Rising unemployment and rapid advances in AI have disrupted the job market, making it more important than ever for job seekers to present themselves effectively-starting with their resumes. While prior research has explored resume evaluation, little attention has been given to leveraging AI for delivering personalized, detailed, instant and actionable feedback. We present ResumeGenAI, an online platform designed to support job seekers create resumes, personalize them to specific job roles, compare them, and seek help through a conversational interface. Through a three-week study (N = 34), we evaluated its effectiveness in helping job seekers build resumes. We found that the platform significantly improved resume quality in areas such as skills, grammar, and overall impact, as assessed by expert reviewers. Participants also highlighted features like resume personalization, export analysis, and resume templates as particularly valuable.
In 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 customizabilityallowing 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.
We sought to understand the effectiveness of conversational user interfaces (CUIs) in answering questions relevant to immigrants, refugees, and visible minority users navigating key government services. To do this, we conducted a comparative analysis on the responses of ChatGPT, Gemini, and Microsoft Copilot to understand the characteristics, similarities, and differences in responses. We found that these revolved around clarity, tone, depth of information, use of official sources, and accessibility and inclusivity. Furthermore, we observed that ChatGPT provided detailed, userfriendly responses, Microsoft Copilot provided concise information but lacked contextual depth, and Gemini offered users a structured, step-by-step response to navigate the different services. We conclude with design implications for developing more inclusive and effective government-focused CUIs. Future work will involve conducting usability testing to collect additional data, investigate the impact of these variations, and validate the accuracy and effectiveness of the CUIs' responses.
Conversational 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.
We 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.
Climate action is difficult to persuade because we tend to perceive climate change as remote and disconnected from daily life. Instead of traditional informational engagements, game-based interventions can create narratives that immerse the visitor in situations where their actions have tangible consequences. To make these narratives engaging, we used a speculative scenario of an alien stumbling upon social media to obliquely address climate change through a text-based adventure game installation. Mimicking visitors' natural dialogue in social media apps, we designed an LLMbased chatbot with knowledge of post-climate devastated world that mirrors our own planet Earth. In discovering the world's downfall through interactive chatting and posted images, players begin to realize that their own actions can make a difference on impacts of climate change in this distant world, fostering pro-environmental attitudes. Previously published at CHI, this game installation demonstrates the potential of LLM-based creative narratives in exploring speculative worlds driving social change.
Although exhibiting great potential in enabling seamless communication between humans and conversational agents, large vocabulary recognition is still challenging for silent speech interfaces. In this research, we propose a novel interaction technique that combines silent speech and typing to enable more efficient text entry while preserving privacy. This technique allows users to use abbreviated phrase input while still ensuring high accuracy by leveraging visual information. By fine-tuning a large language model with a visual speech encoder, we condition the models to decode the speech content with word initials as hints. Evaluations on existing datasets show that our model can reduce the Word Error Rate from 20.3% to 9.19%, compared to state-of-the-art visual speech recognition models. Results from a user study demonstrated significant improvements in input speed and keystroke saving. Participants reported that our prototype, LipType, leads to an overall lower perceived workload, particularly in the effort and physical demand dimension.
Since the early development of conversational agents (CAs), humanlikeness 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.
New AI developments are enabling CUIs to take on diverse social roles to facilitate interactions with humans. To support such increasingly complex and social interactions, researchers draw from Theory of Mind (ToM)-our ability to attribute mental states like intentions, goals, and emotions to ourselves and others for seamless communication. Given ToM's importance in human interaction, AI and HCI researchers explore both building ToM-like capabilities in CUIs and understanding how humans attribute mental states to CUIs. These perspectives form the emerging paradigm of Mutual Theory of Mind (MToM) in human-CUI interaction, where both parties iteratively interpret each other's internal states. Building on the success of the 1st ToMinHAI workshop at CHI 2024, this installment invites researchers from AI, ML, HCI, and related fields to discuss ToM in human-CUI interactions to inform the future design of conversational AI.
This study explores user responses to two distinctive data units /components (i.e., text transcripts and voice profiles) identified within a single data source (i.e., voice recordings). Participants were randomly assigned to one of three scenarios in which Amazon Alexa presented data used for personalization differently (i.e., voice recordings vs. voice profiles + text transcripts vs. text transcripts only). Users' perceived information sensitivity across different data types was also surveyed. The main findings include that users distinguish voice interaction data from other types of personal information, and those who report higher data sensitivity over voice interaction data assess its privacy risks higher. Furthermore, only using text transcripts (without voice profiles) for personalization alleviated users' perceived privacy risk. This study informs future privacy implications for data transparency and design to incorporate modality differences in user data collected, stored, and processed towards personalization of voice-enabled CUIs (like OpenAI ChatGPT and Google Gemini) that extend traditional voice assistants (like Amazon Alexa and Apple Siri).
The widespread adoption of Artificial Intelligence (AI) has significantly influenced how students engage with learning, driving increased use of AI tools for both academic and non-academic purposes. This study explores students' motivations for using AI tools and examines the relationship between usage patterns and socioeconomic backgrounds. Drawing on 15 semi-structured interviews, our research investigates students' perceptions, learning motivations, and concerns from a Global South perspective. The findings reveal a user-friendly and multifaceted engagement between students and AI chatbots-spanning academic assistance, assessments, photo editing, emotional expression, and entertainment suggestions. While students generally appreciate the benefits of these tools, some expressed privacy concerns rooted in skepticism about data deletion, even when such requests are made to AI platforms. This study provides valuable insights into how students in the Global South interact with AI tools, shedding light on both their functional use and user trust issues.
