The Role of Artificial Intelligence (AI) in Personalised Physics Education
DOI:
https://doi.org/10.15294/jpii.v14i3.29752Keywords:
AI in education, personalized learning, physics education, adaptive learning systems, educational technologyAbstract
This study aimed to establish conceptual mechanisms and patterns for applying artificial intelligence (AI) technologies to personalised physics education within the Kazakhstani educational system. Methods. A comprehensive methodology was employed, combining both theoretical and empirical approaches. The theoretical phase involved reviewing regulatory documents in Kazakhstan’s education system. The empirical phase consisted of a pedagogical experiment conducted between September and December 2024 across three educational institutions in Taldykorgan, Kazakhstan. The study involved 58 tenth-grade students, divided into experimental and control groups; the experimental group utilised an AI-driven personalised learning system designed to adapt content based on student performance. Data were collected on academic performance, theoretical knowledge, practical skills, and research competencies using pre-tests, interim assessments, and final evaluations. Results. The experimental groups demonstrated a significant improvement in academic performance, with the average score increasing from 4.2 to 4.6. 76% of students in experimental groups successfully solved advanced problems, compared to 52% in control groups. The system fostered improved critical thinking, research competencies, and self-assessment skills, while enhancing students’ ability to engage in scientific discourse and apply knowledge in interdisciplinary contexts. Conclusions. The AI-driven model proved highly effective at personalising learning and improving students' academic and cognitive outcomes. It offers a scalable framework for adapting content to individual learning styles, with positive impacts on motivation and problem-solving abilities. The findings contribute to a growing body of research on AI applications in education and provide a foundation for further advancements in personal learning systems.
References
Ahmad, S. F., Rahmat, M. K., Mubarik, M. S., Alam, M. M., & Hyder, S. I. (2021). Artificial intelligence and its role in education. Sustainability, 13(22), 12902.
Ahmed, H. (2024). Institutional Integration of Artificial Intelligence in Higher Education: The Moderation Effect of Ethical Consideration. International Journal of Educational Reform.
Alarbi, K., Halaweh, M., Tairab, H., Alsalhi, N. R., Annamalai, N., & Aldarmaki, F. (2024). Making a revolution in physics learning in high schools with ChatGPT: A case study in UAE. Eurasia Journal of Mathematics, Science and Technology Education, 20(9), em2499.
Alawneh, Y. J. J., Sleema, H., Salman, F. N., Alshammat, M. F., Oteer, R. S., & ALrashidi, N. K. N. (2024). Adaptive learning systems: Revolutionizing higher education through AI-driven curricula. In: 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS) (pp. 799-804). RedHook: Curran Associates.
Allam, H., Dempere, J., Akre, V., Parakash, D., Mazher, N., & Ahamed, J. (2023). Artificial intelligence in education: Anargument of Chat-GPT use in education. In: 2023 9th International Conference on Information Technology Trends (ITT) (pp. 151-156). RedHook: CurranAssociates.
AlShaikh, F., & Hewahi, N. (2021). AI and machine learning techniques in the development of Intelligent Tutoring System: A review. In: 2021 International Conferenceon Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) (pp. 403-410). RedHook: Curran Associates.
Baizhanov, N. (2024). Artificial intelligence in educational testology: Application prospects and psychological aspects. Bulletin of Abai KazNPU. Series of Psychology, 79(2).
Bessas, N., Tzanaki, E., Vavougios, D., & Plagianakos, V. P. (2025). The role of ChatGPT in junior high school physics education: Insights from teachers and students and guidelines for optimal use. Social Sciences & Humanities, 11, 101610.
Bestyuk, A., & Pokhnatiuk, S. (2025). Integration of artificial intelligence into higher military education as a factor in increasing the efficiency of professional training. Scientific Bulletin of Mukachevo State University. Series “Pedagogy and Psychology”, 11(2), 60-71.
Bokshyts, O., & Kamenska, I. (2024). Pedagogical communication in practical training of future vocational education specialists: Process, implementations and outcomes. Professional Education: Methodology, Theory and Technologies, 10(1), 53-63.
