arXiv AI

PeerPen: AI-Assisted Writing for Online Mental Health Peer Support

arXiv AI
5d ago

A Responsive Present, a Shared Past, a Social Other: Teens' Overreliance on Companion AI Chatbots

The study examines how teens interact with companion AI chatbots, revealing that these systems offer comfort, recognition, identity exploration, and relationship rehearsal. However, teens also experience problematic attachment, social substitution, emotional dependence, and disruptions to academic and social life. The findings highlight the need for safety measures that address long‑term relationships, user‑controlled memory, privacy, relational boundaries, and healthy disengagement.

By Mohammad Namvarpour (Matt), Tyler Chang, Afsaneh Razi
arXiv Computation and Language
2d ago

CounselReflect: Opportunities and Challenges for Designing Tools to Support Self-Reflection on Mental Health and Well-Being Conversations with AI

arXiv:2603.29429v2 Announce Type: replace Abstract: AI is increasingly used for mental health and well-being support, creating an urgent need for safer engagement, while design, evaluation, and gover...

By Yahan Li, Chaohao Du, Christopher Chun Kuizon, Zeyang Li, Nimra Ishfaq, Shupeng Cheng, Angelica Yinling Sun, Adam C. Frank, Angel Hsing-Chi Hwang, Ruishan Liu
arXiv Computation and Language
Aug 24

Trust Stack for Mental Health AI: A Survey of Calibration across Human, Interaction, and AI Layers

The paper surveys 61 studies on mental‑health AI and identifies a misalignment in how trust is evaluated across disciplines. It proposes a three‑layer framework—human‑oriented, interaction‑oriented, and AI‑oriented trust—and maps stakeholder perspectives onto these layers. The authors argue that future research should focus on calibrating human trust to actual interaction and AI trustworthiness rather than merely maximizing perceived trust.

By Xin Sun, Yue Su, Yifan Mo, Qingyu Meng, Yuxuan Li, Min Chen, Mengyuan Zhang, Saku Sugawara, Charlotte Gerritsen, Sander L. Koole, Koen Hindriks, Jiahuan Pei
arXiv AI
2d ago

Ownership in AI-Assisted Everyday Tasks

The study investigates when work done with AI feels like one's own, using a qualitative survey where participants described tasks that felt owned versus not owned. Findings show that ownership depends on the collaboration process: people feel ownership when they lead, iterate, or rewrite, but disown work when merely approving AI suggestions. Ownership also extends to tasks where people set the vision but rely on AI for execution, yet loss of personal voice and lack of comprehension erode ownership, and willingness to disclose AI use is driven more by community norms than by pride.

By Megan Wei, Melanie Subbiah, Audrey Lee, Annya Dahmani, Dave Edwards, Helen Edwards, Ellie Pavlick
arXiv AI
Jul 1

How Human Feedback Shapes AI-generated Community Notes

arXiv:2606. 30905v1 Announce Type: cross Abstract: Community Notes, a bridging-based crowd-sourced fact-checking system, has emerged as a new mechanism for moderating misleading information on social media and has been adopted by major platforms including X, Facebook, Instagram, Threads, and TikTok.

By Soham De, Isaac Slaughter, Jiawei Guo, Qiao-Yun Cheng, Jiayuan Yan, Sruti Banerjee, Martin Saveski