Fake Friends and Sponsored Ads: The Risks of Advertising in Conversational SearchDigital commerce thrives on advertising, with many of the largest technology companies relying on it as a significant source of revenue. However, in the context of information-seeking behavior, such as search, advertising may degrade the user experience by lowering search quality, misusing user data for inappropriate personalization, potentially misleading individuals, or even leading them toward harm. These challenges remain significant as conversational search technologies, such as ChatGPT, become widespread. This paper critically examines the future of advertising in conversational search, utilizing several speculative examples to illustrate the potential risks posed to users who seek guidance on sensitive topics. Additionally, it provides an overview of the forms that advertising might take in this space and introduces the “fake friend dilemma,” the idea that a conversational agent may exploit unaligned user trust to achieve other objectives. This study presents a provocative discussion on the future of online advertising in the space of conversational search and ends with a call to action.2025JEJacob EricksonConversational ChatbotsContent Moderation & Platform GovernanceMisinformation & Fact-CheckingCHI
Towards Age-Inclusive Conversational interfaces: Understanding Requirements Across Age GroupsAs Conversational User Interfaces (CUI) become integrated into daily life, users’ diversity, particularly in age, is increasing. However, older adults often encounter challenges interacting with CUI. Although some of these challenges can be mitigated through age-specific design, many mass-market CUI systems (e.g., smart speakers) are intended for a broad range of consumers. Designing such interfaces that support users of all ages is predicated on a clear understanding of age-based similarities and differences when interacting with CUI. Prior research primarily focused on differences in interaction behaviours. However, we still lack a formal understanding of similarities and differences not only in behaviours, but also in expectations and needs for interacting with CUI. In this paper, we first present an age-based comparison of CUI-related user behaviours, expectations, and needs, synthesized around seven major themes based on a systematic literature review. We then reflect on the implications these have for age-inclusive adaptive CUI design.2025RBRezvan Boostani et al.Voice User Interface (VUI) DesignAging-Friendly Technology DesignCHI
The Ethics of Psychological Manipulation in Adversarial Conversational AI: Confronting the Recognition-Behaviour GapConversational AI systems, powered by advanced Large Language Models, have rapidly developed human-like persuasion capabilities that raise concerns about psychological manipulation. This provocation examines the ethical problems that arise when these systems exploit cognitive biases and social compliance mechanisms during interactions with users. Building on established theoretical work and recent empirical research, we identify a particularly concerning pattern: the recognition-behaviour gap, where users consciously identify manipulative strategies yet fail to protect themselves accordingly. Current ethical frameworks fall short in addressing these sophisticated risks in conversational contexts. Rather than proposing yet another comprehensive framework, we identify five essential dimensions that extend existing approaches to address this recognition-behaviour gap: preserving user autonomy through structural design, implementing safeguards beyond awareness, developing context-sensitive ethics, ensuring persona consistency and transparency, and establishing continuous vulnerability monitoring. This paper confronts these ethical challenges directly and calls for practical protective measures to safeguard user autonomy as conversational AI becomes increasingly prevalent in everyday life.2025SAStephen Aboshi et al.AI Ethics, Fairness & AccountabilityCHI
Crossing the Line? The Paradox of Human-Like Design in Conversational AgentsSince the early development of conversational agents (CAs), human-likeness has been a central design focus. Numerous studies have highlighted the benefits of more human-like CAs, including user experience, engagement, and trust improvements. As a result, researchers have proposed guidelines for designing CAs that closely resemble human communication styles. However, a growing body of research argues against excessive human-likeness, citing concerns about setting unrealistic expectations, facilitating overtrust, and enabling manipulation. To mitigate these risks, some researchers advocate for design choices that clearly differentiate CAs from humans, such as using synthetic voices or robotic visual representations to signal their artificial nature. This provocation paper explores the paradox between these two perspectives. Does the very act of making CAs interact in human-like ways inherently contradict efforts to maintain transparency about their artificial nature? We invite discussion on the implications this contradiction holds for the future of CA design.2025NZNima Zargham et al.Agent Personality & AnthropomorphismCHI
