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Intan Kurniawati
Pendidikan IPA, Fakultas Matematika dan Ilmu Pengetahuan Alam, Semarang, 50229, Indonesia
Religion Humanities Education Library & Information Science Other

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Trends and Impacts of Artificial Intelligence-Assisted Deep Learning Approaches in Science Education: A Systematic Literature Review Intan Kurniawati; Arif Widyatmoko; Novi Ratna Dewi; Dyah Rini Indriyanti
Journal of Educational Sciences Vol. 10 No. 6 (2026): Journal of Educational Sciences
Publisher : FKIP - Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jes.10.6.p.177-187

Abstract

Rapid advances in Artificial Intelligence (AI) technology have transformed educational practices, including Natural Sciences learning. The integration of AI in deep learning approaches provides opportunities to enhance learning quality by strengthening student’s scientific literacy, digital literacy, and critical thinking skills. This study aims to examine the trends and impacts of AI implementation in deep learning approaches to science education using the Systematic Literature Review (SLR) method. A total of 10 articles published between 2020 and 2026 from Scopus, Google Scholar, and Springer were analyzed through identification, selection, and thematic synthesis. The results reveal four main themes: (1) AI contributes to improving science literacy and conceptual understanding; (2) AI integration strengthens digital literacy and 21st-century skills such as critical thinking and problem-solving; (3) AI enables more adaptive and inclusive learning through personalization features; and (4) several challenges remain, including teacher’s pedagogical readiness, infrastructure limitations, and the need to improve student’s digital literacy. Overall, the findings indicate that integrating AI into deep learning approaches has great potential to improve the quality of science education.
Co-Authors Arif Widyatmoko Dyah Rini Indriyanti Novi Ratna Dewi