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All Journal PREMISE: Journal of English Education and Applied Linguistics IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature JOURNAL OF ENGLISH FOR ACADEMIC Aisyah Journal of Informatics and Electrical Engineering Scope: Journal of English Language Teaching PROJECT (Professional Journal of English Education) Jurnal Aisyah : Jurnal Ilmu Kesehatan Jurnal Pengabdian Kepada Masyarakat Ungu ( Abdi Ke Ungu) English Teaching Journal and Research: Journal of English Education, Literature, And Linguistics Journal of Applied Nursing and Health Journal of Information Systems and Technology Research Journal of English Teaching and Linguistic Issues (JETLI) Nuras : Jurnal Pengabdian Kepada Masyarakat IJOEM: Indonesian Journal of Elearning and Multimedia Finger : Jurnal Ilmiah Teknologi Pendidikan Aisyah Journal of English Language Teaching
Ariyanti, Septika
Universitas Aisyah Pringsewu
Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Veterinary Other

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THE ROLE OF ARTIFICIAL INTELLIGENCE (AI) IN ENGLISH LANGUAGE TEACHING IN THE ERA OF GLOBALIZATION: A LITERATURE REVIEW Ela Nurainisah; Septika Ariyanti; Defy Gustianing
English Teaching Journal and Research: Journal of English Education, Literature, And Linguistics Vol. 5 No. 2 (2025): ETJaR December 2025
Publisher : Center for the Research and the Community Service, Islamic Institut of Hasanuddin Pare - Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55148/h3a55366

Abstract

This study aims to review the impact and challenges of the application of Artificial Intelligence (AI) in English language teaching in the era of globalization. The background of this review is based on the significant digital transformation in language teaching methods triggered by technological advances, especially AI which offers a more adaptive and interactive approach to learning. The method used is a literature review with a qualitative descriptive approach to various scientific publications in the last 10 years obtained from databases such as Google Scholar, ResearchGate, and ScienceDirect. Literature review shows that AI can expand access to rich learning resources, and support adaptive learning. However, negative impacts and challenges such as reducing the role of the teacher as a critical learning facilitator, ethical issues, teacher readiness and over-reliance have also emerged significantly. Therefore, although AI has great potential in revolutionizing ELT, its application must consider ethical principles, human resource readiness, and the balance between technology and human touch in the learning process.
A Conceptual Framework for Technology-Enhanced Learning Design: Bridging Pedagogy and Digital Innovation Dita Septasari; Ikna Awaliyani; Nur Aminudin; Septika Ariyanti; Shima Asadi
FINGER : Jurnal Ilmiah Teknologi Pendidikan Vol. 5 No. 1 (2026): Finger : Jurnal Ilmiah Teknologi Pendidikan
Publisher : CV. Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/finger.v5i1.518

Abstract

Background: Learning designs must be grounded in pedagogical principles and make appropriate use of technical advancements in light of the digital transformation of education. Nonetheless, there is still a disconnect in many educational environments between the use of technology and pedagogical requirements.Aims: The research objective is to develop a conceptual framework for Technology-Enhanced Learning Design that bridges pedagogical principles with digital innovation. The research scope included a literature analysis, a review of best practices, and initial validation through education and technology experts.Methods: This research employed a qualitative approach with conceptual analysis and expert validation methods. Data were collected through a systematic literature review (2020–2025) and interviews with education and technology experts. Analysis was conducted using a thematic approach to identify the key dimensions of the technology-based learning design framework.Results: Pedagogical (learner-centered design, active engagement, personalization), technological (interoperability, scalability, AI integration), and implementation (continuous evaluation, institutional context, user readiness) are the three primary dimensions of the conceptual framework that emerged from the research. Compared to earlier studies, this framework has demonstrated the ability to more thoroughly integrate digital innovation with pedagogical concepts.Conclusion: The significance of combining technology and pedagogy in learning design is emphasized by this study. Researchers, educators, and legislators can use the conceptual framework that is produced as a guide for creating digital learning that is more sustainable and successful.
Design and Evaluation of AI-Enhanced Multimedia Learning Systems: Usability, Accessibility, and Engagement in Broadband-Based Online Education Ikna Awaliyani; Dita Septasari; Nur Aminudin; Septika Ariyanti
IJOEM: Indonesian Journal of E-learning and Multimedia Vol. 5 No. 2 (2026): Indonesian Journal of E-learning and Multimedia
Publisher : CV. Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijoem.v5i2.573

