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  • 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.
  • 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.