Technical Implementation of Large Language Models in Educational Scenarios: A Case Study of DeepSeek

Abstract

Large Language Models (LLMs) present transformative potential for education, yet their practical deployment faces persistent challenges in domain knowledge adaptation, dynamic interaction design, and ethics-compliance. This paper proposes and validates a pedagogical principle-driven framework for implementing the general-purpose LLM DeepSeek in K-12 to tertiary educational scenarios. Through a mixed-methods approach (technical benchmarking + empirical field trials), we demonstrate that DeepSeek’s three-core strategy.
(1) curriculum-grounded knowledge graph augmentation, 
(2) pedagogically aligned multimodal architecture, and 
(3) collaborative teacher-in-the-loop refinement—effectively resolves critical conflicts between educational causality and AI stochasticity. Furthermore, we systematize domain-specific technical requirements, including: 
Cross-modal alignment of symbolic-natural language systems (e.g., mathematical formalization), Sub-second dynamic feedback efficiency (<300ms latency),  Federated learning solutions mitigating data privacy risks (7.2% utility loss vs. 39.2% baseline).Empirical studies across 42 institutions confirm that the optimized framework elevates:STEM problem-solving accuracy to >90% (Δ+21.8% vs. generic models), Student knowledge retention by 22.4% (p<0.001), Teacher adoption rates to 89% (SUS score). 
This work provides a transferable paradigm for human-centered, ethically grounded LLM deployment in global education ecosystems.