PITCH: Designing Agentic Conversational Support for Planning and Self-reflectionEffective planning and reflection are essential for knowledge workers' productivity and well-being, yet many struggle with them. While conversational agents (CAs) have shown promise, existing approaches rely on repetitive check-in without variance. We designed PITCH, a CA that checks in twice daily for morning planning and evening reflection while considering the morning conversation. A two-week field study with 12 graduate students demonstrated that engagement with PITCH increased their perceived well-being over time. We also evaluated a rotation strategy, which cycles through diverse topics every day, hypothesizing that rotation would mitigate wear-out effects and offer new perspectives. The results revealed that the specificity of a randomly chosen goal was perceived as being out of context and authoritarian, with most preferring the non-rotation version for consistency and flexibility. These findings highlight the potential of CAs to support knowledge workers and offer design considerations for varying conversations to provide topical diversity.2025AAAdnan Abbas et al.Conversational ChatbotsKnowledge Worker Tools & WorkflowsCHI
Beyond Functionality: Co-Designing Voice User Interfaces for Older Adults' Well-beingThe global population is rapidly aging, necessitating technologies that promote healthy aging. Voice User Interfaces (VUIs), leveraging natural language interaction, offer a promising solution for older adults due to their ease of use. However, current design practices often overemphasize functionality, neglecting older adults’ complex aspirations, psychological well-being, and social connectedness. To address this gap, we conducted co-design sessions with 20 older adults employing an empathic design approach. Half of the participants interacted with a probe involving health information learning, while the others focused on a probe related to exercise. This method engaged participants in collaborative activities to uncover non-functional requirements early in the design process. Results indicate that when encouraged to share their needs within a social context, older adults revealed a range of sensory, aesthetic, hedonic, and social preferences and, more importantly, the specific personas of VUIs. These insights inform the relative importance of these factors in VUI design.2025XHXinhui Hu et al.Voice User Interface (VUI) DesignAging-Friendly Technology DesignParticipatory DesignCHI
Exploring Artists’ and Art Viewers’ Perspectives for Art Chatbots: Implications for a Design FrameworkRecent advances in large language models (LLMs) and conversational user interfaces (CUIs) unlock new ways to help art viewers get answers about artworks. To clarify the roles that artists and viewers envision for art chatbots, we conducted two empirical studies in the domain of traditional Chinese painting, given its cultural depth. First, we interviewed five artists about how they currently respond to viewer inquiries and their attitudes toward chatbots. Second, we asked art viewers (N=102) to pose questions to either an artist or a chatbot. Results show that artists see chatbots as useful for factual or repetitive queries but hesitate to entrust emotive or personal discussions to them. Viewers also favor chatbots for efficiency but desire human input for deeper or personal topics. Based on these insights, we propose a design framework that balances the perspectives of both artists and viewers, contributing to the CUI community’s understanding of domain-specific chatbot design.2025JLJinyu Liu et al.Conversational ChatbotsDesign FictionCHI
The Art of Talking Machines: A Comprehensive Literature Review of Conversational User InterfacesConversational User Interfaces (CUIs) enable human-like interactions via voice, text, and multimodal communication, driven by natural language processing and machine learning. Prior literature reviews have primarily focused on specific application domains or design aspects, lacking an integrated, multi-dimensional analysis. This study addresses this gap by providing a structured framework synthesizing CUI research into interface design, system development, and ethical considerations. Our analysis highlights advancements in CUI design, such as dialogue structure, multimodal interactions, and adaptability. It also reveals persistent challenges, including bias in persona design, trust calibration, and data privacy. System development benefits from improvements in NLP, conversation memory, and multilingual capabilities. Ethical considerations, including social bias, user autonomy, and transparency, remain central to discussions on responsible CUI design. By analyzing existing research, we identify key gaps and suggest future directions, including multilingual and culturally adaptive CUIs, privacy-preserving AI techniques, and enhanced reasoning mechanisms for context-aware interactions.2025MNMohammad (Matt) Namvarpour et al.Intelligent Voice Assistants (Alexa, Siri, etc.)Conversational ChatbotsAgent Personality & AnthropomorphismCHI