Bursova, S., & Vashak, O. (2024). Innovative approaches to physical education of young children: Methods and practices. Ukrainian Professional Education, 8(1), 101-107. https://uaprofedu.com.ua/en/journals/tom-8-1-2024/innovatsiyni-pidkhodi-do-fizichnogo-vikhovannyaditey-rannogo-viku-metodi-ta-praktiki
Dahlkemper, M. N., Lahme, S. Z., & Klein, P. (2023). How do physics students evaluate artificial intelligence responses on comprehension questions? A study on the perceived scientific accuracy and linguistic quality of ChatGPT. Physical Review Physics Education Research, 19, 010142.
Das, A., Malaviya, S., & Singh, M. (2023). The impact of AI-driven personalization on learners’ performance. International Journal of Computer Sciences and Engineering, 11(8), 15-22.
Désy, C. (2023). A foray into the world of adaptive learning. College Pedagogy, 37(1), 36-43. https://www.calameo.com/aqpc/read/006737414b9004dc267ae
Diachuk, O. (2024). Adapting curricula to the requirements of the modern digital environment. Professional Education: Methodology, Theory and Technologies, 10(1), 10-21.
Dudar, V., Riznyk, V., Kotsur, V., & Nosachenko, V. (2025). Internet platforms in an open educational environment in the organisation of students' independent work. Humanities Studios: Pedagogy, Psychology, Philosophy, 13(1), 9-23.
Ejjami, R. (2024). The future of learning: AI-based curriculum development. International Journal for Multidisciplinary Research, 6(4), 1-31.
Ezzaim, A., Dahbi, A., Assad, N., & Haidine, A. (2022). AI-based adaptive learning – State of theart. In: J. Kacprzyk, M. Ezziyyani, V.E. Balas (Eds.), International Conference on Advanced Intelligent Systems for Sustainable Development Volume 1 –Advanced Intelligent Systems on Artificial Intelligence, Software, and Data Science (pp. 155-167). Cham: Springer.
Ezzaim, A., Dahbi, A., Haidine, A., & Aqqal, A. (2023). AI-based adaptive learning: A systematic mapping of the literature. Journal of Universal Computer Science, 29(10), 1161-1197.
Fang, Y., Roscoe, R. D., & McNamara, D. S. (2023). Artificial intelligence-based assessment in education. In: B. du Boulay, A. Mitrovic, K. Yacef (Eds.), Handbook of Artificial Intelligence in Education (pp. 485-504). Cheltenham: Edward Elgar Publishing Limited.
Galchynsky, L., Graivoronskyi, M., & Dmytrenko, O. (2021). Evaluation of Machine Learning Methods to Detect DoS / DDoS Attacks on IoT. Ceur Workshop Proceedings, 3241, 225-236. https://ceur-ws.org/Vol-3241/paper21.pdf
Gharahighehi, A., VanSchoors, R., Topali, P., & Ooge, J. (2024). Adaptive lifelong learning (all). In: A.M. Olney, I.A.Chounta, Z.Liu, O.C. Santos, I.I.Bittencourt (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky: 25th International Conference, AIED 2024, Recife, Brazil, July 8-12, 2024, Proceedings, Part II (pp. 452-459). Cham: Springer.
Hao, M., Wang, Y., & Peng, J. (2024). Empirical research on AI technology-supported precision teaching in high school science subjects. Applied Sciences, 14(17), 7544.
Heeg, D. M., & Avraamidou, L. (2023). The use of artificial intelligence in school science: A systematic literature review. Educational Media International, 60(2), 125-150.
Ibatov, M. K., Pak, Y. N., Zhetesova, G. S., & Pak, D. Y. (2021). Development of entrepreneurial university in the conditions of higher education modernization. Vysshee Obrazovanie V Rossii, 30(2), 154-168.
Ivanashko, O., Kozak, A., Knysh, T., & Honchar, K. (2024). The role of artificial intelligence in shaping the future of education: Opportunities and challenges. Futurity Education, 4(1), 126-146.
Järvis, M., Ivanenko, L., Antonenko, I., Semenenko, T., Virovere, A., & Barantsova, T. (2022). Application of the Integration Model in the System of Inclusive Education. Journal of Curriculum and Teaching, 11(1), 35-44.
Kortemeyer, G. (2023). Could an artificial-intelligence agent pass an introductory physics course? Physical Review Physics Education Research, 19(1).
