Understanding user enjoyment is crucial in human-robot interaction (HRI), as it can impact interaction quality and influence user acceptance and long-term engagement with robots, particularly in the context of conversations with social robots. However, current assessment methods rely solely on self-reported questionnaires, failing to capture interaction dynamics. This work introduces the Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES), a novel 5-point scale to assess user enjoyment from an external perspective (e.g. by an annotator) for conversations with a robot. The scale was developed through rigorous evaluations and discussions among three annotators with relevant expertise, using open-domain conversations with a companion robot that was powered by a large language model, and was applied to each conversation exchange (i.e. a robot-participant turn pair) alongside overall interaction. It was evaluated on 25 older adults' interactions with the companion robot, corresponding to 174 minutes of data, showing moderate to good alignment between annotators. Although the scale was developed and tested in the context of older adult interactions with a robot, its basis in general and non-task-specific indicators of enjoyment supports its broader applicability. The study further offers insights into understanding the nuances and challenges of assessing user enjoyment in robot interactions, and provides guidelines on applying the scale to other domains and populations. The dataset is available online.

The emergence of Large Language Models (LLMs) has opened new possibilities for language learning through conversational interaction with chatbots. Yet, little empirical evidence exists on how students experience such interactions and how corrective feedback should be provided. Research suggests that immediate corrective feedback is generally more effective than delayed feedback. Nevertheless, learners' perception of this effectiveness and their preferences for feedback timing, particularly in the domain of Computer-Assisted Language Learning (CALL), remain underexplored. This study investigates the feasibility of providing immediate feedback and examines the impact of feedback timing on user experience and grammar learning gains in English. An in-the-wild experiment was conducted with 66 L2 English learners, who integrated chatbot sessions into their English course as an extracurricular activity over one semester. Participants were randomly assigned to two groups receiving feedback either during or after the conversation. Findings reveal no significant difference in learning gains, but immediate feedback enhanced user experience, leading to overall positive perceptions of the chatbot. Additionally, we explore users' perceptions of the chatbot's social role and personality, offering a roadmap for future enhancements. These results provide valuable insights into the potential of LLMs and chatbots for language learning.

In Autonomous Public Transport (APT), particularly with shuttle buses, passengers travel in smaller, more intimate vehicles—and in the future, such vehicles may operate without an authoritative driver or host. This setup may lead to potential safety concerns, as passengers are left alone together. Additionally, this future absence of a driver or host means that there is no one to address questions or uncertainties that may arise. One proposed solution is introducing a robot onboard the bus, serving a similar role to a human host. To explore this solution, an experiment was conducted in Barkarby, Stockholm, Sweden. Passengers, generally unfamiliar with APT or social robots, experienced two short rides on a bus equipped with either an embodied Furhat robot as the host or a disembodied voice agent in the ceiling. Data were collected from passenger-agent interactions, post-questionnaires, and semi-structured focus group interviews. Results indicate a division in passenger preferences, with some favoring the robot and others the voice assistant. Passengers asked more questions to the robot, suggesting a clearer affordance for interaction. While the questionnaires did not show significant differences, passenger behaviors indicated that they anthropomorphized the robot more. The interviews revealed that passengers felt more secure with a human operator and doubted the robot’s authority during incidents with aggressive passengers or accidents. Our findings show that social robots can help make autonomous buses feel more welcoming and interactive. Future APT systems have many design issues that need to be resolved before riders can find them safe and appropriate to use, and social robots can play a role in resolving such issues—both the ones we see today, and potentially ones that will appear in the future.

Objective: To explore whether an AI-enhanced social robotic virtual patient (VP) platform reinforces empathetic behaviour patterns in medical students compared with a traditional computer-based platform. Methods: Twenty-three sixth-semester medical students from Karolinska Institutet participated in semi-structured interviews following VP encounters with the Social AI-enhanced Robotic Interface (SARI) and, as a comparator, the computer-based Virtual Interactive Case system (VIC). Additionally, 178 students evaluated the VP platforms in empathetic training quantitatively using categorical nominal variables and a visual analogue scale (VAS), with a score of 0 indicating full preference for SARI and 10 full preference for VIC. Interview data were thematically analysed, and quantitative preferences were compared using the Fisher’s exact test with Monte Carlo simulation and the Wilcoxon signed-rank test. Results: Thematic analysis yielded five major themes wherein students consistently reported that SARI facilitated greater empathetic engagement through multimodal interaction, ability to express emotions, and real-time communication adaptability. Quantitative analysis demonstrated a higher preference for SARI versus VIC (78% versus 6%; OR: 190.4; 95% CI: 76.8–472.0; p < 0.001), which remained consistent across subgroups of interest, i.e., female and male students, with and without prior experience in VPs, and students first exposed to SARI or first exposed to VIC. VAS data also showed a preference for SARI versus VIC (median: 2.00; IQR: 1.00–4.00; W: 738.5; r: 0.70; p < 0.001). Conclusion: Our AI-enhanced social robotic VP platform was superior to a traditional computer-based VP platform in fostering empathetic engagement in medical students through enhanced authenticity and interactivity, supporting its potential to supplement clinical rotations.

