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Advances in artificial intelligence (AI) are enabling systems that augmentand collaborate with humans to perform simple, mechanistic tasks likescheduling meetings and grammar-checking text. However, such Human-AIcollaboration poses challenges for more complex, creative tasks, such ascarrying out empathic conversations, due to difficulties of AI systems inunderstanding complex human emotions and the open-ended nature of these tasks.Here, we focus on peer-to-peer mental health support, a setting in whichempathy is critical for success, and examine how AI can collaborate with humansto facilitate peer empathy during textual, online supportive conversations. Wedevelop Hailey, an AI-in-the-loop agent that provides just-in-time feedback tohelp participants who provide support (peer supporters) respond moreempathically to those seeking help (support seekers). We evaluate Hailey in anon-clinical randomized controlled trial with real-world peer supporters onTalkLife (N=300), a large online peer-to-peer support platform. We show thatour Human-AI collaboration approach leads to a 19.60% increase inconversational empathy between peers overall. Furthermore, we find a larger38.88% increase in empathy within the subsample of peer supporters whoself-identify as experiencing difficulty providing support. We systematicallyanalyze the Human-AI collaboration patterns and find that peer supporters areable to use the AI feedback both directly and indirectly without becomingoverly reliant on AI while reporting improved self-efficacy post-feedback. Ourfindings demonstrate the potential of feedback-driven, AI-in-the-loop writingsystems to empower humans in open-ended, social, creative tasks such asempathic conversations.

Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support