Raudhatul Haura
Universitas Islam Kalimantan (UNISKA) Muhammad Arsyad Al Banjari

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Artificial Intelligence in Intelligent Tutoring Systems for Education Literature Review and Bibliometric Analysis Using R-Biblioshiny Raudhatul Haura; Farouq Sessah Mensah; Alaa Hussein Jafar Al-Anbari; Hariharasudan Anandhan; Mustafa Kayyali
LEOTECH: Journal of Learning Education and Technology Vol. 3 No. 1 (2026): LEOTECH: Journal of Learning Education and Technology
Publisher : CV. Akademi Merdeka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70152/leotech.v3i1.341

Abstract

 The rapid advancement of Artificial Intelligence (AI) has accelerated the integration of technology into digital learning, particularly through Intelligent Tutoring Systems (ITS) that are capable of adapting instructional content, feedback, and learning pathways to students’ individual needs. The growing volume of publications on AI-based ITS highlights the need for a systematic mapping of the literature to better understand research trends, thematic emphases, and future research directions. This study aims to analyze publication trends, identify influential authors, institutions, journals, and countries, map the conceptual structure of the research field, and uncover research gaps and potential avenues for future studies. A quantitative approach was employed using bibliometric analysis. Data were retrieved from the Scopus database through searches of titles, abstracts, and keywords, and were subsequently screened using the PRISMA flow diagram, resulting in 322 articles published between 2012 and 2026. Bibliometric analysis was conducted using the Bibliometrix package and Biblioshiny to examine publication patterns, citation performance, collaboration networks, and keyword and thematic relationships. The findings indicate a steady increase in publications, with dominant themes centered on AI, intelligent tutoring systems, and adaptive learning. However, studies focusing on pedagogical implementation and long-term learning outcomes remain relatively limited. These results point to significant opportunities for future research, particularly in empirical evaluation and pedagogical integration. Overall, this study provides a comprehensive overview of the development of AI-based ITS research and serves as a valuable reference for researchers and practitioners in designing learning systems that align with educational needs.
Discourse Markers in Learners’ YouTube Comments: A Study of Informal Academic English Raudhatul Haura; Aloba Fatimah Musa
DUTIES: Education and Humanities International Journal Vol. 1 No. 2 (2025): DUTIES: Education and Humanities International Journal
Publisher : CV. Akademi Merdeka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70152/duties.v1i2.226

Abstract

This study investigates the use of discourse markers (DMs) by English as a Foreign Language (EFL) learners in YouTube comments on academic-related videos. Drawing on Fraser’s typology, the research explores the types of DMs most frequently used and how learners employ them to structure arguments, express stance, and maintain coherence in informal academic English. A total of 300 learner comments, some from the same users, were collected from ten academic YouTube videos and analysed through qualitative content analysis. The findings reveal that learners primarily used contrastive, elaborative, and inferential discourse markers such as but, also, and therefore to organize ideas and express reasoning. Stance-related markers like I think and actually were also prevalent, signaling learners' personal evaluations. Additionally, elaborative markers contributed to textual flow and coherence. These patterns indicate that EFL learners are able to apply academic discourse strategies within informal digital contexts, demonstrating emerging discourse competence and pragmatic awareness. The study highlights the pedagogical potential of digital platforms like YouTube as spaces for meaningful language use and suggests incorporating informal online texts into language teaching practices.