What are the application scenarios and challenges of domestic large models in the field of education?

When domestic large models are applied in the field of education, common scenarios include personalized learning support and intelligent teaching assistance, while facing core challenges such as data security and content accuracy. In terms of application scenarios: - Personalized learning: Generate learning paths adapted to cognitive levels based on students' answer data, learning duration, etc., such as intensive practice on weak knowledge points. - Intelligent tutoring: Respond to subject-related questions 24/7, provide problem-solving ideas or concept explanations to assist independent after-school learning. - Resource generation: Quickly create teaching plans, mock test questions, or popular science materials, reducing teachers' lesson preparation time costs. Challenges mainly focus on: - Data security: Students' learning data involves privacy, requiring the establishment of compliant data collection and usage mechanisms. - Content accuracy: Large models may generate incorrect knowledge, requiring manual verification to ensure the rigor of teaching content. - Educational equity: Uneven distribution of technical resources may exacerbate regional or inter-school educational gaps. When promoting applications, it is recommended to prioritize pilot projects in subjects with high standardization (such as mathematics and languages), optimize content quality through teacher supervision, and pay attention to the issue of resource balance in technology popularization.