Driver distraction remains a leading contributor to motor vehicle crashes, necessitating rigorous evaluation of new in-vehicle technologies. This study assessed the visual and cognitive demands associated with an advanced Large Language Model (LLM) conversational agent (Gemini Live) during on-road driving, comparing it against handsfree phone calls, visual turn-by-turn guidance (low load baseline), and the Operation Span (OSPAN) task (high load anchor). Thirty-two licensed drivers completed five secondary tasks while visual and cognitive demands were measured using the Detection Response Task (DRT) for cognitive load, eye-tracking for visual attention, and subjective workload ratings. Results indicated that Gemini Live interactions (both single-turn and multi-turn) and hands-free phone calls shared similar levels of cognitive load, between that of visual turn-by-turn guidance and OSPAN. Exploratory analysis showed that cognitive load remained stable across extended multi-turn conversations. All tasks maintained mean glance durations well below the well-established 2-second safety threshold, confirming low visual demand. Furthermore, drivers consistently dedicated longer glances to the roadway between brief off-road glances toward the device during task completion, particularly during voice-based interactions, rendering longer total-eyes-off-road time findings less consequential. Subjective ratings mirrored objective data, with participants reporting low effort, demands, and perceived distraction for Gemini Live. These findings demonstrate that advanced LLM conversational agents, when implemented via voice interfaces, impose cognitive and visual demands comparable to established, low-risk hands-free benchmarks, supporting their safe deployment in the driving environment.
Better Adherence, Richer Context: A Field Evaluation of LLM-Powered Conversational Voice Diaries for Sleep
The Johns Hopkins University（约翰霍普金斯大学）；Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine（精神病学与行为科学系，约翰霍普金斯大学医学院）
The recent advancements in Large Language Models (LLMs), particularly conversational LLMs like ChatGPT, have prompted changes in a range of fields, including design. This study aims to examine the capabilities of ChatGPT in a human-centered design process. To this end, a hypothetical design project was conducted, where ChatGPT was utilized to generate personas, simulate interviews with fictional users, create new design ideas, simulate usage scenarios and conversations between an imaginary prototype and fictional users, and lastly evaluate user experience. The results show that ChatGPT effecti…
LETGAMES: An LLM-Powered Gamified Approach to Cognitive Training for Patients with Cognitive Impairment
Shanghai University of Finance and Economics（上海财经大学）；Northwest Polytechnical University（西北工业大学）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；College of Computer Science, Sichuan University（四川大学计算机学院）
NEOLAF, an LLM-powered neural-symbolic cognitive architecture
IEEE Artificial Intelligent Standards Committee, IEEE（电气和电子工程师协会人工智能标准委员会）；Macao University of Science and Technology（澳门科技大学）；Carnegie Learning（卡内基学习公司）；University of Sheffield（谢菲尔德大学）；National Institute of Education, Singapore（新加坡国立教育学院）；University of Memphis（孟菲斯大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Rochester（罗切斯特大学）；Stanford University（斯坦福大学）；Beijing Normal University（北京师范大学）
Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning
Florida State University（佛罗里达州立大学）；University of Washington（华盛顿大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Towards the "Digital Me": A vision of authentic Conversational Agents powered by personal Human Digital Twins
Technical University of Denmark（丹麦技术大学）；School of Design（设计学院）；University of Strathclyde（斯特拉斯克莱德大学）；Department of Design, Manufacturing and Engineering Management（设计、制造与工程管理系）
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
Mila-Québec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）；Arizona State University（亚利桑那州立大学）；Princeton University（普林斯顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Université de Montréal（蒙特利尔大学）；National Research Council Canada（加拿大国家研究委员会）
Language-Based Digital Twins for Elderly Cognitive Assistance
Ritchie School of Engineering and Computer Science, University of Denver（丹佛大学里奇工程与计算机科学学院）；Department of Neurology, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院神经内科）
RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models
School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；School of Computing, Clemson University（克莱姆森大学计算机学院）；Washington University School of Medicine in St. Louis（圣路易斯华盛顿大学医学院）；The Keck School of Medicine of the University of Southern California（南加州大学凯克医学院）；Mayo Clinic（梅奥诊所）；Banner Alzheimer’s Institute（班纳阿尔茨海默病研究所）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Juji. Inc.（Juji公司）；IBM Research AI（IBM研究AI部门）；University of California, Irvine（加利福尼亚大学欧文分校）
