NAZAROV A. PROBLEMS AND PROSPECTS OF USING GENERATIVE ARTIFICIAL INTELLIGENCE IN TEACHING PHYSICS TO IT BACHELOR'S DEGREE STUDENTS. LIFELONG EDUCATION: The 21st Century.
2026. Vol. 14. No. 1. DOI: 10.15393/j5.art.2026.11708


Vol. 14. No. 1.

Innovative approaches to lifelong learning
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PROBLEMS AND PROSPECTS OF USING GENERATIVE ARTIFICIAL INTELLIGENCE IN TEACHING PHYSICS TO IT BACHELOR'S DEGREE STUDENTS

NAZAROV Aleksei I.
Doctor of Pedagogical Sciences, Professor of General Physics Department
Petrozavodsk State University
(Petrozavodsk, Russian Federation)
anazarov@petrsu.ru
Keywords:
generative artificial intelligence
Top-IT education programs
personalization of physics teaching
SOLO taxonomy
critical thinking.		 Abstract: the article examines the potential of generative artificial intelligence (GAI) in education and substantiates the feasibility of its use in teaching physics to undergraduate students in information technology programs. The study proposes an adaptive educational strategy based on a comprehensive content analysis of scientific publications, comparative curriculum analysis, secondary analysis of large-scale surveys conducted among students and faculty at Russian universities, and the application of pedagogical design methods. Rather than promoting restrictive policies on GAI in teaching, the strategy emphasizes using GAI to foster students’ critical thinking and research independence. The findings suggest that intelligent systems are well-suited to integrating fundamental physics concepts with applied IT tasks. They can act as a virtual tutor, a source of multimodal content, a means of personalizing learning trajectories, etc. To enhance the effective use of GAI, the study introduces a set of physics problems with varying levels of complexity and diverse expected output formats. It also outlines verification methods, such as cross-checking responses across multiple GAI models, synchronous dialogue, visualization and modeling. The combined use of Bloom’s and SOLO taxonomies is recommended for assessing learning outcomes, as it enables evaluation of both the level of students’ cognitive activity and the depth of their conceptual understanding. The proposed solutions are aimed at developing students’ creative competencies related to critical thinking, the ability to interact effectively with intelligent systems, and readiness for lifelong learning.
Paper submitted on: 10/29/2025; Accepted on: 02/02/2026; Published online on: 03/26/2026.

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DOI: http://dx.doi.org/10.15393/j5.art.2026.11708