SmartEats: Investigating the Effects of Customizable Conversational Agent in Dietary RecommendationsIn conversational recommender systems (CRS), the communication characteristics exhibited by the conversational agent (CA) can greatly shape user experience and their perceptions of the recommendation quality. Yet, prior work often adopts a one-size-fits-all approach, leaving the potential benefits of CA customizability—allowing users to tailor agent traits to their preferences—largely unexplored. We examine this gap in the context of dietary recommendations by introducing SmartEats, a CRS featuring a CA that can be customized by users. Through a between-subjects experiment (N = 214), we compared SmartEats to a non-customizable baseline, and followed up with participants after one week to understand whether and how the recommendations affect their food choices. We found that CA customizability directly improved participants' immediate experience and indirectly enhanced their ability to later recall the recommendations. Reflecting on the findings, we discuss opportunities for CRS to enhance health and well-being by leveraging the customizability of emerging AI technologies.2025MLMinhui Liang et al.Intelligent Voice Assistants (Alexa, Siri, etc.)Conversational ChatbotsRecommender System UXCHI
User Preferences in Conversational AI for Healthcare: Insights from an Interview StudyChatbot-based symptom diagnosis apps are becoming increasingly popular, yet concerns remain around usability and user trust. This study explores user preferences regarding chatbot characteristics using a rhetorical structure in symptom diagnosis chatbots. We conducted 16 semi-structured interviews across two use-case groups (varying in symptom severity) and analyzed 69 user reviews from four chatbot applications. Findings show that users consistently valued logos (clear explanations, structured dialogue) and ethos (consistency, next steps), while pathos (emotional support) became more important in high-severity scenarios. Similarly, logos-based characteristics were pivotal in all phases, but ethos became prominent in the third phase – diagnosis delivery. Interviews uncovered various themes around dialogue management, interaction design, and personalization needs. App reviews supported these findings, highlighting gaps in transparency, empathy, and usability. Based on these insights, we propose design guidelines and visualize interaction concepts that align with rhetorical strategies to improve trust and effectiveness in health-focused conversational agents.2025RJRutuja Joshi et al.Intelligent Voice Assistants (Alexa, Siri, etc.)Conversational ChatbotsAgent Personality & AnthropomorphismCHI
TacTalk: Personalizing Haptics Through ConversationHaptic experiences are highly personal, but despite prior work exploring interfaces enabling personalization, we don't know what process drives the personalization of haptics. To enable a study of this process, including users' mental models and vocabularies, we introduce TacTalk, a conversational system enabling real time tuning of virtual haptic experiences. We present an application using TacTalk in a popular racing video game, Forza Horizon 5. Through an empirical study, we find that tracking user preference profiles may improve TacTalk's ability to cater to individual differences, and that TacTalk is more usable than an existing slider-based personalization tool. A thematic analysis of participant interviews reveals an archetypal process of conversational personalization - starting with real-world experiences and domain-specific metaphors, then subsequently inspecting specific aspects of the experience including in-game events and the game controller.2025AMAnchit Mishra et al.In-Vehicle Haptic, Audio & Multimodal FeedbackConversational ChatbotsGame UX & Player BehaviorCHI
Learn, Explore and Reflect by Chatting: Understanding the Value of an LLM-Based Voting Advice Application ChatbotVoting advice applications (VAAs), which have become increasingly prominent in European elections, are seen as a successful tool for boosting electorates' political knowledge and engagement. However, VAAs' complex language and rigid presentation constrain their utility to less-sophisticated voters. While previous work enhanced VAAs' click-based interaction with scripted explanations, a conversational chatbot's potential for tailored discussion and deliberate political decision-making remains untapped. Our exploratory mixed-method study investigates how LLM-based chatbots can support voting preparation. We deployed a VAA chatbot to 331 users before Germany's 2024 European Parliament election, gathering insights from surveys, conversation logs, and 10 follow-up interviews. Participants found the VAA chatbot intuitive and informative, citing its simple language and flexible interaction. We further uncovered VAA chatbots' role as a catalyst for reflection and rationalization. Expanding on participants' desire for transparency, we provide design recommendations for building interactive and trustworthy VAA chatbots.2025JZJianlong Zhu et al.Conversational ChatbotsHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityCHI
Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual AgentsWe investigated the challenges of mitigating response delays in free-form conversations with virtual agents powered by Large Language Models (LLMs) within Virtual Reality (VR). For this, we used conversational fillers, such as gestures and verbal cues, to bridge delays between user input and system responses and evaluate their effectiveness across various latency levels and interaction scenarios. We found that latency above 4 seconds degrades quality of experience, while natural conversational fillers improve perceived response time, especially in high-delay conditions. Our findings provide insights for practitioners and researchers to optimize user engagement whenever conversational systems' responses are delayed by network limitations or slow hardware. We also contribute an open-source pipeline that streamlines deploying conversational agents in virtual environments.2025MMMykola Maslych et al.Social & Collaborative VRHuman-LLM CollaborationCHI
Transparent Conversational Agents: The Impact of Capability Communication on User Behavior and Mental Model AlignmentWhen a user interacts with a conversational agent for the first time, they may not be aware of the agent's capabilities, leading to suboptimal use or interaction breakdowns. To avoid a mismatch with the actual capabilities, the agent's capabilities have to be made transparent to the user. To investigate whether communication of an agent's capabilities during interactions enhances transparency and improves the user's mental model, we conducted a user study with 56 participants. Each participant had three speech-based interactions with an agent that communicated its capabilities or an agent that did not. Our results suggest that the communication led to a change in user behavior with significantly longer utterances. However, the users' mental models of the agent's capabilities were not significantly different between the conditions. Participants were able to significantly improve their knowledge of the agent's capabilities by aligning their mental model over time in both conditions.2025MRMerle M. Reimann et al.Agent Personality & AnthropomorphismExplainable AI (XAI)Privacy by Design & User ControlCHI
DesignMinds: Enhancing Video-Based Design Ideation with a Vision-Language Model and a Context-Injected Large Language ModelIdeation is a critical component of video-based design (VBD), where videos serve as the primary medium for design exploration and inspiration. The emergence of generative AI offers considerable potential to enhance this process by streamlining video analysis and facilitating idea generation. In this paper, we present DesignMinds, a prototype that integrates a state-of-the-art Vision-Language Model (VLM) with a context-enhanced Large Language Model (LLM) to support ideation in VBD. To evaluate DesignMinds, we conducted a between-subject study with 35 design practitioners, comparing its performance to a baseline condition. Our results demonstrate that DesignMinds significantly enhances the flexibility and originality of ideation, while also increasing task engagement. Importantly, the introduction of this technology did not negatively impact user experience, technology acceptance, or usability.2025THTianhao He et al.Generative AI (Text, Image, Music, Video)Human-LLM CollaborationGraphic Design & Typography ToolsCHI
WatchWithMe: LLM-Based Interactive Guided Watching of Review VideosVideos are a popular way for viewers to follow topics of interest. In areas such as product and technology reviews, videos often present in-depth perspectives in a compact fashion, driving viewers to look for additional explanations. We propose WatchWithMe, an automatic approach that provides viewers in-context guided watching during video playback. Powered by large language models, WatchWithMe generates guided materials from the video transcript as if creating a reading guide, including summaries, highlights, and question prompts. WatchWithMe reveals relevant information responsive to the spoken content in a review video. Viewers skim and prompt in our text-based conversational UI, to which we automatically expand the video viewing context to the model for contextual responses. We evaluated WatchWithMe with public videos and collected feedback from 20 participants. Findings showed that our method encouraged viewers to seek out viewpoints or confirmations related to the video topics.2025PCPeggy Chi et al.Conversational ChatbotsHuman-LLM CollaborationCHI
NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning ExperiencesGenerative AI is reshaping education by enabling personalized, on-demand learning experiences. However, current AI systems lack awareness of the learner’s cognitive state, limiting their adaptability. In parallel, electroencephalography (EEG)-based neuroadaptive systems have shown promise in enhancing engagement through real-time physiological feedback. This paper introduces NeuroChat, a neuroadaptive AI tutor that integrates real-time EEG-based engagement tracking with a large language model to adapt its conversational responses. By continuously monitoring learners’ cognitive engagement, NeuroChat dynamically adjusts content complexity, tone, and response style in a closed-loop interaction. In a within-subjects study (n=24), NeuroChat significantly increased both EEG-measured and self-reported engagement compared to a non-adaptive chatbot. However, no significant differences in short-term learning outcomes were observed. These findings demonstrate the feasibility of real-time brain–AI interaction for education and highlight opportunities for deeper personalization, longer-term adaptation, and richer learning assessment in future neuroadaptive systems.2025DBDunya Baradari et al.Brain-Computer Interface (BCI) & NeurofeedbackHuman-LLM CollaborationIntelligent Tutoring Systems & Learning AnalyticsCHI
When AI Joins the Negotiation Table: Evaluating AI as a ModeratorNegotiation is a crucial decision-making process where parties seek to resolve differences and optimize outcomes. While prior research has focused on maximizing negotiation outcomes, fostering a collaborative atmosphere is essential for long-term relationship-building. This study explores the role of AI-assisted moderation in negotiations that emulate high-stress environments. We developed a text-based AI moderator and evaluated its usability and effectiveness in a two-phase study: a pilot study with 14 participants followed by a final user study with 16 participants. To provide an initial point of comparison, we assessed trust, respect, and equitability in AI-moderated versus non-moderated negotiations. Quantitative findings indicate a negative effect of AI-assisted moderation on relationship-building, while qualitative insights suggest that AI moderation fosters collaboration. However, the cognitive load of text-based facilitation hinders its effectiveness. These results highlight the importance of seamless AI integration and contribute to the broader discourse on AI’s role in behavior change and mediated communication.2025CKCharlotte Kobiella et al.Agent Personality & AnthropomorphismAI-Assisted Decision-Making & AutomationCHI
Hearing Ambiguity: Exploring Beyond-Gender Impressions of Artificial Ambiguous VoicesVoice perception plays a fundamental role in all types of interactions, from human-to-human communication to human-technology interaction. When it comes to technology, we sometimes have the option to choose the type of voice we want to hear. But why is the default (almost) always a feminine or masculine voice? In this research, we evaluated user perceptions of gender-ambiguous voices, a relatively unexplored option. In our novel comparative study, we evaluated six gender-ambiguous voices with participants of diverse gender identities (men, women, and non-binary individuals), with 74 participants in each group. Additionally, half of the participants were told in advance that the voices had been designed to be gender-ambiguous, and half were not. We aimed to move beyond subjective perceptions of voice gender by exploring how such voices are perceived across different dimensions: trustworthiness, appeal, comfort, anthropomorphism, and aversion. Our findings reveal that while men and women had similar perceptions, non-binary participants rated the voices more negatively, with lower trust and higher aversion. Interestingly, priming participants about the voices' ambiguity did not significantly affect overall perceptions, though it increased critical evaluations from non-binary individuals. These findings contribute to growing research on gender-ambiguous voices by providing perceptual comparisons of multiple voices and highlighting the need for more inclusive voice designs that appeal to non-binary users.2025MCMartina De Cet et al.Voice User Interface (VUI) DesignMultilingual & Cross-Cultural Voice InteractionAgent Personality & AnthropomorphismCHI
ActionaBot: Structuring Metacognitive Conversations towards In-Situ Awareness in How-To Instruction FollowingPeople often rely on shared procedures and tips to handle unfamiliar tasks, but following tutorials can be challenging. Individuals may skip steps, alter actions, or miss information, leading to mistakes or task failure. Tutorials are often based on personal experiences and may omit important details, which vary with context. Furthermore, when others attempt to follow these tutorials, differing situations can make it hard to follow the steps or track progress. Inspired by how coworkers discuss work status and work approach in-situ through metacognitive conversations, we propose Action-a-bot, a chatbot framework that transforms static tutorials into interactive, structural, step-by-step guidance. Action-a-bot drives users to focus on each step, review what they’ve completed, and anticipate the next steps, while adapting actions and solving problems. Our study explores how human-chatbot interaction can improve task completion and make tutorials more actionable by increasing user engagement and awareness of the work situation. We discuss the potential of chatbots in supporting instructional communication and task execution.2025QZQingxiaoyang Zhu et al.Conversational ChatbotsAgent Personality & AnthropomorphismPrototyping & User TestingCHI