This article analyzes the capabilities of AI tools at various stages of preparing general physics lectures at a technical university – from constructing a logical structure and selecting applied examples to creating visual support and formulating introductory questions for knowledge reinforcement. Particular attention is paid to both the potential of neural network technologies for reducing instructor effort and their limitations associated with the need for additional verification of generated content. The study is based on the authors’ practical experience in teaching physics. The findings can be used in the development of methodological recommendations, digital educational environments, and in the daily practice of physics departments at technical universities. The article is intended for physics teachers and specialists in higher education methodology.
engineering education, artificial intelligence, neural network technologies, lecture preparation, general physics, physics teaching methods, multimedia support
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