mlsanityData to 14 Oct 2026

Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregivers

arXiv 2506.15047 ↗First posted 18 Jun 2025cs.HC cs.AI cs.CYcited by 13

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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.

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

23 of 187 references.

References · 23

  1. 2020Artificial Intelligence, Values and Alignmentcited by 448
  2. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 401
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  6. 2023CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotationcited by 94
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  10. 2023Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuringcited by 53
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11 more
  1. 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
  2. 2024Designing for Human-Agent Alignment: Understanding what humans want from their agentscited by 18
  3. 2024Lessons From an App Update at Replika AI: Identity Discontinuity in Human-AI Relationshipscited by 15
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  5. 2025AI Chatbots for Mental Health: Values and Harms from Lived Experiences of Depressioncited by 13
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  7. 2025Balancing Caregiving and Self-Care: Exploring Mental Health Needs of Alzheimer's and Dementia Caregiverscited by 7
  8. 2023Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?cited by 6
  9. 2020Patterns of Patient and Caregiver Mutual Support Connections in an Online Health Communitycited by 4
  10. 2023Opportunities in Mental Health Support for Informal Dementia Caregivers Suffering from Verbal Agitationcited by 4
  11. 2024MentalImager: Exploring Generative Images for Assisting Support-Seekers' Self-Disclosure in Online Mental Health Communitiescited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 11.

13 papers.

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