NSF Award
2326144
This research project is a response to the national shortage of mental health workers who are skilled in research-supported treatment protocols. The investigators seek to understand how recent innovations in artificial intelligence (AI) can effectively and ethically address and mitigate unmet demands for mental health treatment. Mental health workers include several related professions including clinical psychologists, social workers, and counselors. This undersized workforce is in dire need for scalable and effective upskilling in order to facilitate widespread and routine implementation of research-supported treatment protocols. Upskilling the workforce has been constrained because there are insufficient numbers of expert trainers to keep mental health workers proficient in the best available practices. This workforce has primarily relied on initial human-to-human training (e.g., graduate school) followed by relatively minimal follow-up observation and feedback throughout one’s career. As a result, millions of Americans with mental health conditions have restricted access to effective, research-supported care. The mental health workforce will benefit from technology that helps clinicians learn and sustain their use of research-supported treatment protocols. Important to this need, modern AI systems have developed to such a point where the technology can be considered a teammate in highly skilled work contexts, not simply a data processing tool. Integrating recent advancements in AI, the interdisciplinary team of investigators will develop an interactive AI system that can quickly evaluate a mental health worker’s performance with a patient, provide actionable feedback to the worker, and receive input from the worker so that feedback is based on what that individual worker needs to learn. This computational system, called the Trustworthy, Explainable, and Adaptive Monitoring Machine for AI Teams (TEAMMAIT), will function as an objective, nonjudgmental, and confidential colleague who can provide individualized feedback over a period of time. This type of Worker-AI Teaming has potential to transform the upskilling process by reducing the reliance on cost-prohibitive and scarcely available human-to-human training. While this project focuses on mental health work due to critical unmet demands, insights from this project can generalize to other healthcare and educational contexts.<br/><br/>This project brings together several disciplines including clinical psychology, industrial-organizational psychology, human-computer interaction, and information science. The team is structured to achieve multiple convergent goals. First, the investigators aim to better understand how introducing Worker-AI Teams will impact the expected competencies of mental health workers including how to collaborate with AI and respond to risks. Second, the investigators aim to gain insights regarding how to design AI Teammates in mental health work that facilitate ethical and effective Worker-AI Teaming. And third, the investigators aim to learn how to develop and deploy AI Teammates that can upskill the mental health workforce. A prototype of TEAMMAIT will be evaluated in diverse settings and with diverse workers and diverse patient populations. Data collected from prototype users will result in a set of development guidelines for Worker-AI Teaming in mental health work, as well as a set of generalizable ethical guidelines for developing and using these systems. Interviews with users will provide insights into how mental health workplaces can best prepare for Worker-AI Teaming and optimize its use while maintaining worker well-being and high-quality clinical care. The research plan will provide insights that will help make mental health worker upskilling more scalable and effective in real-world clinics, improving access to best practices for diverse patient populations across the United States. This project has been funded by the Future of Work at the Human-Technology Frontier cross-directorate program to promote deeper basic understanding of the interdependent human-technology partnership in work contexts by advancing the design of intelligent work technologies that operate in harmony with human workers.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
