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Utama, Agus Hadi
Universitas Lambung Mangkurat, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Integrating Generative AI within Deep Learning Frameworks to Transform Student Learning Dimensions: A Systematic Literature Review Susanti Sufyadi; Agus Hadi Utama; Adinda Sadilla; Muhammad Afriandy; Ahmad Syarif
Indonesian Journal of Instructional Media and Model Vol 8 No 1 (2026): Indonesian Journal of Instructional Media and Model
Publisher : Universitas Veteran Bangun Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/ijimm.v8i1.8076

Abstract

The rapid advancement of Artificial Intelligence (AI) in education has increased the necessity of understanding its role in supporting learning processes. Most current studies focus on teacher performance or instructional efficiency, while the relationship between AI and the student learning model remains under-explored. This study aims to analyze how the utilization of AI supports student learning models and identifies specific AI technologies, applications, or platforms that optimize these models. Through a Systematic Literature Review (SLR) protocol tracking 392 initial records down to 25 final articles from Scopus and SINTA (2020-2025) databases, this study provides an explicit methodological framework mapping AI's interaction with student agency. Theoretically, it shifts the educational discourse from teacher performance to student-centered autonomy, while practically offering design strategies for implementing collaborative AI tools within deep learning environments. Through a SLR method, two primary findings emerged. First, AI technologies such as Intelligent Tutoring Systems (ITS) and Large Language Models (LLM) significantly influence indicators of aspirations, learning to learn capabilities, and interpersonal relationships. Second, AI-based learning environments facilitate a more adaptive, personalized, and student-centered learning model by simultaneously integrating cognitive, social, and motivational dimensions. These findings confirm that AI is not merely an automation tool but a collaborative partner that strengthens student learning autonomy.
Co-Authors AA Sudharmawan, AA Abdul Rahman Abdul Rahman Adinda Sadilla Adrie Satrio Afriana, Atrin Afriandy, M Agus Salim Agus Salim Agus Salim Ahmad Syarif Alkahfi, Muhammad Indra Azmi, Muhammad Naufal Azmy, M. Noor Azumaradhi Adzra Brezto Asagi Dewantara Bunyamin Maftuh Dian Agus Ruchliyadi Dony Saputra Encep Syarief Nurdin, Encep Syarief Ermawati Ermawati Fatimah Fatimah Hamsi Mansur Hanun, Naila Hariadi Noor Ramadan Hidayat Nur Salim Kamilia Salsabila Khairil Azimi Kiki Fima Kartika Kurnia Kurnia Lazaro Kumala Dewi M. Padli Maimunah, Maimunah Mansur , Hamsi Mastur Mastur Mastur Mastur Mastur Mastur Maulidi, Muhammad Rezki Mawarni Eka Sari Monry Fraick Nicky Gillian Ratumbuysang Muhammad Afriandy Muhammad Assazili Muhammad Farhan Muhammad Ghiffari Muhammad Hasby Muhammad Zein Nur Rahman Prasetya, Adita Dwi Putri Ni'mah Azizah Qomario Qomario Qomario Qomario Qomario Rio Rafiudin Rafiudin Rezeky Nur Amalia Rica Rahmah Rifki Reza Pahlevi Rini, Sulistyo Rizki, Ahmad Naufal Rusminah Rusminah, Rusminah Sadila, Adinda Saputro, Ferry Cahyo Saropi, Nanda Sherly Merliana Az-Zahra Sukmawati Sukmawati Sulistyo Rini Susanti Sufyadi Virginia Fiba Stavanka Zaudah Cyly Arrum Dalu