Fitness tracking platforms increasingly integrate generative AI to interpret activity data, such as Strava’s Athlete Intelligence. These integrations raise questions about how athletes engage with AI-supported fitness self-tracking. We analyzed 297 Reddit threads and 5,692 comments from r/Strava following the company’s launch of AI features to examine user reactions to AI-generated fitness feedback. Our findings revealed four recurring tensions: (1) numerical evaluation versus contextual understanding; (2) isolated session summaries versus ongoing training narratives; (3) a fixed AI tone versus diverse emotional states; and (4) a single AI voice versus different athletic types. Across these tensions, users resisted AI feedback that constrained interpretations of their own lived experiences. These findings shed light on the implicit challenges of integrating AI into self-tracking platforms. We conclude with implications for the design of AI-supported self-tracking systems that preserve interpretive openness and user agency.

As conversational agents become increasingly common in behaviour change interventions, understanding optimal feedback delivery mechanisms becomes increasingly important. However, choosing a style that both lessens psychological reactance (perceived threats to freedom) while simultaneously eliciting feelings of surprise and engagement represents a complex design problem. We explored how three different feedback styles: Direct, Politeness, and Verbal Leakage (slips or disfluencies to reveal a desired behaviour) affect user perceptions and behavioural intentions. Matching expectations from literature, the Direct chatbot led to lower behavioural intentions and higher reactance, while the Politeness chatbot evoked higher behavioural intentions and lower reactance. However, Politeness was also seen as unsurprising and unengaging by participants. In contrast, Verbal Leakage evoked reactance, yet also elicited higher feelings of surprise, engagement, and humour. These findings highlight that effective feedback requires navigating trade-offs between user reactance and engagement, with novel approaches such as Verbal Leakage offering promising alternative design opportunities.

Despite growing recognition that responsible AI requires domain knowledge, current work on conversational AI primarily draws on clinical expertise that prioritises diagnosis and intervention. However, much of everyday emotional support needs occur in non-clinical contexts, and therefore requires different conversational approaches. We examine how chaplains, who guide individuals through personal crises, grief, and reflection, perceive and engage with conversational AI. We recruited eighteen chaplains to build AI chatbots. While some chaplains viewed chatbots with cautious optimism, the majority expressed limitations of chatbots’ ability to support everyday well-being. Our analysis reveals how chaplains perceive their pastoral care duties and areas where AI chatbots fall short, along the themes of Listening, Connecting, Carrying, and Wanting. These themes resonate with the idea of attunement, recently highlighted as a relational lens for understanding the delicate experiences care technologies provide. This perspective informs chatbot design aimed at supporting well-being in non-clinical contexts.

Figure 1: The Prompt Machine, a tangible learning tool for integrated AI in education.(A) Pupils write assignments.(B) Pupils input their written assignments.(C) Pupils modify their texts using tangible prompt cubes.(D) Pupils receive physical print outs of modified texts.(E) Teachers facilitate reflections and discussions about modified texts with pupils.

General practitioner (GP) consultations are the typical starting point for a patient's healthcare journey.Here, GPs aim to support and inform patients to enable a shared decision-making process.In this work we explore how an interactive chatbot, designed to prepare patients for their GP consultation, is perceived by GPs to impact patient consultations, patient-GP interaction, and their work.We conducted an in-depth evaluation and interview with 15 GPs from 12 different practices.Our findings provide insights into common challenges in shared decision-making, GP perspectives on the role of chatbots in preparing patients, and how chatbot technology could impact and transform general practice.Finally, we reflect on patient and GP agency in shared decision-making and the impact of technology on this complex relationship.

Intertemporal reflection, flexibly thinking forward and backward in time, is vital for one's future planning.Yet, cultivating intertemporal reflection about encountering difficult futures, e.g., developing a progressive cognitive condition like dementia, can be challenging.We assessed people's attitudes towards dementia following conversing with a chatbot presented as either neurotypical or simulating dementia symptoms.While neither the chatbot's presentation nor the framing of participants' future selves impacted attitudes toward dementia, it influenced participants' experiences.When framed as future selves, the chatbot evoked a strong emotional connection, leading to reflection on aging, particularly with the chatbot simulating dementia symptoms.Participants interacting with the chatbot framed as a stranger with simulated symptoms often felt frustrated, especially when they had a task-oriented mindset.Chatbots can be promising tools for prompting reflections on challenging futures, such as dementia, although their effectiveness varies due to the tensions between simulated cognitive decline and expectations for effective communication.

People are increasingly relying on technology for self-care, including, more recently, seeking help through conversational interfaces driven by large language models (LLMs). Yet, how interaction with LLMs has impacted people’s self-care processes is not well understood. Therefore, we collected 405 user stories posted on Reddit about using LLMs for self-care. We identified four key themes on how people use LLMs for this purpose: Letting go , Finding comfort , Building up , and Reflecting on . We interviewed twelve counsellors to capture their perspectives on this practice, given their professional expertise and understanding of healthy self-care practices. Our results show that counsellors recognised several benefits, such as using LLMs as stepping stones or springboards towards improved self-care. They also highlighted several areas of concern, such as unintended consequences that might negatively affect users. We discuss the dissonance around how the early adopters of LLMs appropriate this technology to care for themselves, how counsellors see such usage, and outline implications of using LLMs as a technology for self-care. • We present an analysis of 405 LLM self-care stories. • We used stories representative of themes in interviews with diverse counsellors. • We discuss user and counsellor perspectives on LLMs as self-care technologies.

