AI Use, Disclosure, & Training Across School-Based & Related Service Professionals in Ohio

Artificial intelligence (AI) is increasingly shaping professional work across healthcare and education, with implications for service delivery, documentation, and professional governance. Advances in generative AI and machine learning have expanded access to tools that support writing, summarization, information retrieval, and administrative workflows. However, these practices have emerged largely without formal institutional oversight (Lockwood, Shergill et al., 2025; Farmer et al., 2026; Microsoft & LinkedIn, 2024). Although early discussions of AI in healthcare emphasized high-risk applications such as diagnosis or treatment planning, available empirical evidence suggests that current practitioner uptake is more commonly concentrated in lower-visibility but high-frequency activities, including documentation support, report writing, and content generation, rather than direct clinical decision-making (American Psychological Association [APA], 2024; Microsoft & LinkedIn, 2024). While the use of AI in non-clinical applications may reduce certain risks, such as bias or error in diagnostic decision-making (Chustecki, 2024), AI use in these contexts nevertheless raises important questions about transparency, informed consent, and professional accountability within each discipline. These issues have direct implications for shared governance in interprofessional environments, where client care is shaped by professional outputs that cross disciplinary boundaries (Hoying & Allen, 2011).