The Role of Artificial Intelligence (AI) in Personalised Physics Education - Abdulayeva, Aigerim; Zhanatbekova, Nazym; Andasbayev, Yerlan; Khaimuldanov, Yerlan; Zhiyembayev, Zhomart (Jurnal Pendidikan IPA Indonesia, 2025)
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.