Individuals are increasingly interested in and responsible for assessing their own health. This study evaluates a fictional AI dermatologist for assistance in the self-assessment of moles. Building on the Signalling Theory, we tested the effect of textual descriptions provided by a virtual dermatologist, as manipulated across ‘Ability’, ‘Integrity, ’ and ‘Benevolence’, along with the clinical assessment, ‘benign’ or ‘malignant’, affect users’ trust in the aforementioned trust pillars. Our study (N = 40) follows a 2 (Ability low/high) × 2 (Integrity low/high) × 2 (Benevolence low/high) × 2 (mole assessment benign/malignant) within-subject factorial design. Our results demonstrate that we can successfully influence perceptions of ability and benevolence by manipulating the corresponding aspects of trust but not perceived integrity. Further, in the case of a malignant assessment, participants’ perception of trust increased across all aspects. Our results provide insights into the design of AI support systems for sensitive use cases, such as clinical self-assessments.

Users ask large language models (LLMs) to help with their homework, for lifestyle advice, or for support in making challenging decisions. Yet LLMs are often unable to fulfil these requests, either as a result of their technical inabilities or policies restricting their responses. To investigate the effect of LLMs denying user requests, we evaluate participants’ perceptions of different denial styles. We compare specific denial styles (baseline, factual, diverting, and opinionated) across two studies, respectively focusing on LLM’s technical limitations and their social policy restrictions. Our results indicate significant differences in users’ perceptions of the denials between the denial styles. The baseline denial, which provided participants with brief denials without any motivation, was rated significantly higher on frustration and significantly lower on usefulness, appropriateness, and relevance. In contrast, we found that participants generally appreciated the diverting denial style. We provide design recommendations for LLM denials that better meet peoples’ denial expectations.

Successful collaboration in computer-mediated teams requires awareness among group members of each other’s knowledge, skills, and goals. Large Language Models (LLMs) can play a mediating role in establishing and maintaining this awareness among group members. In an in-situ study, we explored the impact of an LLM-based chatbot on cognitive and social group awareness through a distributed text-based group task. We instructed participants (N = 48) to complete a travel-planning task in sixteen groups of three, with each member given conflicting goals. Each chat was complemented by a chatbot that could be asked for assistance. Through a survey and semi-structured interview, we gained insight into participants’ deliberations on the task and the chatbot’s role. We found that the chatbot’s presence helped increase group awareness as users are forced to clearly and transparently formulate their intentions when prompting the chatbot. The chatbot’s ability to provide suggestions that compromise between user goals based on the chat history helped participants reach a consensus. We present implications for the design of chatbots for collaborative settings.

Young adults may feel embarrassed when disclosing sensitive information to their parents, while parents might similarly avoid sharing sensitive aspects of their lives with their children. How to design interactive interventions that are sensitive to the needs of both younger and older family members in mediating sensitive information remains an open question. In this paper, we explore the integration of large language models (LLMs) with social robots. Specifically, we use GPT-4 to adapt different Robot Communication Styles (RCS) for a social robot mediator designed to elicit self-disclosure and mediate health information between parents and young adults living apart. We design and compare four literature-informed RCS: three LLM-adapted (Humorous, Self-deprecating, and Persuasive) and one manually created (Human-scripted), and assess participant perceptions of Likeability, Usefulness, Helpfulness, Relatedness, and Interpersonal Closeness . Through an online experiment with 183 participants, we assess the RCS across two groups: adults with children (Parents) and young adults without children (Young Adults). Our results indicate that both Parents and Young Adults favoured the Human-scripted and Self-deprecating RCS as compared to the other two RCS. The Self-deprecating RCS furthermore led to increased relatedness as compared to the Humorous RCS. Our qualitative findings reveal challenges people have in disclosing health information to family members, and who normally assumes the role of family facilitator-two areas in which social robots can play a key role. The findings offer insights for integrating LLMs with social robots in health-mediation and other contexts involving the sharing of sensitive information.

Despite repeated reports of socially inappropriate and dangerous chatbot behaviour, chatbots are increasingly used as mental health services in providing support for young people. In sensitive settings as such, the notion of perceived moral agency (PMA) is crucial, given its critical role in human-human interactions. In this paper, we investigate the role of PMA in human-chatbot interactions. Specifically, we seek to understand how PMA influence the perception of trust, likeability, and perceived safety of chatbots for mental health across two distinct age groups. We conduct an online experiment(N = 279)to evaluate chatbots with low and high PMA as targeted towards teenagers and adults. Our results indicate increased trust, likeability, and perceived safety in mental health chatbots displaying high PMA. A qualitative analysis revealed four themes, assessing participants' expectations of mental health chatbots in general, as well as targeted towards teenagers: Anthropomorphism, Warmth, Sensitivity, and Appearance manifestation. We show that PMA plays a crucial role in influencing the perceptions of chatbots and provide recommendations for designing socially appropriate mental health chatbots.

State-of-the-art intelligent and interactive agents, such as Alexa or Siri, often present overly conforming behaviour during interactions with humans. This can result in a misalignment between end-user expectations and agent behaviour. To overcome this barrier in human-AI interactions, we introduce the Critical Friend (CF), a conceptual idea that guides critical behaviour in human-human interactions. We present our results as a formal understanding that can be described through description logic and utilised for reasoning capabilities, enabling implementations of the CF as an intelligent interactive agent.