Why it's high time we stopped anthropomorphising ants We have long drawn parallels between ants and humans. Now we are comparing the insects to computers. Pollution is making many cities unlivable for their human inhabitants, but it is also tearing ant families and communities apart. Ants recognise each other by sniffing a thin layer of hydrocarbons on the outside of their exoskeletons; each colony has a specific "smell". But a new study reveals that ozone emissions can change the structure of these hydrocarbons.

Artificial Intelligence (AI) chatbots powered by a large language model (LLM) are entering young children's learning and play, yet little is known about how young children construe these agents or how such construals relate to engagement. We examined anthropomorphism of a social AI chatbot during collaborative storytelling and asked how children's attributions related to their behavior and prefrontal activation. Children at ages 5-6 (N = 23) completed three storytelling sessions: interacting with (1) an AI chatbot only, (2) a parent only, and (3) the AI and a parent together. After the sessions, children completed an interview assessing anthropomorphism toward both the AI chatbot and the parent. Behavioral engagement was indexed by the conversational turn count (CTC) ratio, and concurrent fNIRS measured oxygenated hemoglobin in bilateral vmPFC and dmPFC regions. Children reported higher anthropomorphism for parents than for the AI chatbot overall, although AI ratings were relatively high for perceptive abilities and epistemic states. Anthropomorphism was not associated with CTC. In the right dmPFC, higher perceptive scores were associated with greater activation during the AI-only condition and with lower activation during the AI+Parent condition. Exploratory analyses indicated that higher dmPFC activation during the AI-only condition correlated with higher end-of-session "scared" mood ratings. Findings suggest that stronger perceptive anthropomorphism can be associated with greater brain activation related to interpreting the AI's mental states, whereas parent co-presence may help some children interpret and regulate novel AI interactions. These results may have design implications for encouraging parent-AI co-use in early childhood.

The rapid evolution of large language models (LLMs) creates complex bidirectional expectations between users and AI systems that are poorly understood. We introduce the concept of "mutual wanting" to analyze these expectations during major model transitions. Through analysis of user comments from major AI forums and controlled experiments across multiple OpenAI models, we provide the first large-scale empirical validation of bidirectional desire dynamics in human-AI interaction. Our findings reveal that nearly half of users employ anthropomorphic language, trust significantly exceeds betrayal language, and users cluster into distinct "mutual wanting" types. We identify measurable expectation violation patterns and quantify the expectation-reality gap following major model releases. Using advanced NLP techniques including dual-algorithm topic modeling and multi-dimensional feature extraction, we develop the Mutual Wanting Alignment Framework (M-WAF) with practical applications for proactive user experience management and AI system design. These findings establish mutual wanting as a measurable phenomenon with clear implications for building more trustworthy and relationally-aware AI systems.

From 10th to 12th September 2025, Barcelona hosted an academic gathering at the Universitat Oberta de Catalunya: the first Hype Studies Conference, titled "(Don't) Believe the Hype!?" Organised by a transnational, collective research group of scholars and practitioners, the conference drew together researchers, activists, artists, journalists, and technology professionals to examine hype as a significant force shaping contemporary society. Hype Studies is an emerging academic field that analyses how and why excessive expectations form around technologies, ideas, or phenomena, and what effects those expectations have on society, culture, economics, and policy. As the playful brackets around "Don't" in the conference title suggest - both a warning and an invitation to question that warning - the aim of the conference wasn't to simply reject hype, but to understand it. The conference approached hype critically by examining it as a phenomenon with real power and consequences that needs to be understood and questioned. The purpose here was to build collective knowledge about hype, develop better and more concrete theories, share empirical findings, and create an interdisciplinary community whilst advancing the field's scholarship and knowledge.

Robots with anthropomorphic features are increasingly shaping how humans perceive and morally engage with them. Our research investigates how different levels of anthropomorphism influence protective responses to robot abuse, extending the Computers as Social Actors (CASA) and uncanny valley theories into a moral domain. In an experiment, we invite 201 participants to view videos depicting abuse toward a robot with low (Spider), moderate (Two-Foot), or high (Humanoid) anthropomorphism. To provide a comprehensive analysis, we triangulate three modalities: self-report surveys measuring emotions and uncanniness, physiological data from automated facial expression analysis, and qualitative reflections. Findings indicate that protective responses are not linear. The moderately anthropomorphic Two-Foot robot, rated highest in eeriness and "spine-tingling" sensations consistent with the uncanny valley, elicited the strongest physiological anger expressions. Self-reported anger and guilt are significantly higher for both the Two-Foot and Humanoid robots compared to the Spider. Qualitative findings further reveal that as anthropomorphism increases, moral reasoning shifts from technical assessments of property damage to condemnation of the abuser's character, while governance proposals expand from property law to calls for quasi-animal rights and broader societal responsibility. These results suggest that the uncanny valley does not dampen moral concern but paradoxically heightens protective impulses, offering critical implications for robot design, policy, and future legal frameworks.

