An AI-mediated framework for recursive learning: transforming individual experiences into organizational knowledge and autonomous engagement in elderly care.

Introduction: The elderly care industry in Japan is currently facing significant challenges, including chronic labor shortages, high staff turnover, and low levels of IT utilization. Effective human resource development is crucial to address these issues. Methods: In this study, we propose an AI-supported method to facilitate both individual and organizational development in elderly care settings. This approach integrates Kolb's experiential learning theory and Huber's organizational learning framework. Staff members use digital tools to record their daily observations, which are then processed by an AI system. The AI organizes and visualizes the data to promote structured reflection and behavioral improvement. Results: The visualized data serve as a basis for personalized guidance provided by managers and senior staff, tailored to each employee's personality and situation. This process fosters cooperation and knowledge sharing within the organization. The demonstration study showed that AI-supported reflection enhanced organizational collaboration, improved work procedures, and increased staff's goal awareness and proactive behavior. Discussion: Furthermore, individual experiences were transformed into structured, purpose-oriented organizational knowledge through AI-driven analysis and were utilized as manuals. The method ensures cost efficiency and adaptability for small-scale care facilities through the use of digital infrastructure. It provides a practical and scalable solution to enhance care quality and workforce development.