Conversational agents increasingly mediate everyday digital interactions, yet the effects of their communication style on user experience and task success remain insufficiently understood. Addressing this gap, we report a between-subject user study in which participants interacted with one of two versions of a chatbot called NAVI, which assisted them in an interactive map-based 2D navigation task. The two chatbot versions were designed to differ primarily in communication style: one used a friendly and supportive tone, while the other used a direct and task-focused tone. We also included a control condition where participants did not interact with a chatbot but received the step-by-step navigation instructions. The friendly chatbot significantly increased users' communication satisfaction and was associated with higher task success than the direct chatbot. However, participants in the control condition achieved the highest task success overall, suggesting that chatbot interaction may introduce overhead in tasks that can be completed effectively using straightforward instructions. We did not find significant evidence that gender moderated the effects of communication style, although exploratory gender-stratified analyses suggested patterns that warrant further investigation. Finally, we found limited evidence of global linguistic accommodation, with only selective feature-level alignment. These findings suggest that chatbot communication style influences users' perceptions of conversational agents and may improve performance relative to less supportive chatbot designs, but the overall value of chatbot interaction depends on the task context. The study highlights the need for task-sensitive, transparent and carefully evaluated communication-style choices in conversational-agent design.
Decoding Student Minds: Leveraging Conversational Agents for Psychological and Learning Analysis
STIH Laboratory, Sorbonne University（索邦大学STIH实验室）；LIPAH-LR11ES14, National Engineering School of Tunis（突尼斯国家工程学院LIPAH-LR11ES14）；Faculty of Sciences of Tunis（突尼斯科学学院）；LINFI Laboratory（LINFI实验室）
Stylistic Retrieval-based Dialogue System with Unparallel Training Data
Meituan Group（美团）；Tencent AI Lab（腾讯人工智能实验室）；Gaoling School of Artificial Intelliegent, Renmin University of China（中国人民大学高瓴人工智能学院）；College of William & Mary（威廉与玛丽学院）；Universite De Montreal（蒙特利尔大学）
Developing Effective Educational Chatbots with ChatGPT prompts: Insights from Preliminary Tests in a Case Study on Social Media Literacy (with appendix)
Università degli Studi di Milano Bicocca（米兰比可卡大学）；Universitat Pompeu Fabra（庞培法布拉大学）；University of Regensburg（雷根斯堡大学）；Politecnico di Milano（米兰理工大学）；Università degli Studi di Milano（米兰大学）
When Generative AI Is Intimate, Sexy, and Violent: Examining Not-Safe-For-Work (NSFW) Chatbots on FlowGPT
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Parsons School of Design（帕森斯设计学院）；School of Design, SUSTech（南方科技大学设计学院）；Clark University（克拉克大学）
User Perceptions of an LLM-Based Chatbot for Cognitive Reappraisal of Stress: Feasibility Study
King Center on Global Development, Stanford University（斯坦福大学全球发展国王中心）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Microsoft Research（微软研究院）；School of Interactive Computing, Georgia Institute of Technology（佐治亚理工学院互动计算学院）
Anthropomorphism and Trust in Human-Large Language Model interactions
Brain and Creativity Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California（脑与创造力研究所，文理学院，南加州大学）；USC Mrs. T.H. Chan Division of Occupational Science and Occupational Therapy, University of Southern California（USC 玛丽·T.H.陈职业科学与职业治疗 division，南加州大学）；Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles（精神病学与生物行为科学，大卫·格芬医学院，加州大学洛杉矶分校）；Google DeepMind, Zurich（谷歌深Mind，苏黎世）；Google Research（谷歌研究）；Brain and Creativity Institute（脑与创造力研究所）
