Artificial intelligence in physics education: A systematic review of content coverage, implementation models, learning impact, and pedagogical challenges

Roziqin Roziqin; Achmad Samsudin; Duden Saepuzaman; Haslinda Nawang Sari; Mimin Iryanti; Roziqin Roziqin; Achmad Samsudin; Duden Saepuzaman; Haslinda Nawang Sari; Mimin Iryanti

doi:10.3934/steme.2026023

STEM Education

2026, Volume 6, Issue 4: 539-583. doi: 10.3934/steme.2026023

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Artificial intelligence in physics education: A systematic review of content coverage, implementation models, learning impact, and pedagogical challenges

1.
Program of Physics Education, Faculty of Mathematics and Natural Sciences Education, Universitas Pendidikan Indonesia, 40154 Bandung, Indonesia; roziqin@upi.edu, achmadsamsudin@upi.edu, dsaepuzaman@upi.edu, mien_iryanti@upi.edu
2.
Department of Chemistry Education, Universitas Negeri Yogyakarta, 55281 Yogyakarta, Indonesia; haslindanawang.2025@student.uny.ac.id

Academic Editor: Feng-Kuang Chiang

Received: 04 February 2026 Revised: 11 May 2026 Accepted: 15 May 2026 Published: 04 June 2026

Artificial intelligence (AI) has made a fast entry into the world of physics education research, but the empirical literature is still fragmented according to content domain, pedagogical function, and evaluation criteria. Leveraging disciplinary epistemic practices, instructional scaffolding theory, and self-regulated learning, this is a systematic literature review of 44 empirical studies published from 2021 to 2025 to examine the implementation of AI in physics education, where it is concentrated, the learning functions it serves, and the epistemic risks associated with its use. Following the guidelines of PRISMA, the review combines frequency analysis and thematic synthesis in order to search for patterns beyond isolated outcomes across studies. The results show that there is a high concentration of AI applications in the areas of mechanics, thermodynamics, and modern physics, which are characterized by machine-interpretable problem structures and high demands for abstraction. AI is mainly applied in conversational tutoring, automated feedback, and personalization systems, and the benefits have been shown to be focused on short-term learning performance, engagement, and instructional efficiency. However, common themes among the contexts are observed, such as epistemic unreliability, overreliance on AI outputs, lack of AI literacy, and fragility of methodology, which directly pose a threat to disciplinary reasoning practices that are central to learning physics. Far from reflecting pedagogical transformation, the current use of AI appears from the synthesized evidence to be instrumented instructional support, frequently divorced from explicit theory-driven learning design. This review introduces a theory-informed, integrative, and explanatory framework for relationships between characteristics of physics content, AI pedagogical functions, and epistemic risks to provide a basis for future research to go beyond tool-centered evaluations, toward sustainable, discipline-sensitive AI integration in physics education.
- artificial intelligence,
- physics education,
- systematic literature review,
- learning outcomes,
- AI-based instruction,
- pedagogical challenges
Citation: Roziqin Roziqin, Achmad Samsudin, Duden Saepuzaman, Haslinda Nawang Sari, Mimin Iryanti. Artificial intelligence in physics education: A systematic review of content coverage, implementation models, learning impact, and pedagogical challenges[J]. STEM Education, 2026, 6(4): 539-583. doi: 10.3934/steme.2026023

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Abstract

Artificial intelligence (AI) has made a fast entry into the world of physics education research, but the empirical literature is still fragmented according to content domain, pedagogical function, and evaluation criteria. Leveraging disciplinary epistemic practices, instructional scaffolding theory, and self-regulated learning, this is a systematic literature review of 44 empirical studies published from 2021 to 2025 to examine the implementation of AI in physics education, where it is concentrated, the learning functions it serves, and the epistemic risks associated with its use. Following the guidelines of PRISMA, the review combines frequency analysis and thematic synthesis in order to search for patterns beyond isolated outcomes across studies. The results show that there is a high concentration of AI applications in the areas of mechanics, thermodynamics, and modern physics, which are characterized by machine-interpretable problem structures and high demands for abstraction. AI is mainly applied in conversational tutoring, automated feedback, and personalization systems, and the benefits have been shown to be focused on short-term learning performance, engagement, and instructional efficiency. However, common themes among the contexts are observed, such as epistemic unreliability, overreliance on AI outputs, lack of AI literacy, and fragility of methodology, which directly pose a threat to disciplinary reasoning practices that are central to learning physics. Far from reflecting pedagogical transformation, the current use of AI appears from the synthesized evidence to be instrumented instructional support, frequently divorced from explicit theory-driven learning design. This review introduces a theory-informed, integrative, and explanatory framework for relationships between characteristics of physics content, AI pedagogical functions, and epistemic risks to provide a basis for future research to go beyond tool-centered evaluations, toward sustainable, discipline-sensitive AI integration in physics education.

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Author's biography Roziqin is a master's student affiliated with the Department of Physics Education, Faculty of Mathematics and Natural Sciences Education, Universitas Pendidikan Indonesia, Bandung, Indonesia. His academic interests focus on physics education and the development of innovative learning approaches and instructional media; Dr. Achmad Samsudin is an Associate Professor at Universitas Pendidikan Indonesia and currently serves as Head of the Bachelor and Master of Physics Education Study Program. His research focuses on physics education, conceptual change, misconceptions, cognitive psychology in physics learning, and science education. He has an extensive publication record and is widely recognized for his contributions to physics education research in Indonesia; Dr. Duden Saepuzaman is a senior lecturer in the Physics Education Program at Universitas Pendidikan Indonesia. He holds a doctoral degree and conducts research in physics education, especially in assessment, misconceptions, meta-analysis, and earth physics/geophysics. His academic work reflects sustained engagement in improving physics learning and assessment practices in higher education and school contexts; Haslinda Nawang Sari is a master's student affiliated with the Department of Chemistry Education, Faculty of Mathematics and Natural Sciences, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia. Her academic interests focus on chemistry education and the development of innovative learning resources, particularly in green chemistry, sustainable development, and instructional media; Dr. Mimin Iryanti is a physics lecturer at Universitas Pendidikan Indonesia with an academic profile in Earth sciences and environmental studies. Her ORCID record lists her employment at Universitas Pendidikan Indonesia since 2002, and her public scholar profile highlights research activity in Earth sciences and the environment. She is also publicly listed in association with the Physics Study Program at UPI

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