DeepSeek Models in STEM Education: Capabilities, Applications, and Challenges

Abstract

The application of artificial intelligence (AI) in STEM education has introduced new capabilities for enhancing structured reasoning, mathematical problem-solving, and programming instruction. General-purpose large language models (LLMs) lack domain-specific precision and pedagogical alignment. This paper explores the applications of DeepSeek models (DeepSeek R1, DeepSeek Math, and DeepSeek Coder) in addressing these limitations by integrating structured logic, stepwise reasoning, and contextualized programming guidance. DeepSeek models support learning by offering adaptive feedback, iterative guidance, and human-like reasoning. This study evaluates their capabilities in improving educational outcomes while addressing key challenges such as overreliance, algorithmic bias, and skill fragmentation. Additionally, it discusses policies for responsible AI integration and explores future directions, including AIdriven personalization, collaborative AI-human instruction, and interdisciplinary applications.

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