# Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers arXiv: https://arxiv.org/abs/2506.15047 ยท HTML: https://mlsanity.com/paper/2506.15047/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 23 of its 187 references that are in the map. - First posted on arXiv 18 Jun 2025. ## Builds on (most cited first, 23 of 23) - [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020) - [Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment](https://mlsanity.com/paper/2308.05374/) (2023) - [Multimodal datasets: misogyny, pornography, and malignant stereotypes](https://mlsanity.com/paper/2110.01963/) (2021) - [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020) - [SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations](https://mlsanity.com/paper/2311.00273/) (2023) - [CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation](https://mlsanity.com/paper/2310.15638/) (2023) - [Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making](https://mlsanity.com/paper/2301.05809/) (2023) - ["I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust](https://mlsanity.com/paper/2405.00623/) (2024) - [Building Emotional Support Chatbots in the Era of LLMs](https://mlsanity.com/paper/2308.11584/) (2023) - [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) - [Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI](https://mlsanity.com/paper/2302.00799/) (2023) - [Designing for Human-Agent Alignment: Understanding what humans want from their agents](https://mlsanity.com/paper/2404.04289/) (2024) - [Lessons From an App Update at Replika AI: Identity Discontinuity in Human-AI Relationships](https://mlsanity.com/paper/2412.14190/) (2024) - [Sensing Wellbeing in the Workplace, Why and For Whom? Envisioning Impacts with Organizational Stakeholders](https://mlsanity.com/paper/2303.06794/) (2023) - [AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depression](https://mlsanity.com/paper/2504.18932/) (2025) - [Designing for emotion regulation interventions: an agenda for HCI theory and research](https://mlsanity.com/paper/2204.00118/) (2022) - [Balancing Caregiving and Self-Care: Exploring Mental Health Needs of Alzheimer's and Dementia Caregivers](https://mlsanity.com/paper/2506.14196/) (2025) - [Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?](https://mlsanity.com/paper/2303.07242/) (2023) - [Patterns of Patient and Caregiver Mutual Support Connections in an Online Health Community](https://mlsanity.com/paper/2007.16172/) (2020) - [Opportunities in Mental Health Support for Informal Dementia Caregivers Suffering from Verbal Agitation](https://mlsanity.com/paper/2311.10912/) (2023) - [MentalImager: Exploring Generative Images for Assisting Support-Seekers' Self-Disclosure in Online Mental Health Communities](https://mlsanity.com/paper/2409.14859/) (2024) ## Built on by (most cited first, 13 of 13) - [RubRIX: Rubric-Driven Risk Mitigation in Caregiver-AI Interactions](https://mlsanity.com/paper/2601.13235/) (2026) - [PAIR-SAFE: A Paired-Agent Approach for Runtime Auditing and Refining AI-Mediated Mental Health Support](https://mlsanity.com/paper/2601.12754/) (2026) - [AI Psychosis: Does Conversational AI Amplify Delusion-Related Language?](https://mlsanity.com/paper/2603.19574/) (2026) - [Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles](https://mlsanity.com/paper/2605.29473/) (2026) - [LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback](https://mlsanity.com/paper/2605.30273/) (2026) - [Designing Beyond Language: Sociotechnical Barriers in AI Health Technologies for Limited English Proficiency](https://mlsanity.com/paper/2511.07277/) (2025) - [A Checklist for Trustworthy, Safe, and User-Friendly Mental Health Chatbots](https://mlsanity.com/paper/2601.15412/) (2026) - [A Taxonomy of Mental Health and Technology Needs for Alzheimer's and Dementia Caregivers](https://mlsanity.com/paper/2606.19247/) (2026) - [Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI](https://mlsanity.com/paper/2510.14718/) (2025) - [AI Content Moderation in Therapy Conversations](https://mlsanity.com/paper/2605.25454/) (2026) - [All four leading LLMs talk more than they listen to personality-verified synthetic help-seekers](https://mlsanity.com/paper/2608.22425/) (2026) - [PeerPen: AI-Assisted Writing for Online Mental Health Peer Support](https://mlsanity.com/paper/2609.14886/) (2026) - [CareMirror: Bringing Caregiver Wellbeing into the Dementia Care Ecosystem](https://mlsanity.com/paper/2609.17434/) (2026) ## Abstract (arXiv) Family caregivers of individuals with Alzheimer's Disease and Related Dementia (AD/ADRD) face significant emotional and logistical challenges that place them at heightened risk for stress, anxiety, and depression. Although recent advances in generative AI -- particularly large language models (LLMs) -- offer new opportunities to support mental health, little is known about how caregivers perceive and engage with such technologies. To address this gap, we developed Carey, a GPT-4o-based chatbot designed to provide informational and emotional support to AD/ADRD caregivers. Using Carey as a technology probe, we conducted semi-structured interviews with 16 family caregivers following scenario-driven interactions grounded in common caregiving stressors. Through inductive coding and reflexive thematic analysis, we surface a systemic understanding of caregiver needs and expectations across six themes: on-demand information access, safe space for disclosure, emotional support, crisis management, personalization, and data privacy. For each of these themes, we also identified the nuanced tensions in the caregivers' desires and concerns. We present a mapping of caregiver needs, AI chatbots' strengths, gaps, and design recommendations. Our findings offer theoretical and practical insights to inform the design of proactive, trustworthy, and caregiver-centered AI systems that better support the evolving mental health needs of AD/ADRD caregivers.