INTEGRATING ARTIFICIAL INTELLIGENCE INTO PHYSICS EDUCATION AT PROFESSIONAL PRE-HIGHER EDUCATION INSTITUTIONS

Abstract

The article provides a theoretical analysis of integrating artificial intelligence into physics education at professional pre-higher education institutions. The relevance of the problem is linked to differences in students’ prior preparation, the need for personalized pedagogical support, and the necessity of preserving evidence-based physical reasoning amid the growing use of generative and analytical digital tools.

The study aims to theoretically substantiate a pedagogically appropriate model for integrating artificial intelligence into physics education through adaptive diagnostics, individualized support, and mandatory verification of results.

The research procedure combined theoretical analysis and generalization, systematization of artificial intelligence functions at different stages of physics learning, and pedagogical modelling of diagnostic, computational, laboratory, and assessment scenarios.

Results. Four interconnected areas of application were identified: comprehensive diagnosis of learning difficulties and development of a dynamic course mastery profile; scaffolded support for solving physics problems; visualization and modelling of computational experiments; and intelligent analysis of laboratory data. Adaptive assistance should follow the principle of minimum necessary support, while decisions concerning task complexity, assessment criteria, and interpretation of outcomes must remain under the teacher’s professional responsibility.

Conclusions. The study concludes that the educational value of artificial intelligence lies not in automatically producing answers, but in structuring a sequence of learning actions: prediction, model selection, calculation or measurement, verification, and reasoned explanation. Effective integration requires transparent rules of use, data protection, equitable access, teachers’ methodological readiness, and the preservation of student autonomy.