从“听课”到“探索”：基于 Deep Seek 支持的促进中学生深度学习投入的教学实践研究

Abstract

With the advancement of artificial intelligence technology, generative large models like DeepSeek are revolutionizing educational paradigms. By integrating hybrid expert architectures and interpretable chain-of-reasoning techniques, DeepSeek achieves efficient training and cognitive expression. Its "deep thinking mode" exhibits high compatibility with practical teaching scenarios, enabling students to systematically deconstruct and analyze problems— a feature increasingly integrated into classroom practices. However, high school history education remains trapped in traditional paradigms: instruction prioritizes knowledge transmission over conceptual understanding, while learning outcomes are often measured through rote memorization and standardized test preparation. Compounding these challenges is the sheer volume of textbook content, which overwhelms students’ self-directed learning and inquiry capabilities. Consequently, classrooms frequently default to passive knowledge reception and fragmented historical fact retention. This study investigates the transformation from traditional knowledge-based instruction to discipline-specific deep learning in history education. By designing curriculum practices centered on the development of students’ core competencies, we aim to.