Generative AI based on Large Language Models (LLMs) is advancing rapidly and becoming increasingly prevalent worldwide. This development has enabled the personalization of AI by integrating specific human knowledge, making it possible for AI to substitute certain work tasks. In collaborative endeavors between humans and AI, the continuous provision of evolving human knowledge to AI is essential, and it is equally important for humans to recognize and develop their thinking in response to changes in their knowledge. This study proposes a new knowledge transfer model, the Human-AI-Collaboration SECI (HAC-SECI) Model, predicated on the interaction between humans and AI. This model evolves the traditional framework of knowledge management by incorporating a dual-loop structure: the Inner Loop (Agent Growth Loop) and the Outer Loop (Target Development Loop). In the Inner Loop, humans (Targets) provide knowledge to AI (Agents), facilitating Agent growth. Conversely, the Outer Loop uses the knowledge accumulated in AI to enable humans (Targets) to recognize and develop their own knowledge. This paper examines a use case where an expert delegates the task of insight creation to AI, testing the functionality of the HAC-SECI Model's Inner Loop. The case study demonstrates the potential applicability of the HAC-SECI Model in creating a dynamic learning environment where AI, infused with human insights, enhances its capabilities, concurrently contributing to the cognitive development of human participants.