Behavioral health conditions, which include mental health and substance use disorders, are the leading disease burden in the United States. Peer-run behavioral health organizations (PROs) critically assist individuals facing these conditions by combining mental health services with assistance for needs such as income, employment, and housing. However, limited funds and staffing make it difficult for PROs to address all service user needs. To assist peer providers at PROs with their day-to-day tasks, we introduce PeerCoPilot, a large language model (LLM)-powered assistant that helps peer providers create wellness plans, construct step-by-step goals, and locate organizational resources to support these goals. PeerCoPilot ensures information reliability through a retrieval-augmented generation pipeline backed by a large database of over 1,300 vetted resources. We conducted human evaluations with 15 peer providers and 6 service users and found that over 90% of users supported using PeerCoPilot. Moreover, we demonstrate that PeerCoPilot provides more reliable and specific information than a baseline LLM. PeerCoPilot is now used by a group of 5-10 peer providers at CSPNJ, a large behavioral health organization serving over 10,000 service users, and we are actively expanding PeerCoPilot's use.

Artificial intelligence (AI) systems are increasingly prevalent in youths’ lives, despite documentation and concern of potential algorithmic harm. With responsible AI (RAI) efforts aiming to address harms, youth are largely overlooked as contributors, despite being stakeholders of AI. This paper explores perspectives on challenges, barriers, and opportunities of youth participation in responsibly creating AI. Through workshops with 16 teens and 11 parents, as well as interviews with 8 AI practitioners, we find that while all groups recognize the value of youth perspectives, the opinions of those not directly paying for AI are not prioritized. Many parents were optimistic about their youths’ ability to contribute, while youth showed a desire to contribute, coupled with an awareness of their strengths and limitations. Practitioners saw the potential of youth to contribute but not necessarily as empowered decision makers in RAI processes. We offer insights on involving youth in RAI, balancing protection with agency.

Peer-run organizations (PROs) provide critical, recovery-based behavioral health support rooted in lived experience. As large language models (LLMs) enter this domain, their scale, conversationality, and opacity introduce new challenges for situatedness, trust, and autonomy. Partnering with Collaborative Support Programs of New Jersey (CSPNJ), a statewide PRO in the Northeastern United States, we used comicboarding, a co-design method, to conduct workshops with 16 peer specialists and 10 service users exploring perceptions of integrating an LLM-based recommendation system into peer support. Findings show that depending on how LLMs are introduced, constrained, and co-used, they can reconfigure in-room dynamics by sustaining, undermining, or amplifying the relational authority that grounds peer support. We identify opportunities, risks, and mitigation strategies across three tensions: bridging scale and locality, protecting trust and relational dynamics, and preserving peer autonomy amid efficiency gains. We contribute design implications that center lived-experience-in-the-loop, reframe trust as co-constructed, and position LLMs not as clinical tools but as relational collaborators in high-stakes, community-led care.

Around 90% of CEOs see AI as the “most critical technology for ensuring future profitability and competitiveness.” At the same time, up to 95% of AI projects fail. Currently, little is known about the leaders who approve and guide AI initiatives. We call them AI Deciders. This study investigates how AI Deciders reason about AI benefits and risks, and how their knowledge about AI influences their decisions on what and where to innovate. We interviewed AI Deciders across diverse organizations. We found no ideation. AI Deciders just consider one concept at a time. Design and HCI played no role in deciding what to build. Many AI Deciders overestimate AI’s benefits while underestimating risks. Based on these findings, we identified opportunities for design and HCI to support impactful and responsible AI innovation. This should reduce AI project failure.

Generative AI has enabled novice designers to quickly create professional-looking visual representations for product concepts. However, novices have limited domain knowledge that could constrain their ability to write prompts that effectively explore a product design space. To understand how experts explore and communicate about design spaces, we conducted a formative study with 12 experienced product designers and found that experts -- and their less-versed clients -- often use visual references to guide co-design discussions rather than written descriptions. These insights inspired DesignWeaver, an interface that helps novices generate prompts for a text-to-image model by surfacing key product design dimensions from generated images into a palette for quick selection. In a study with 52 novices, DesignWeaver enabled participants to craft longer prompts with more domain-specific vocabularies, resulting in more diverse, innovative product designs. However, the nuanced prompts heightened participants' expectations beyond what current text-to-image models could deliver. We discuss implications for AI-based product design support tools.

Personalized problem selection enhances student practice in tutoring systems.Prior research has focused on transparent problem selection that supports learner control but rarely engages learners in selecting practice materials.We explored how different levels of control (i.e., full AI control, shared control, and full learner control), combined with showing learning analytics on skill mastery and visual what-if explanations, can support students in practice contexts requiring high degrees of self-regulation, such as homework.Semistructured interviews with six middle school students revealed three key insights: (1) participants highly valued learner control for an enhanced learning experience and better self-regulation, especially because most wanted to avoid losses in skill mastery;(2) only seeing their skill mastery estimates often made participants base problem selection on their weaknesses; and (3) what-if explanations stimulated participants to focus more on their strengths and improve skills until they were mastered.These findings show how explainable learning analytics could shape students' selection strategies when they have control over what to practice.They suggest promising avenues for helping students learn to regulate their effort, motivation, and goals during practice with tutoring systems.