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

arXiv: https://arxiv.org/abs/2203.15144 · HTML: https://mlsanity.com/paper/2203.15144/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 92 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [MIME: MIMicking Emotions for Empathetic Response Generation](https://mlsanity.com/paper/2010.01454/) (2020)
- [MoEL: Mixture of Empathetic Listeners](https://mlsanity.com/paper/1908.07687/) (2019)
- [Sparks: Inspiration for Science Writing using Language Models](https://mlsanity.com/paper/2110.07640/) (2021)
- [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021)
- [The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writers](https://mlsanity.com/paper/2101.09157/) (2021)
- [CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation](https://mlsanity.com/paper/2105.08316/) (2021)
- [Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication](https://mlsanity.com/paper/2106.11791/) (2021)
- [Supporting Cognitive and Emotional Empathic Writing of Students](https://mlsanity.com/paper/2105.14815/) (2021)
- [Artificial Intelligence and the Future of Psychiatry: Insights from a Global Physician Survey](https://mlsanity.com/paper/1907.12386/) (2019)
- [Artificial Intelligence and the Future of Psychiatry: Qualitative Findings from a Global Physician Survey](https://mlsanity.com/paper/1910.09956/) (2019)

## Built on by (most cited first, 30 of 68)

- [A Roadmap to Pluralistic Alignment](https://mlsanity.com/paper/2402.05070/) (2024)
- [Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction](https://mlsanity.com/paper/2305.02466/) (2023)
- [Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers](https://mlsanity.com/paper/2504.18412/) (2025)
- [Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data](https://mlsanity.com/paper/2307.14385/) (2023)
- [A Computational Framework for Behavioral Assessment of LLM Therapists](https://mlsanity.com/paper/2401.00820/) (2024)
- [Rehearsal: Simulating Conflict to Teach Conflict Resolution](https://mlsanity.com/paper/2309.12309/) (2023)
- [The opportunities and risks of large language models in mental health](https://mlsanity.com/paper/2403.14814/) (2024)
- [Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant](https://mlsanity.com/paper/2502.01390/) (2025)
- [Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring](https://mlsanity.com/paper/2310.15461/) (2023)
- [MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journaling](https://mlsanity.com/paper/2310.05231/) (2023)
- [Benefits and Harms of Large Language Models in Digital Mental Health](https://mlsanity.com/paper/2311.14693/) (2023)
- [AnthroScore: A Computational Linguistic Measure of Anthropomorphism](https://mlsanity.com/paper/2402.02056/) (2024)
- [Risks from Language Models for Automated Mental Healthcare: Ethics and Structure for Implementation](https://mlsanity.com/paper/2406.11852/) (2024)
- [Evaluating Human-AI Collaboration: A Review and Methodological Framework](https://mlsanity.com/paper/2407.19098/) (2024)
- [SciSciGPT: Advancing Human-AI Collaboration in the Science of Science](https://mlsanity.com/paper/2504.05559/) (2025)
- [Helping the Helper: Supporting Peer Counselors via AI-Empowered Practice and Feedback](https://mlsanity.com/paper/2305.08982/) (2023)
- [When Large Language Models are Reliable for Judging Empathic Communication](https://mlsanity.com/paper/2506.10150/) (2025)
- [Empathic Conversations: A Multi-level Dataset of Contextualized Conversations](https://mlsanity.com/paper/2205.12698/) (2022)
- [Transforming Wearable Data into Personal Health Insights using Large Language Model Agents](https://mlsanity.com/paper/2406.06464/) (2024)
- [Human Decision-making is Susceptible to AI-driven Manipulation](https://mlsanity.com/paper/2502.07663/) (2025)
- [Gendered Mental Health Stigma in Masked Language Models](https://mlsanity.com/paper/2210.15144/) (2022)
- [Challenges of Large Language Models for Mental Health Counseling](https://mlsanity.com/paper/2311.13857/) (2023)
- [Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Review](https://mlsanity.com/paper/2401.01519/) (2024)
- [The Role of AI in Peer Support for Young People: A Study of Preferences for Human- and AI-Generated Responses](https://mlsanity.com/paper/2405.02711/) (2024)
- [Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided](https://mlsanity.com/paper/2404.01288/) (2024)
- [Human-like Affective Cognition in Foundation Models](https://mlsanity.com/paper/2409.11733/) (2024)
- [The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and Millennial Generation teachers?](https://mlsanity.com/paper/2305.02878/) (2023)
- [InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training](https://mlsanity.com/paper/2510.15859/) (2025)
- [MimeQA: Towards Socially-Intelligent Nonverbal Foundation Models](https://mlsanity.com/paper/2502.16671/) (2025)
- [Seeing Seeds Beyond Weeds: Green Teaming Generative AI for Beneficial Uses](https://mlsanity.com/paper/2306.03097/) (2023)

## Abstract (arXiv)

Advances in artificial intelligence (AI) are enabling systems that augment and collaborate with humans to perform simple, mechanistic tasks like scheduling meetings and grammar-checking text. However, such Human-AI collaboration poses challenges for more complex, creative tasks, such as carrying out empathic conversations, due to difficulties of AI systems in understanding complex human emotions and the open-ended nature of these tasks. Here, we focus on peer-to-peer mental health support, a setting in which empathy is critical for success, and examine how AI can collaborate with humans to facilitate peer empathy during textual, online supportive conversations. We develop Hailey, an AI-in-the-loop agent that provides just-in-time feedback to help participants who provide support (peer supporters) respond more empathically to those seeking help (support seekers). We evaluate Hailey in a non-clinical randomized controlled trial with real-world peer supporters on TalkLife (N=300), a large online peer-to-peer support platform. We show that our Human-AI collaboration approach leads to a 19.60% increase in conversational empathy between peers overall. Furthermore, we find a larger 38.88% increase in empathy within the subsample of peer supporters who self-identify as experiencing difficulty providing support. We systematically analyze the Human-AI collaboration patterns and find that peer supporters are able to use the AI feedback both directly and indirectly without becoming overly reliant on AI while reporting improved self-efficacy post-feedback. Our findings demonstrate the potential of feedback-driven, AI-in-the-loop writing systems to empower humans in open-ended, social, creative tasks such as empathic conversations.