Many Large Language Model (LLM) chatbots are designed and used for companionship, and people have reported forming friendships, mentorships, and romantic partnerships with them. Concerns that companion chatbots may harm or replace real human relationships have been raised, but whether and how these social consequences occur remains unclear. In the present longitudinal study ($N = 183$), participants were randomly assigned to a chatbot condition (text chat with a companion chatbot) or to a control condition (text-based word games) for 10 minutes a day for 21 days. Participants also completed four surveys during the 21 days and engaged in audio recorded interviews on day 1 and 21. Overall, social health and relationships were not significantly impacted by companion chatbot interactions across 21 days of use. However, a detailed analysis showed a different story. People who had a higher desire to socially connect also tended to anthropomorphize the chatbot more, attributing humanlike properties to it; and those who anthropomorphized the chatbot more also reported that talking to the chatbot had a greater impact on their social interactions and relationships with family and friends. Via a mediation analysis, our results suggest a key mechanism at work: the impact of human-AI interaction on human-human social outcomes is mediated by the extent to which people anthropomorphize the AI agent, which is in turn motivated by a desire to socially connect. In a world where the desire to socially connect is on the rise, this finding may be cause for concern.

There has been extensive prior work exploring how psychological factors such as anthropomorphism affect the adoption of Shared Autonomous Vehicles (SAVs). However, limited research has been conducted on how prompt strategies in large language models (LLM)-powered conversational SAV agents affect users' perceptions, experiences, and intentions to adopt such technology. In this work, we investigate how conversational SAV agents powered by LLMs drive these psychological factors, such as psychological ownership, the sense of possession a user may come to feel towards an entity or object they may not legally own. We designed four SAV agents with varying levels of anthropomorphic characteristics and psychological ownership triggers. Quantitative measures of psychological ownership, anthropomorphism, quality of service, disclosure tendency, sentiment of SAV responses, and overall acceptance were collected after participants interacted with each SAV. Qualitative feedback was also gathered regarding the experience of psychological ownership during the interactions. The results indicate that an SAV designed to be more anthropomorphic and to induce psychological ownership improved users' perceptions of the SAV's human-like qualities, and its responses were perceived as more positive but also more subjective compared to the control conditions. Qualitative findings support established routes to psychological ownership in the SAV context and suggest that the conversational agent's perceived performance may also influence psychological ownership. Both quantitative and qualitative outcomes highlight the importance of personalization in designing effective SAV interactions. These findings provide practical guidance for designing conversational SAV agents that enhance user experience and adoption.

Human autonomy and sense of agency are increasingly recognised as critical for user well-being, motivation, and the ethical deployment of robots in human-robot interaction (HRI). Given the rapid development of artificial intelligence, robot capabilities and their potential to function as colleagues and companions are growing. This systematic literature review synthesises 22 empirical studies selected from an initial pool of 728 articles published between 2011 and 2024. Articles were retrieved from major scientific databases and identified based on empirical focus and conceptual relevance, namely, how to preserve and promote human autonomy and sense of agency in HRI. Derived through thematic synthesis, five clusters of potentially influential factors are revealed: robot adaptiveness, communication style, anthropomorphism, presence of a robot and individual differences. Measured through psychometric scales or the intentional binding paradigm, perceptions of autonomy and agency varied across industrial, educational, healthcare, care, and hospitality settings. The review underscores the theoretical differences between both concepts, but their yet entangled use in HRI. Despite increasing interest, the current body of empirical evidence remains limited and fragmented, underscoring the necessity for standardised definitions, more robust operationalisations, and further exploratory and qualitative research. By identifying existing gaps and highlighting emerging trends, this review contributes to the development of human-centered, autonomy-supportive robot design strategies that uphold ethical and psychological principles, ultimately supporting well-being in human-robot interaction.

Large Language Models (LLMs) increasingly exhibit \textbf{anthropomorphism} characteristics -- human-like qualities portrayed across their outlook, language, behavior, and reasoning functions. Such characteristics enable more intuitive and engaging human-AI interactions. However, current research on anthropomorphism remains predominantly risk-focused, emphasizing over-trust and user deception while offering limited design guidance. We argue that anthropomorphism should instead be treated as a \emph{concept of design} that can be intentionally tuned to support user goals. Drawing from multiple disciplines, we propose that the anthropomorphism of an LLM-based artifact should reflect the interaction between artifact designers and interpreters. This interaction is facilitated by cues embedded in the artifact by the designers and the (cognitive) responses of the interpreters to the cues. Cues are categorized into four dimensions: \textit{perceptive, linguistic, behavioral}, and \textit{cognitive}. By analyzing the manifestation and effectiveness of each cue, we provide a unified taxonomy with actionable levers for practitioners. Consequently, we advocate for function-oriented evaluations of anthropomorphic design.

The question of whether artificial agents (e.g., chatbots and social robots) can replace human therapists has received notable attention following the recent launch of large language models. However, little is known about the processes of change in psychotherapy delivered by artificial agents. To facilitate hypothesis development and stimulate scientific debate, the present article offers the first theoretical framework of the processes of change in psychotherapy delivered by artificial agents. The theoretical framework rests upon a conceptual analysis of what active ingredients may be inherently linked to the presence of human therapists. We propose that human therapists' ontological status as human beings and sociocultural status as socially sanctioned healthcare professionals play crucial roles in promoting treatment outcomes. In the absence of the ontological and sociocultural status of human therapists, we propose what we coin the genuineness gap and credibility gap can emerge and undermine key processes of change in psychotherapy. Based on these propositions, we propose avenues for scientific investigations and practical applications aimed at leveraging the strengths of artificial agents and human therapists respectively. We also highlight the intricate agentic nature of artificial agents and discuss how this complicates endeavors to establish universally applicable propositions regarding the processes of change in these interventions.