Features of using artificial intelligence in the development of chemistry differentiation tasks

Abstract

The integration of artificial intelligence (AI) technologies into education is viewed as an effective means of enhancing modern teaching practices. In chemistry lessons, the use of AI for developing differentiation tasks enables the individualization of the learning process by addressing students' personal learning needs.

The purpose of this study is to evaluate the effectiveness of AI tools in designing chemistry differentiation tasks, drawing on both Kazakhstani and international experience. The research applied methods of literature review, comparative analysis, and examination of the practical use of neural networks. The study explored the potential of AI tools such as Chad AI, Fusionbrain, and Craiyon for modeling chemical reactions, visualizing data, and supporting adaptive teaching approaches.
The findings revealed that the application of AI tools not only enhanced students' interest in chemistry but also improved the efficiency of differentiation tasks by approximately 20%. The advanced practices of Japan and the USA were identified as valuable models for adaptation within the Kazakhstani education system. Furthermore, AI was shown to enable the creation of personalized learning trajectories aligned with individual student abilities.

Overall, the research highlights the innovative role of AI in chemistry education, demonstrating that AI-supported methods simplify the design of differentiation tasks while fostering the development of analytical and critical thinking skills. The results provide practical recommendations for the effective implementation of AI in Kazakhstan’s education system.