Abstract

Background: Artificial intelligence (AI) has increasingly been integrated into multimedia learning environments to support personalization, accessibility, and learner engagement in broadband-based online education. However, many existing systems still evaluate these dimensions separately, which limits their overall effectiveness and scalability.Aims: This study aims to design and empirically evaluate an AI-enhanced multimedia learning system using a unified evaluation framework that integrates system performance, usability, accessibility, and learner engagement within broadband-based higher education contexts.Methods: An explanatory sequential mixed-methods design was employed, involving quantitative analysis with 150 students and qualitative exploration with 12 participants. Data were collected through system performance logs, System Usability Scale (SUS) assessments, WCAG 2.1–based accessibility evaluations, and learner engagement metrics.Results: The findings indicate that AI-driven adaptivity improves system responsiveness, achieves high usability, supports digital accessibility, and enhances learner engagement in broadband-based learning environments. The results demonstrate the effectiveness of the system across technical, experiential, and behavioral dimensions.Conclusion: The key contribution of this study lies in proposing and validating an integrated evaluation framework that holistically captures the performance and user experience of AI-enhanced multimedia learning systems, an area that has been underexplored in prior research. These findings provide important theoretical and practical implications for the design of inclusive, adaptive, and user-centered online learning platforms.
An Intelligence-Oriented System Architecture for Integrated Pharmaceutical Data Analytics and Decision Support Ningsiah; Nur Aminudin; Septika Ariyanti; Ramil Abbasov
Journal of Information Systems and Technology Research Vol. 5 No. 1 (2026): January 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i1.1461

Abstract

This study proposes and evaluates an intelligence-oriented hybrid information system architecture for pharmaceutical data analytics and decision support. Unlike conventional approaches that treat analytics as an external component, the proposed framework embeds analytical intelligence directly into the core system architecture through an integrated, multi-layer design. The study adopts an experimental and system development methodology using a large-scale public pharmaceutical dataset consisting of 240,591 records and 10 attributes. Supervised machine learning models are implemented to support data classification and intelligence generation, and system performance is evaluated using accuracy, precision, recall, and F1-score. The results demonstrate that the proposed hybrid system consistently outperforms baseline and non-integrated approaches, achieving higher predictive stability and analytical consistency. The main contribution of this study lies in its system-level integration model, which enables the transformation of raw pharmaceutical data into actionable decision-support intelligence. The findings confirm that embedding analytics within information system architecture significantly enhances both analytical performance and decision-making capability in pharmaceutical information systems.
Co-Authors Abdur Rahman Adnan A.A Ahlun N Ahlun Nazar Ancolo Arif Alexander Bastian Aulia Hanifah Qomar Bambang Setia Kesuma Bastian, Arif Alexander Dita Septasari Dwi Feriyanto Dyah Fitri Mulati Eka Pratiwi Yunianti Ela Nurainisah Faricha Rizqi Feri Agustriyani Ferly Ardhy Fitra Endi Fernanda, Fitra Endi Fitri Mulati, Dyah Garda Arif Wicaksono Gustianing, Defy Ikna Awaliyani Inawati, Iin Kurniati, Desi Laksha, Ali Dwi M Fatkhu Arifin Mulati, Dyah Fitri Mulati, Dyah Fitri Ningsiah Nur Aminudin Ramil Abbasov ROMADHONA, ZULFA DIANA Sabila Dieni Hanifa, Eisha Shima Asadi Siti Saidah Tahta Herdian Andika