arXiv:2607. 25057v1 Announce Type: new Abstract: As conversational AI systems become increasingly integrated into daily life, their potential effects on user well-being require ongoing attention.
arXiv:2606. 04150v1 Announce Type: new Abstract: Public discourse and emerging policy typically assume that AI emotional support is a deliberate act: a lonely user consciously seeking comfort from a dedicated companion chatbot.
This scoping review examined 48 studies on generative AI chatbots designed to deliver motivational interviewing (MI). It found that most systems were text‑based and disembodied, with about half incorporating dynamic adaptation, and that safety reporting was inconsistent. While user perceptions were generally positive and many studies reported MI‑consistent interactions, evidence for sustained behavioral or functional change remains limited.
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:2607. 09253v1 Announce Type: cross Abstract: AI chatbots are increasingly used for answering health-related questions.
arXiv:2609.00250v1 Announce Type: cross Abstract: Many people now see AI systems as not just productivity tools but as social companions. Researchers are eager to study the consequences of AI compani...
arXiv:2511.11689v4 Announce Type: replace-cross Abstract: Generative AI chatbots built for mental health could extend access to care, but evidence from real-world use is limited. We report a single-a...
The paper introduces Safety Nudges, a browser-based tool that displays lightweight, in situ flags when a conversational AI exhibits risky behavior such as hallucination or overconfidence. In a two‑week field study with 45 frequent chatbot users, participants reported that the nudges were useful, clear, and minimally disruptive, and most felt more aware of potential AI harms. However, increased awareness did not automatically translate into measurable changes in user behavior, underscoring the need for relevance, calibration, and user control in nudge design.
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:2505.08894v2 Announce Type: replace-cross Abstract: Large language model (LLM) chatbots are increasingly reaching users through messaging platforms (e.g. WhatsApp). However, these systems remai...
arXiv:2608. 07902v1 Announce Type: cross Abstract: Youth increasingly turn to AI chatbots for social and emotional support, raising concerns about how these systems respond, especially in high-stakes situations.
The study simulates a virtual classroom of 20 student agents who consult either a friend or a counselor AI when stressed. Five state variables (stress, happiness, self‑reliance, AI dependence, sociability) are tracked over daily phases, and the counselor AI is tested with six response styles (affirming, listening, solution‑oriented, reality‑redirecting, inciting, blaming). Results show that a solution‑oriented style lowers AI dependence and boosts self‑reliance, while affirming and inciting styles increase AI dependence, with inciting also raising stress and absenteeism; the listening style does not alleviate stress.
arXiv:2608. 10672v1 Announce Type: cross Abstract: Social interaction has become one of the most common uses of LLMs, yet research on emotional bonds with AI has focused largely on how users experience these systems, leaving the systems' role in relationship formation poorly understood.