People 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.
When 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.
Recent 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.
Postpartum period is a crucial time for physical and mental adjustment for a mother, which can worsen through increased demands and mental load. In Brazil, as in many Latin American countries, the unequal division of childcare responsibilities increases mothers' risks of postpartum depression and anxiety while preventing mothers from focusing on their recovery. While today's Voice Assistants (VAs) is promising to offer a hands-free, eyes-free and on-demand support, it remains unclear how VAs can be designed to effectively support mothers and their associated tasks during the postpartum period. To address this challenge, we conducted an online survey study with 55 Brazilian mothers to investigate how VAs support postpartum mothers and their current usage in childcare-related tasks. We identified key challenges preventing VAs from effectively supporting Brazilian mothers, including language barriers, lack of personalized information retrieval, and missing features tailored to postpartum care and early childhood needs. We then proposed a set of design considerations for how VAs could meet mothers' needs for greater adoption in Brazil.
Sexual health is a critical global issue, with African young adults being especially vulnerable. This study presents the design, development and pilot evaluation of a multi-chatbot mobile app combining generative and rule-based solutions, based on the Health Belief Model and Persuasive System Design principles, to educate and motivate African young adults to avoid risky sexual behaviors. Early results showed high usability, positive user experience, strong persuasiveness, and high educational value. Users particularly appreciated the app's cultural elements, gamified modules, and LLM-based generative chatbot. Areas for improvement included UI enhancements and removing barriers to user engagement. This work contributes to advancing knowledge on healthcare chatbots and provides insights into designing mobile health apps for sexual health education and behavior change.
Memory is a key aspect of human interaction, allowing us to remember facts about our interlocutors and personalise the way we interact. This pilot study investigates how a semantic long-term memory affects user assessments of chatbot likeability, perceived intelligence, and perceived safety, addressing limitations in Large Language Model (LLM) memory. We introduce MemoryGraph, a knowledge graph-based memory system for LLMs that enables visual inspection of memories by users. A user study compared interactions with a chatbot under three conditions: no memory, memory alone, and memory with visualisation. User ratings for likeability, intelligence, and safety were recorded. Preliminary findings show that adding a memory without visualisation reduced positive assessments compared to the baseline, whereas combining memory with visualisations improved these ratings. This tentatively suggests that the observability of memory functions influences key user perceptions of memory-augmented conversational AI, although more research is needed to confirm these initial results.
Large Language Models (LLMs) can enhance structured design thinking, yet existing copilot approaches integrate them into human workflows rather than exploring their autonomous potential. This paper investigates how LLM-based communicative AI agents can independently tackle open-ended design problems and how their strengths and limitations inform human-AI collaboration. We iteratively design a system where AI agents play different roles and simulate human design activity through conversational turns. The agents investigate user needs, identify design constraints, and explore the design space, with useful insights emerging from their interactions. To assess reasoning quality, we conducted a human jury evaluation with five HCI researchers and explored potential applications through a contextual inquiry with seven professionals. Our findings demonstrate that integrating human design thinking techniques enhances AI reasoning. AI agents effectively tackle design problems, generating low-novelty yet well-grounded and practical solutions that meet key design requirements.
In daily life, we interact with each other using the social, regional, and ethnic communication styles typical of our local communities. Successful communication further rests on our ability to seamlessly adjust to our interlocutors following the norms and expectations of our local social setting as well as conversational context and goals. However, despite significant advances in speech technology, most artificial speech systems-particularly, most social robots-still use a single, "standard", non-local communication style for all users, social settings and interaction goals. Recent research has shown that when they interact with digital agents, humans transfer and adapt their sociolinguistic behaviours, including communication bias. Despite this, the barriers set up by this inherent communication bias have never been systematically studied for HRI; and the potential benefits to user engagement from socially inclusive, diverse communication styles have not been explored. We argue that social robotics researchers should also consider sociolinguistic factors constraining human interaction. To explore the implications, we describe two hypothetical robots designed to support the local communication style of two regions of the United Kingdom, and we consider the potential sociolinguistic impact each robot might have on its conversational partners and the wider society.
Pro-environmental behavior (PEB) is vital to combat climate change, yet turning awareness into intention and action remains elusive. We explore large language models (LLMs) as tools to promote PEB, comparing their impact across 3,600 participants: real humans (n=1,200), simulated humans based on actual participant data (n=1,200), and fully synthetic personas (n=1,200). All three participant groups faced either personalized chatbots, standard chatbots, or static statements, employing four persuasion strategies (moral foundations, future self-continuity, action orientation, or "freestyle" chosen by the LLM). Results reveal a "synthetic persuasion paradox": synthetic and simulated participants significantly change their post-intervention PEB stance, while human attitudes barely shift. Simulated participants better approximate human behavior but still overestimate effects. This disconnect underscores LLM's potential for pre-evaluating PEB interventions but warns of its limits in predicting human responses. We call for refined synthetic modeling and sustained and extended human trials to align conversational AI's promise with tangible sustainability outcomes.
