A Requirements Approach for Ecological Health Agentic AI: The Case of Digital Mentor for Occupational Therapists

The use of AI is increasingly adopted in knowledge-intensive domains. One such domain is healthcare, in which experience is imperative for the quality and fit of treatment in diverse contexts. In these professions, mentoring by experts is highly recommended for young professionals. However, these are often not available due to insufficient experienced professionals or limited accessibility to mentors in remote locations. This research in progress explores the potential of developing and integrating AI for mentoring services as part of the ecosystem of healthcare professionals. Specifically, we explore the development of an agentic AI requirements approach for facilitating such ecological solution and demonstrate it on the case of a digital mentor for occupational therapists. The proposed approach is aimed at providing a new frontier for enhancing clinical practice and decision-making, emphasizing the integration of ecological health principles. By systematically addressing multi-level influences, including intrapersonal, interpersonal, organizational, community, and public policy factors, this approach aims to provide requirements for a nuanced, context-sensitive system, tailored to the complexities of client care. The paper outlines a dual-adaptive agentic AI architecture, combining functional agents tasked with specific problem-solving and ecological health requirements agents that ensure adaptability, trustworthiness, and reflective engagement. A fictional case study is employed to illustrate the system's application, highlighting the interplay between individual attributes and broader environmental factors affecting performance. This illustration underscores the importance of ecological health understanding and outlines future research directions for refining the architecture of ecological health agentic AI systems, thereby improving outcomes for both therapists and clients.