Young people's perceptions and recommendations for conversational generative artificial intelligence in youth mental health

Adam Poulsen; Ian B. Hickie; Carla Gorban; Zsofi de Haan; William Capon; Ebenezer Eyeson-Annan; Jalal Radwan; Elizabeth M. Scott

cs.HCApr 15, 2026

Young people's perceptions and recommendations for conversational generative artificial intelligence in youth mental health

Authors: Adam Poulsen, Ian B. Hickie, Carla Gorban, Zsofi de Haan, William Capon, Ebenezer Eyeson-Annan, Jalal Radwan, Elizabeth M. Scott,

Organizations: Brain and Mind Centre, The University of Sydney. Sydney, Australia. · Uncapt. Sydney, Australia.

Abstract

Conversational generative artificial intelligence agents (or genAI chatbots) could benefit youth mental health, yet young people's perspectives remain underexplored. We examined the Mental health Intelligence Agent (Mia), a genAI chatbot originally designed for professionals in Australian youth services. Following co-design, 32 young people participated in online workshops exploring their perceptions of genAI chatbots in youth mental health and to develop recommendations for reconceptualising Mia for consumers and integrating it into services. Four themes were developed: (1) Humanising AI without dehumanising care, (2) I need to know what's under the hood, (3) Right tool, right place, right time?, and (4) Making it mine on safe ground. This study offers insights into young people's attitudes, needs, and requirements regarding genAI chatbots in youth mental health, with key implications for service integration. Additionally, by co-designing system requirements, this work informs the ethics, design, development, implementation, and governance of genAI chatbots in youth mental health contexts.

Mental Health Conversational Agents

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Generative Artificial Intelligence Chatbots for Motivational Interviewing: A Scoping Review From System Design to Intervention Outcomes
Sep 17, 2026Runze Hu, Jingqi Kong, Yang Yang +5Conversational Agents Generative AI Evaluation
Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, China · Institute for Visual Technology, School of Computer Science, Peking University, Beijing, China
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Aug 8, 2026Hannah Cha, Neha Shukla, Solon Barocas +3Human-in-the-Loop Evaluation AI Safety Evaluation
Microsoft Research · Duke University · Abridge AI Inc. +1
Understanding Parents' Desires in Moderating Children's Interactions with GenAI Chatbots through LLM-Generated Probes
Mar 4, 2026John Driscoll, Yulin Chen, Viki Shi +3Human-AI Interaction AI Safety
University of California, San Diego · San Diego State University · Johns Hopkins University

Sep 17, 2026J/K move · Enter open · S save

Generative Artificial Intelligence Chatbots for Motivational Interviewing: A Scoping Review From System Design to Intervention Outcomes

Runze Hu, Jingqi Kong, Yang Yang, Yihang Yang+4

Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, China · Institute for Visual Technology, School of Computer Science, Peking University, Beijing, China

Conversational Agents Generative AI Evaluation Healthcare

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Motivational interviewing (MI) is a collaborative approach to elicit autonomous motivation for health behavior change. Generative AI (GenAI) offers new ways to deliver MI via conversational systems, but evidence on their design, assessment, and translation into interventions remains fragmented. This scoping review characterized evidence on GenAI-MI chatbots across system design, safety, MI quality, user perceptions, and intervention outcomes. We conducted a PRISMA-ScR scoping review. Nine datasets were searched for studies published or publicly available from January 1, 2015 to June 2, 2026 that used GenAI to generate MI chatbot responses or counselor utterances. Data were extracted using a predefined framework and synthesized descriptively. Forty-seven reports (48 studies) were included. Twenty (41.7%) focused on system design without direct participant use; 28 (58.3%) involved direct interaction. Most systems were text based and disembodied; 23 (47.9%) incorporated dynamic adaptation. Safety measures were unevenly reported. Among studies with direct use, 21/28 (75.0%) reported informed consent or user education. Thirty (62.5%) assessed MI quality, generally suggesting MI-consistent interactions. User perceptions were favorable, especially empathy, usability, helpfulness, and intention to use, though measures were heterogeneous. Eighteen (37.5%) reported intervention outcomes, mostly after a single session. Positive findings were more consistent for short-term motivation than sustained behavioral or functional change. GenAI-MI chatbots can deliver MI-consistent interactions perceived favorably, but evidence for sustained behavioral or functional change is limited. Future research should strengthen runtime safety monitoring, standardize MI quality assessment, and use longer-term comparative designs with behavioral and functional outcomes.

Abstract

Mental Health Conversational Agents

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