The rise of increasingly more powerful chatbots offers a new way to collect information through conversational surveys, where a chatbot asks open-ended questions, interprets a user's free-text responses, and probes answers whenever needed. To investigate the effectiveness and limitations of such a chatbot in conducting surveys, we conducted a field study involving about 600 participants. In this study with mostly open-ended questions, half of the participants took a typical online survey on Qualtrics and the other half interacted with an AI-powered chatbot to complete a conversational survey. …
Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs. A promising direction is to combine the two approaches for high quality, low latency, and low cost solutions. In this paper, we introduce Evorus, a crowd-powered conversational assistant built to automate itself over time by (i) allowing new chatbots to be easily integrated to automate more scenarios, (ii) reusing prior crowd answers, and (iii) learning to automatically approve response candidates. Our 5-month-long deployment wit…
Carnegie Mellon University（卡内基梅隆大学）；University of Michigan（密歇根大学）；Ariel University（阿里埃勒大学）
Intelligent conversational assistants, such as Apple's Siri, Microsoft's Cortana, and Amazon's Echo, have quickly become a part of our digital life. However, these assistants have major limitations, which prevents users from conversing with them as they would with human dialog partners. This limits our ability to observe how users really want to interact with the underlying system. To address this problem, we developed a crowd-powered conversational assistant, Chorus, and deployed it to see how users and workers would interact together when mediated by the system. Chorus sophisticatedly conver…
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Humans quite frequently interact with conversational agents. The rapid advancement in generative language modeling through neural networks has helped advance the creation of intelligent conversational agents. Researchers typically evaluate the output of their models through crowdsourced judgments, but there are no established best practices for conducting such studies. Moreover, it is unclear if cognitive biases in decision-making are affecting crowdsourced workers' judgments when they undertake these tasks. To investigate, we conducted a between-subjects study with 77 crowdsourced workers to …
State-of-the-art conversational agents have advanced significantly in conjunction with the use of large transformer-based language models. However, even with these advancements, conversational agents still lack the ability to produce responses that are informative and coherent with the local context. In this work, we propose a dialog framework that incorporates both local knowledge as well as users' past dialogues to generate high quality conversations. We introduce an approach to build a dataset based on Reddit conversations, where outbound URL links are widely available in the conversations …
StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework
Department of Management Science and Engineering, Stanford University（管理科学与工程系，斯坦福大学）；Department of Electrical Engineering, Stanford University（电气工程系，斯坦福大学）；Department of Computer Science, Stanford University（计算机科学系，斯坦福大学）
Politecnico di Milano（米兰理工大学）；University of Claude Bernard Lyon 1（里昂第一大学（克洛德·贝尔纳大学））
In this paper we explore the opportunities brought by cognitive augmentation to provide a more natural and accessible web browsing experience. We explore these opportunities through \textit{conversational web browsing}, an emerging interaction paradigm for the Web that enables blind and visually impaired users (BVIP), as well as regular users, to access the contents and features of websites through conversational agents. Informed by the literature, our previous work and prototyping exercises, we derive a conceptual framework for supporting BVIP conversational web browsing needs, to then focus …
Evaluating User Experience in Conversational Recommender Systems: A Systematic Review Across Classical and LLM-Powered Approaches
School of Computer Science, University of Technology Sydney（技术科技大学计算机科学学院）；The Applied College, King Abdulaziz University（国王阿卜杜勒阿齐兹大学应用学院）；Australian Institute of Health Innovation, Macquarie University（麦考瑞大学健康创新研究所）
University of Maryland College Park（马里兰大学帕克分校）；The Chinese University of Hong Kong（香港中文大学）；Hong Kong University of Science and Technology（香港科技大学）
Conversational agents (CAs) have the great potential in mitigating the clinicians' burden in screening for neurocognitive disorders among older adults. It is important, therefore, to develop CAs that can be engaging, to elicit conversational speech input from older adult participants for supporting assessment of cognitive abilities. As an initial step, this paper presents research in developing the backchanneling ability in CAs in the form of a verbal response to engage the speaker. We analyzed 246 conversations of cognitive assessments between older adults and human assessors, and derived the…
