DeepSeek in Education: Exploring the Transformative Potential of AI-Driven Educational Intelligence

Abstract

The integration of artificial intelligence into education remains challenged by issues of scalability, interpretability, and multimodal adaptability. DeepSeek's AI-driven educational tools show potential to improve educational applications through advances in reasoning efficiency, lightweight deployment, and multimodal fusion. This paper explores the DeepSeek model family and its transformative applications across five core educational scenarios, including learning, teaching, teaching research, management, and evaluation. Despite challenges such as excessive dependence on structured reasoning pathways, hallucination risks, sustainability of localized deployment, and limited ability to process multimodal data in real time, DeepSeek demonstrates significant potential in advancing equitable, intelligent, and human-centric educational ecosystems with ethical governance. Future development priorities include responsible AI frameworks and the shift of educational stakeholders' roles to cultivate equitable, adaptive educational ecosystems with the guidance of educational policies and further breakthroughs in technologies.