人工智能驱动隐性知识共享变革——以DeepSeek为例
AI-Driven Transformation of Tacit Knowledge Sharing—A Case Study of DeepSeek

目的：回应科学认知领域中隐性知识私有性加剧的群体认知差距问题，探索生成式人工智能破解知识传递壁垒的技术赋能路径。方法：聚焦推理链可视化核心机制，解析以DeepSeek为代表的生成式工具对个体创新思维的“解构–复用”逻辑；依托用户规模优势，阐释群体隐性认知向可学习思维范式转化的实现路径。结果：该技术通过暴露“最佳决策”推理黑箱、沉淀优质用户思维模板，有效压缩环境差异导致的认知–解题能力差，为跨群体思路共享提供技术中介。结论：生成式AI驱动的隐性知识显性化，重构知识民主化底层逻辑，为教育公平场景下的认知普惠提供突破性实践范式。
Objective: To address the cognitive gap aggravated by the privacy of tacit knowledge in scientific cognition, this study aims to explore the technical empowerment path of generative artificial intelligence (AI) in breaking knowledge transmission barriers. Method: Firstly, focusing on the core mechanism of reasoning chain visualization, it deciphers the “deconstruction-reuse” logic of generative tools (taking DeepSeek as a representative) for individuals’ innovative thinking. Then, leveraging the large-scale user advantage, it expounds the realization path of transforming group-based tacit cognition into shareable thinking paradigms. Results: This technology exposes the “best-decision” reasoning black box and precipitates high-quality users’ thinking templates, effectively narrowing the cognitive and problem-solving ability gap caused by environmental differences and providing a technical medium for cross-group thinking sharing. Conclusion: The explicitation of tacit knowledge driven by generative AI reconstructs the underlying logic of knowledge democratization, offering a breakthrough practical paradigm for cognitive inclusiveness in educational equity scenarios.