LLM responses mirror venting with higher regulation and escalation; therapist personas lower escalation while preserving regulation, and lay raters miss escalation.
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11 Pith papers cite this work, alongside 2,065 external citations. Polarity classification is still indexing.
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SLIP and ETHICS introduce a staged intervention system for AI emotional companions using qualitative affect and narrative signals, with small-scale deployment and synthetic tests showing zero false positives for normal use but detection gaps in sustained high-energy states.
A dual hierarchical RL framework with two agents coordinates high-level dialogue strategy and low-level question generation to emulate judicial questioning and extract key information from Supreme Court arguments, outperforming baselines.
A multi-agent system with finite state machine for therapeutic stages was perceived as significantly more natural and human-like than single-agent or unguided LLM versions in an RCT with 66 participants.
Participatory workshops with international students produced design ideas for conversational interfaces that target uncertainty, loneliness, and cultural misunderstandings during study-abroad transitions.
LLMs generate 5P causal graphs from 46 psychotherapy intake transcripts that match human expert graphs in structure and meaning, with moderate clinical usefulness ratings.
SafeScreen enforces individualized safety constraints as a prerequisite for video retrieval by using profile extraction, adaptive VideoRAG analysis, and LLM decision-making to approve content for vulnerable users.
Shame/stigma and access barriers to therapy predict higher perceived helpfulness of AI mental health support, especially for therapy-experienced users, while access and cost barriers predict greater usage intensity.
Mixed-methods studies of an LLM-supported peer support system uncover systematic misalignments where mental health experts flag critical safety and fidelity issues in peer responses that the supporters themselves do not perceive.
AI-saturated markets will produce premiums for verified human presence in labor, requiring governance to treat human-provenance verification as infrastructure rather than optional authenticity labels.
A voice-based AI conversational agent for serious illness conversations in the ED was feasible and acceptable to most of 55 patients, with comparable feeling-heard ratings to clinicians but with risks of hallucinated diagnostic statements.
citing papers explorer
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When Support Escalates Distress: Regulation and Escalation in LLM Responses to Venting and Advice-Seeking
LLM responses mirror venting with higher regulation and escalation; therapist personas lower escalation while preserving regulation, and lay raters miss escalation.
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SLIP & ETHICS: Graduated Intervention for AI Emotional Companions
SLIP and ETHICS introduce a staged intervention system for AI emotional companions using qualitative affect and narrative signals, with small-scale deployment and synthetic tests showing zero false positives for normal use but detection gaps in sustained high-energy states.
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Dual Hierarchical Dialogue Policy Learning for Legal Inquisitive Conversational Agents
A dual hierarchical RL framework with two agents coordinates high-level dialogue strategy and low-level question generation to emulate judicial questioning and extract key information from Supreme Court arguments, outperforming baselines.
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Structure Matters: Evaluating Multi-Agents Orchestration in Generative Therapeutic Chatbots
A multi-agent system with finite state machine for therapeutic stages was perceived as significantly more natural and human-like than single-agent or unguided LLM versions in an RCT with 66 participants.
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Navigating Transitions: Envisioning Conversational User Interfaces to Support International Students
Participatory workshops with international students produced design ideas for conversational interfaces that target uncertainty, loneliness, and cultural misunderstandings during study-abroad transitions.
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InsightFlow: LLM-Driven Synthesis of Patient Narratives for Mental Health into Causal Models
LLMs generate 5P causal graphs from 46 psychotherapy intake transcripts that match human expert graphs in structure and meaning, with moderate clinical usefulness ratings.
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SafeScreen: A Safety-First Screening Framework for Personalized Video Retrieval for Vulnerable Users
SafeScreen enforces individualized safety constraints as a prerequisite for video retrieval by using profile extraction, adaptive VideoRAG analysis, and LLM decision-making to approve content for vulnerable users.
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Talking to a Human as an Attitudinal Barrier: A Mixed Methods Evaluation of Stigma, Access, and the Appeal of AI Mental Health Support
Shame/stigma and access barriers to therapy predict higher perceived helpfulness of AI mental health support, especially for therapy-experienced users, while access and cost barriers predict greater usage intensity.
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"Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions
Mixed-methods studies of an LLM-supported peer support system uncover systematic misalignments where mental health experts flag critical safety and fidelity issues in peer responses that the supporters themselves do not perceive.
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Human-Provenance Verification should be Treated as Labor Infrastructure in AI-Saturated Markets
AI-saturated markets will produce premiums for verified human presence in labor, requiring governance to treat human-provenance verification as infrastructure rather than optional authenticity labels.
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Exploring the Feasibility and Acceptability of AI-Mediated Serious Illness Conversations in the Emergency Department
A voice-based AI conversational agent for serious illness conversations in the ED was feasible and acceptable to most of 55 patients, with comparable feeling-heard ratings to clinicians but with risks of hallucinated diagnostic statements.