Didactic Complexity Assessment Physical Devices with AI

The problem of assessing the didactic complexity of physical devices studied at school and university is considered. Its relevance is due to the need to optimize the educational process, taking into account the limited bandwidth of the working memory of students in the framework of the Cognitive Load Theory. The purpose of the article is to identify the main components of the didactic complexity of physical instruments and devices, to develop a methodology for its quantitative assessment using artificial intelligence and its testing at 50 objects. As a methodological basis, the provisions of system theory, qualimetry, complexity theory and methodology of soft systems are used. The study relies on educational, methodological and scientific literature in physics, which considers various options for explaining the structure and functioning of physical instruments. Pairwise comparison is carried out using the Qwen neural network. General scientific methods of analysis and synthesis, qualitative and quantitative modeling (in Excel spreadsheets), formalization and abstraction methods, and a graph construction method are used. Three main factors affecting the didactic complexity of the device have been identified: 1) difficulty in explaining the principle of action; 2) availability of monitoring of its functioning; 3) structural and operational complexity. After paired comparisons of 25 instruments, a normalized didactic complexity scale (from 0 to 1) was obtained. Using this scale, another 50 devices were evaluated. A technique has been developed for using neural networks for a reliable assessment of the complexity of objects using the pairwise comparison method. The results of quantitative ranking of devices by complexity can be used to optimize the training methodology.