arXiv AI
Mind the Style: Impact of Communication Style on Human-Chatbot Interaction
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arXiv Computation and Language
6d ago
Generative Artificial Intelligence Chatbots for Motivational Interviewing: A Scoping Review From System Design to Intervention Outcomes
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.
arXiv AI
Jul 7
Predicting Biased Human Decision-Making with Large Language Models in Conversational Settings
arXiv:2601. 11049v2 Announce Type: replace-cross Abstract: We examine whether large language models (LLMs) can predict biased decision-making in conversational settings, and whether their predictions capture not only human cognitive biases but also how those effects change under cognitive load.
arXiv AI
Jul 29
Psychological Influences of Conversational AI: Research and Design Directions for Reducing Harm and Promoting Well-Being
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 AI
Aug 13
Toward Meaningful Transparency for AI Chatbots: Disclosing Persuasive Intent Reduces Persuasion
arXiv:2608. 11794v1 Announce Type: cross Abstract: The growing role of AI-generated content and AI-enabled systems in public communication has led regulators to demand clear disclosure of content provenance and AI involvement.
arXiv AI
Jun 10
From Perception to Action: Can UI Interventions Foster Sustainable LLM Chatbot
arXiv:2606. 10861v1 Announce Type: cross Abstract: LLM-powered chatbots are increasingly embedded in everyday workflows, raising sustainability concerns due to their energy use.
arXiv AI
Sep 7
How a Chatbot's Response Style Shapes a Classroom: A Multi-Agent Simulation of Students Consulting AI
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.