Adaptive Learning Strategies in Engineering Graphics Education with Visual and Neural Technologies - Байдабеков, Ауез; Sindarov, Rakhmat (Рroblems of engineering and professional education, 2024)
Modern trends in technical education require revision of approaches to methodological support of graphic training of future engineers. In the conditions of rapid development of digital technologies and increased cognitive load on students, the traditional forms of teaching and learning materials (TLM) based mainly on printed manuals and static animation materials lose their effectiveness. This article discusses a new concept of designing teaching and learning complexes based on cognitive-oriented and adaptive approaches. Unlike classical models, the focus is on personalizing visual content, integrating artificial intelligence (AI), and providing interactive feedback, which allows taking into account individual characteristics of students' perception and level of training. The aim of the research is to identify the advantages of using adaptive digital platforms and neural network technologies in the process of graphic training of engineering students. The paper describes the stages of development of teaching and learning tools with the use of edtech tools (Neksbot, Stable Diffusion, GPT), as well as a comparative study of the effectiveness of such complexes in comparison with traditional teaching tools. The use of cognitive diagnostics methods (eye-tracking, EEG), questionnaires, analysis of learning achievements allowed to obtain a comprehensive assessment of the level of involvement, motivation and success of students. The results have shown that the use of flexibly customizable teaching aids, capable of adapting to the educational profile and cognitive preferences of the student, can significantly improve the quality of material perception, develop spatial thinking, and improve the skills of independent work. The obtained data indicate the need for a radical update of the methodological toolkit of teachers of graphic disciplines. The article is of interest for researchers and educators involved in the digital transformation of higher education in engineering, as well as developers of intelligent learning systems.