The Development of Individualized Assignment Generator
Rinata Raisovna Zaripova, Andrey Danilov, Leila Salekhova, Timur R. Fazliakhmetov · 2024
The article discusses the development of a system that uses artificial intelligence (AI) to generate individualized mathematics assignments for bilingual students in Tatarstan, Russia. The goal is to enhance learning by tailoring assignments to students’ linguistic preferences, cognitive styles, and knowledge levels. The system employs machine learning techniques and GPT-based models to create personalized tasks that align with curriculum goals while addressing linguistic diversity, particularly for Tatar-Russian bilinguals. The study evaluates several large language models (LLMs), including GPT-4, GPT-3.5 Turbo, YandexGPT, and GigaChat, based on their ability to generate math problems and content in the Tatar language. While GPT-4 and GPT-3.5 Turbo show superior performance in producing accurate and semantically correct problems, their proficiency in Tatar remains inconsistent. The research underscores the need for further development of LLMs to enhance content generation for bilingual educational contexts and highlights the potential of AI in advancing adaptive learning for mathematics education. Future directions include expanding the system’s functionality and testing its effectiveness across diverse educational settings.