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Role-play exercises are widely utilized for training across a variety of domains; however, they have many shortcomings, including low availability, resource intensity, and lack of diversity. Large language model-driven virtual agents offer a potential avenue to mitigate these limitations and offer lower-risk role-play. The implications, however, of shifting this human-human collaboration to human-agent collaboration are still largely unexplored. In this work we focus on the context of psychotherapy, as psychotherapists-in-training extensively engage in role-play exercises with peers and/or supervisors to practice the interpersonal and therapeutic skills required for effective treatment. We provide a case study of a realistic ''virtual patient'' system for mental health training, evaluated by trained psychotherapists in comparison to their previous experiences with both real role-play partners and real patients. Our qualitative, reflexive analysis generated three themes and thirteen subthemes regarding key interpersonal skills of psychotherapy, the utility of the system compared to traditional role-play techniques, and factors which impacted psychotherapist-perceived ''humanness'' of the virtual patient. Although psychotherapists were optimistic about the system's potential to bolster therapeutic skills, this utility was impacted by the extent to which the virtual patient was perceived as human-like. We leverage the Computers Are Social Actors framework to discuss human-virtual-patient collaboration for practicing rapport, and discuss challenges of prototyping novel human-AI systems for clinical contexts which require a high degree of unpredictability. We pull from the ''SEEK'' three-factor theory of anthropomorphism to stress the importance of adequately representing a variety of cultural communities within mental health AI systems, in alignment with decolonial computing. 

We present a user-centric validation of a teleneurology platform, assessing its effectiveness in conveying screening information, facilitating user queries, and offering resources to enhance user empowerment. This validation process is implemented in the setting of Parkinson's disease (PD), in collaboration with a neurology department of a major medical center in the USA. Our intention is that with this platform, anyone globally with a webcam and microphone-equipped computer can carry out a series of speech, motor, and facial mimicry tasks. Our validation method demonstrates to users a mock PD risk assessment and provides access to relevant resources, including a chatbot driven by GPT, locations of local neurologists, and actionable and scientifically-backed PD prevention and management recommendations. We share findings from 91 participants (48 with PD, 43 without) aimed at evaluating the user experience and collecting feedback. Our framework was rated positively by 80.85% (standard deviation ± 8.92%) of the participants, and it achieved an above-average 70.42 (standard deviation ± 13.85) System-Usability-Scale (SUS) score. We also conducted a thematic analysis of open-ended feedback to further inform our future work. When given the option to ask any questions to the chatbot, participants typically asked for information about neurologists, screening results, and the community support group. We also provide a roadmap of how the knowledge generated in this paper can be generalized to screening frameworks for other diseases through designing appropriate recording environments, appropriate tasks, and tailored user-interfaces.