Kossov, V. N., & Bauyrzhankyzy, T. M. (2023). Integration of modern technologies into physics teaching: Challenges and opportunities. In: S.V. Bondarenko, G.V. Degtyarev, N.A. Khilko, N.R. Ozherelieva, S.K. Saida, S.V. Klimov, V.I. Mikhailov (Eds.), XIX International Scientific and Practical Conference: “Science and Technology: Modernization, Innovation, Progress” Physical and Mathematical Sciences (pp. 20-26). Anapa: Publishing house OOO “NITS ESP”. https://innova-science.ru/wp-content/uploads/2023/12/sbornik-nauchnyh-trudov-28.11.2023-ntav-19.pdf#page=20
Kozhevnikova, A., & Kozhevnykov, P. (2024). Specifics of innovative educational environment and its influence on the development of future teachers’ innovative competence. Scientific Bulletin of Mukachevo State University. Series “Pedagogy and Psychology”, 10(2), 72-80.
Krokhmalnyi, R., Krokhmalna, H., Krokhmalnyi, D., & Kazymi, P. (2021). Information and terminological concepts of project actions in higher education domain. CEUR Workshop Proceedings, 2851, 381-390. https://ceur-ws.org/Vol-2851/paper35.pdf
Kumar, D., Haque, A., Mishra, K., Islam, F., Mishra, B.K., & Ahmad, S. (2023). Exploringthetransformativeroleofartificialintelligenceandmetaverseineducation: A comprehensive review. Metaverse Basic and Applied Research, 2, 55.
Kurni, M., Mohammed, M. S., & Srinivasa, K. G. (2023). Intelligent tutoring systems. In: M. Kurni, M. S. Mohammed, K. G. Srinivasa (Eds.),A Beginner’s Guide to Introduce Artificial Intelligence in Teaching and Learning (pp. 29-44). Cham: Springer.
Laak, K. J., & Aru, J. (2024). AI and personalized learning: Bridging the gap with modern educational goals.
Law of the Republic of Kazakhstan No. 319-III “On Education”. 2007.https://adilet.zan.kz/eng/docs/Z070000319_
Mahligawati, F., Allanas, E., Butarbutar, M. H., & Nordin, N. A. N. (2023). Artificial intelligence in physics education: A comprehensive literature review. Journal of Physics: Conference Series, 2596(1), 012080.
Mamadova, A. M., Novruzova, A. G., Huseynova, S. A., Nasirova, O. A., Azizova, R. S., & Aliyeva, M. L. (2019). Features of education financing in developing countries. Espacios, 40(26). https://www.revistaespacios.com/a19v40n26/a19v40n26p09.pdf
Nurakenova, A., & Nagymzhanova, K. (2024). A Study of Psychological Features Related to Creative Thinking Styles of University Students. Journal of Psycholinguistic Research, 53(1), 1.
Nurgaliyeva, S., Zhumabayeva, A., Kulgildinova, T., Abildina, S., Kurbonova, B., Umirbekova, A., & Bolatov, A. (2025). Evaluating student satisfaction of terminological apparatus with natural and mathematical textbooks in Kazakhstani schools. Cogent Education, 12(1), 2468563.
Order of the Acting Minister of Education of the Republic of Kazakhstan No. 500 “On Approval of the Professional Standard “Teacher’”. (2022). https://adilet.zan.kz/rus/docs/V2200031149
Order of the Minister of Education of the Republic of Kazakhstan No. 348 “On Approval of State Compulsory Standards for Preschool Education and Training, Primary, Basic Secondary and General Secondary, Technical and Vocational, Post-Secondary Education”. (2022). https://adilet.zan.kz/rus/docs/V2200029031
Pak, N. I., Khegay, L. B., Akkasynova, Z. K., Bidaibekov, Y. Y., & Kamalova, G. B. (2021). Preservice teacher training program for working with network mega-projects. Journal of Educators Online, 18(2).
Pardamean, B., Suparyanto, T., Cenggoro, T. W., Sudigyo, D., & Anugrahana, A. (2022). AI-based learning style prediction in online learning for primary education. IEEE Access, 10, 35725-35735.
Pradeep, K. R., Manish, A. S., Adithiyaa, A. S., Sahana, N., & Abhishek, S. T. (2024). Personalized adaptive learning platform empowered by artificial intelligence. In: 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS) (pp. 1504-1509). RedHook: CurranAssociates.
Qu, J., Zhao, Y., & Xie, Y. (2022). Artificial intelligence leads the reform of education models. Systems Research and Behavioral Science, 39(3), 581-588.
