textual connections and a 15% increase in offering diverse perspectives. In contrast, the control group's scores rose modestly from 69.8 (SD = 6.3) to 75.1 (SD = 6.2; t(49) = 2.14, p < .05), showing only a 7.6% improvement in textual connections and a 5% increase in diverse perspectives. Qualitative findings indicated improved comprehension, critical thinking, motivation, and emotional engagement, with 80% of participants reporting increased analytical confidence due to the AI assistant. These results suggest that AI integration advances reader response theory, improves interpretation, and enhances accessibility for diverse students in digital literary education.
  • 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.
  • 2025Tahun PublikasiSINTA 3Science and Technology Index (SINTA): 3 (S3)
    An artificial intelligence-assisted storytelling tool for enhancing students' writing skills: Exploring axiological dimensions
    BM Ghaluh, E Swatikasari, A Efendi, & H Hartono
    Universitas Negeri Padang
    Jurnal Bahasa dan Sastra, 13 (2), 441-454.
    Abstrak:
    This study presents the development and evaluation of RekaCiptaCerita, an artificial intelligence-assisted storytelling tool designed to enhance students' writing skills by exploring axiological dimensions. The tool integrates advanced AI algorithms to provide real-time feedback, suggestions, and creative prompts, aiming to improve students' narrative construction, vocabulary, and overall writing proficiency. An experimental methodology was employed, involving a controlled study with students from diverse backgrounds. Participants used RekaCiptaCerita over eight weeks, during which their writing skills were assessed through pre- and post-intervention tests. The experiment aimed to measure the tool's effectiveness in enhancing writing abilities and fostering an understanding of ethical, aesthetic, and moral values in writing. Results indicated significant improvements in students' writing skills, including enhanced creativity, coherence, and stylistic sophistication. The incorporation of axiological dimensions in the AI tool promoted critical thinking and ethical awareness, enabling students to produce more meaningful and reflective narratives. Students reported that the AI-assisted tool not only helped them structure their stories better but also encouraged them to consider deeper philosophical questions related to their narratives. Future research should explore the long-term impacts of such tools and their applicability across different educational contexts. RekaCiptaCerita can be accessed via https://rekaciptacerita.my.id.
  • 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.
  • 2022Tahun PublikasiSINTA 2Science and Technology Index (SINTA): 2 (S2)
    Optimization reading to learn learning model on narrative text writing skills for junior high school students
    Y Hayati, RH Ulya, M Amazola, H Hafrizal, BM Ghaluh, & I El Husna
    LPPM STAI Hubbulwathan
    Al-Ishlah: Jurnal Pendidikan, 14 (4), 5099-5110.
    Abstrak:
    Educational research is currently more dominated by the development of learning models. However, this has not shown satisfactory results because it tends not to be piloted or disseminated. The purpose of this study is to explain the effect of applying the reading to learn learning model on improving students' narrative text writing skills, especially junior high school students. The sample of this study was the 9th-grade students of SMPN 5 Padang Panjang, totaling 32 students. Data was collected by processing the results of the pretest in the form of performance obtained before the implementation of the reading to learn learning model and the post-test obtained after the implementation of the reading to learn learning model. Data were analyzed using the descriptive analysis method. Based on the results of data analysis, it was found that there was an increase in students' narrative text writing skills before and after the implementation of the reading to learn learning model. This increase can be seen from the number of scores obtained by students regarding the completeness of the narrative text structure from 68 to 89, the completeness of narrative elements from 60 to 75, and the application of EBI from 50 to 72. Therefore, this study can be useful to see the effectiveness of the reading to learn learning model toward students' writing skills and as a way out of writing problems.