arXiv AI
Jun 11
Designing AI-Supported Focus Groups: A Role x Modality Playbook
arXiv:2606. 11835v1 Announce Type: cross Abstract: Collecting participants' lived experiences is central to design research.
Generative Experiences for Digital Mental Health Interventions: Evidence from a Randomized Study
arXiv:2604. 07558v3 Announce Type: replace-cross Abstract: Digital mental health (DMH) tools have extensively explored personalization of interventions to users' needs and contexts.
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arXiv AI
Sep 15
Personalizing Personal Health Interfaces: Co-Design with Generative AI
The paper explores how generative AI can lower the barrier to personalizing health dashboards by enabling users to co-design interfaces in Figma Make. In a study with 14 participants, redesigns of Google and Apple Health focused on personal context, future planning, and interactive experiences, though conversational AI designs tended toward chat-window conventions. AI facilitated the materialization of loosely articulated ideas, yet model defaults and generation latency influenced iteration, and the process highlighted interpretability and accountability over privacy, trust, and emotional safety.
arXiv AI
Jun 17
Talking to Your Data: Exploring Embodied Conversation as an Interface for Personal Health Reflection
arXiv:2606. 17767v1 Announce Type: cross Abstract: Personal health data from wearables are typically presented through dashboards of charts and summary statistics, requiring users to actively interpret patterns and implications.
arXiv AI
Sep 2
Designing Proactive Thought Partners for Writing
arXiv:2609.01588v1 Announce Type: cross Abstract: Writing involves diverse cognitive activities, from ideation to revision, and writers' needs vary across individuals and moments. Proactive AI promis...
arXiv Computation and Language
Sep 22
ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?
arXiv:2605.00468v2 Announce Type: replace Abstract: Plain Language Summaries (PLS) aim to make research accessible to lay readers, but they are typically written in a one-size-fits-all style that ign...
arXiv Computation and Language
Sep 18
CounselReflect: Opportunities and Challenges for Designing Tools to Support Self-Reflection on Mental Health and Well-Being Conversations with AI
The paper introduces CounselReflect, a tool that converts counseling quality metrics into a framework for users to reflect on their mental‑health AI conversations. Through interviews with 21 users, the study finds that while most participants rarely reflect on their interactions, they identify specific questions they would like such a tool to address. The findings also reveal that users tend to confirm existing beliefs and focus on familiar dimensions, highlighting the need for reflection tools to expose blind spots and encourage a more comprehensive examination of AI interactions, especially when revisiting emotionally charged exchanges.
arXiv AI
Sep 1
MAGneT: Coordinated Multi-Agent Generation of Synthetic Multi-Turn Mental Health Counseling Sessions
arXiv:2509.04183v3 Announce Type: replace-cross Abstract: The growing demand for scalable psychological counseling highlights the need for high-quality, privacy-compliant data, yet such data remains...
arXiv AI
Jun 15
Thinking Outside the [Chat]Box: Bridging Computer Science and Industrial Design for Cognitive-Inclusive Generative AI
arXiv:2606. 14306v1 Announce Type: cross Abstract: Current Generative AI (GenAI) interfaces remain largely constrained to chatbox interaction, which can impose high cognitive demands on users and create substantial barriers for people with intellectual disabilities (ID), including prompt formulation difficulties, response overload, and limited mechanisms to assess information reliability.
arXiv AI
Sep 21
Aligning with Lived Experience: Heterogeneous Benefits of Fine Tuning in Mental Health Support Generation
The paper introduces the COmmunity-centered Peer Engaged Support (COPES) dataset and a three‑axis evaluation framework to gauge how well Large Language Models (LLMs) align with community perspectives on mental‑health support queries. Experiments show that fine‑tuning LLMs on COPES improves strategy alignment and emotion‑tone alignment by over 50% for general‑purpose models, yet these gains are uneven across subreddits and coping strategies. The study also finds that post‑training shifts the model’s recommendations toward problem‑focused advice while reducing emotion‑focused responses, indicating persistent disparities in performance across different communities and needs.
arXiv Computation and Language
6d ago
MindEval2: Clinically Grounded Patients and Rubrics for Evaluating Language Models in Mental Health Support
MindEval2 is a new evaluation framework for AI chatbots in mental health support, comprising Mental Health Quality Rubrics (MHQR) and the MindSim patient simulation harness. MHQR, developed with PhD-level psychologists, provides explicit, actionable criteria for assessing response quality, while MindSim offers realistic, longitudinal patient interactions with a memory component. The study evaluated 18 models, finding that although surface-level quality has improved, guidance calibration remains the main failure across all models.
arXiv AI
Sep 2
ConvDeck: Conversational Paper-to-Slide Generation via Stage-Specific User Feedback
arXiv:2609.00226v1 Announce Type: new Abstract: Automatic academic paper-to-slide generation is inherently iterative, because creating an effective presentation requires repeated cycles of generation...
Hugging Face Trending Papers
Jul 28
From Dyad to Triad: Eliciting XAI Requirements in Stroke Rehabilitation
Eliciting explainable AI (XAI) requirements from stroke survivors presents a methodological challenge with direct implications for the design of trustworthy brain-computer interfaces for rehabilitation. How can patients and caregivers articulate preferences about algorithmic transparency when they lack conceptual frameworks for explainability, and when standard elicitation approaches are structurally inadequate for users with acquired communication disorders?