Rane, N., Choudhary, S., & Rane, J. (2023). Education 4.0 and 5.0: Integrating artificial intelligence (AI) for personalized and adaptive learning. Journal of Artificial Intelligence and Robotics, 1(1), 29-43.
Rudenko, O. (2025). Development and impact of stress on university students’ performance during distance learning. Scientia et Societus, 4(1), 15-25.
Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2024). Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. Information, 15(10), 596.
Sakhipov, A., Yermaganbetova, M., Latypov, R., & Ualiyev, N. (2022). Application of blockchain technology in higher education institutions. Journal of Theoretical and Applied Information Technology, 100(4), 1138-1147.
Shevchuk, L., & Hunaza, L. (2025). Analysis of international experience in implementing Artificial Intelligence in the educational process. Scientia et Societus, 4(1), 76-85.
Sirnoorkar, A., Zollman, D., Laverty, J. T., Magana, A. J., Rebello, N. S., & Bryan, L. A. (2024). Student and AI responses to physics problems examined through the lenses of sensemaking and mechanistic reasoning. Computers and Education: Artificial Intelligence, 7, 100318.
Smagulov, K., Bisenbayeva, L., & Ashirmatov, E. (2022). Pedagogical bases of professional training of future specialists in the conditions of modern education. Bulletin of the Jusup Balasagyn Kyrgyz National University, 14(3), 96-102.
Sushama, C., Arulprakash, P., Kumar, M. S., Ganesh, D., & Sujatha, K. (2022). The future of education: Artificial intelligence based remote learning. International Journal of Early Childhood Special Education, 14(3), 3827-3831.
Tekesbayeva, N., Oshanova, N., Zhunusova, L., & Anuarbekova, G. (2024). Innovative approaches to digitalization of education based on adaptive training technologies. Bulletin of Abai KazNPU. Series of Physical and Mathematical Sciences, 85(1), 296-304.
Trout, J. J., & Winterbottom, L. (2025). Artificial intelligence and undergraduate physics education. Physics Education, 60(1), 015024.
Tschisgale, P., Wulff, P., & Kubsch, M. (2023). Integrating artificial intelligence-based methods into qualitative research in physics education research: A case for computational grounded theory. Physical Review Physics Education Research, 21, 019901.
Usanova, L., Usanov, I., & Shtepa, O. (2024). Formation of critical thinking in the system of competence-based training of specialists. Ukrainian Professional Education, 8(2), 48-55.
Vakarou, G., Georgios, S., & Konstantinos, T. K. (2024). AI for Enhancing Physics Education: Practical Tools and Lesson Plans. The International Journal of Science, Mathematics and Technology Learning, 31(2), 159-176.
Vakulyk, I. (2025). Minimal effort: The key to effective learning and goal attainment. Humanities Studios: Pedagogy, Psychology, Philosophy, 13(1), 70-82.
Wu, T. T., Lee, H. Y., Wang, W. S., Lin, C. J., & Huang, Y. M. (2023). Leveraging computer vision for adaptive learning in STEM education: Effect of engagement and self-efficacy. International Journal of Educational Technology in Higher Education, 20(1), 53.
Yeadon, W., & Hardy, T. (2024). The impact of AI in physics education: a comprehensive review from GCSE to university levels. Physics Education, 59(2), 025010.
Yekollu, R. K., Ghuge, T. B., Biradar, S. S., Haldikar, S. V., & Kader, F. M. A. O. (2024). AI-driven personalized learning paths: Enhancing education through adaptive systems. In: R. Asokan, D.P. Ruiz, S.Piramuthu (Eds.), Smart Data Intelligence: Proceedings of ICSMDI 2024 (pp. 507-517). Singapore: SpringerNature.
Zanca, F., Hernandez-Giron, I., Avanzo, M., Guidi, G., Crijns, W., Diaz, O., Kagadis, G., Rampado, O., Lønne, P., Ken, S., Colgan, N., Zaidi, H., Zakaria, G., & Kortesniemi, M. (2021). Expanding the medical physicist curricular and professional programme to include Artificial Intelligence. Physica Medica, 83, 174-183.
Zarrabi, M., & Mohammadi, M. (2024). Reflective Teaching as an Agent of Reform in Teacher Education: Inhibitors, Motivators, and Tools. International Journal of Educational Reform, 33(4), 503-521.