Autonomous Public Transport (APT) is a developing innovation with the potential to transform our current transport systems. APT can potentially provide an affordable, safe, and convenient travel solution for daily travel. Recent studies in the field of APT have identified that among several concerns, safety, and trust played an important role in positively impacting user acceptance of APT. A potential solution to address some of these concerns is integrating “Social Robots” into APT. By creating agents that are designed to be distinct entities from the autonomous vehicle, designed to interact with humans socially and intuitively, these robots can potentially improve passenger experiences and the perceived level of safety for the passenger. This could help to build trust in the technology. Using existing literature, this paper explores the benefits and drawbacks of integrating social robots into APT, including improving passenger satisfaction, safety, and efficiency, as well as privacy and security issues.

Objective/Background: Integration of virtual patient (VP) cases has traditionally complemented clinical encounters during medical students clinical placements at Karolinska Institutet (KI). This study aimed to assess the educational value of clinical reasoning (CR) training by comparing two platforms: a novel large language model (LLM)-enhanced social robotic VP platform and a conventional computer-based platform within the context of rheumatology.

Methods/summary of work: A qualitative study involved 23 third-year medical students from KI during clinical placements in rheumatology. Each student completed nine VP cases using two distinct platforms: an LLM-enhanced social robotic platform and a computer-based semi-linear platform. Following each case completion, students participated in seminars with consultant rheumatologists to discuss the clinical cases. In-depth interviews assessed students’ self-perceived acquirement of CR skills using the two platforms following all VP cases and their corresponding seminars. Thematic analysis was employed to identify themes and sub-themes.

Results/Summary of results: Thematic analysis revealed three principal themes: authenticity, VP application, and strengths and limitations. Students perceived the social robotic platform more authentic and engaging, particularly through its capacity for interactive communication and emotional expression, collectively delivering a realistic clinical experience. The platform demonstrated effectiveness in facilitating active learning processes, hypothesis formation, and adaptive thinking skills. However, notable limitations were identified, including the absence of physical examination capabilities and instances of mechanical dialogue patterns.

Conclusion: In our setting of undergraduate medical education placements within rheumatology, an LLM-enhanced social robotic VP platform offered a more authentic and interactive learning experience compared to a conventional computer-based platform. Despite some limitations, the social robotic platform shows promise in training CR skills, communication, and adaptive thinking. AI-enhanced Social robotic VPs may prove useful learning modalities for exposing medical students to diverse, highly interactive patient simulations.

Background: Virtual patient (VP) simulations can be used to practice clinical reasoning (CR) in controlled learning environments. Traditional computer-based VP platforms often lack the authenticity and interactivity required for effective CR training. Artificial intelligence (AI)–enhanced social robotic VPs can enhance realism and engagement; however, quantitative evidence comparing them with conventional VP platforms remains limited.

Objective: We compared medical students’ experience of an AI-enhanced social robotic versus a conventional computer-based VP platform regarding the extent to which the design characteristics of the respective platform facilitate CR skill training.

Methods: This observational crossover cohort study involved 178 sixth-semester medical students at Karolinska Institutet, Stockholm, Sweden (response rate: 42.3%; 178 of 421 invited students; Spring 2024-Spring 2025), who experienced both a large language model–enhanced social robotic VP platform supporting dialogue (social artificial intelligence–enhanced robotic interface [SARI]) and a conventional computer-based VP platform (virtual interactive case [VIC]) during their clinical rotation within rheumatology. Platform order was determined by clinical rotation scheduling. VP design was evaluated using a validated questionnaire across 5 domains: authenticity, professional approach, coaching quality, learning effects, and overall judgment. Students’ CR training preferences were assessed using categorical responses and a Visual Analogue Scale, where a lower score favored SARI and a score of 5 indicated equal preference between platforms.

Results: SARI outperformed VIC across all 5 VP design domains. Students rated SARI higher for authenticity (median 4.0, IQR 3.5-4.5 vs 3.0, IQR 2.5-3.5; P<.001), professional approach (median 4.5, IQR 4.0-4.8 vs 4.0, IQR 3.5-4.5; P<.001), coaching quality (median 4.3, IQR 4.0-4.7 vs 4.0, IQR 3.7-4.7; P<.001), learning effect (median 4.4, IQR 4.0-5.0 vs 4.0, IQR 3.5-4.5; P<.001), and overall judgment (median 5.0, 4.0-5.0 vs 4.0, IQR 4.0-5.0; P<.001). Students strongly preferred SARI for CR training (72% vs 14%; odds ratio [OR] 27.1, 95% CI 14.3-53.7; P<.001), with Visual Analogue Scale scores confirming this preference (median 3.0, IQR 2.0-5.0; P<.001). Preferences were consistent across most subgroups (sex, prior VP experience, and platform order); in 2 subgroups, the difference was not significant, that is, students with prior VP experience (62% vs 38%; OR 2.6; 95% CI 0.8-8.9; P=.11) and students first introduced to VIC (55% vs 45%; OR 1.5; 95% CI 0.7-2.9; P=.33).

Conclusions: Our findings provide the first quantitative evidence that AI-enhanced social robotic VPs offer superior design characteristics than conventional computer-based platforms for CR training in medical education. These results support the use of AI-driven social robots for VP simulations to better prepare medical students for real clinical encounters, and warrant future research on objective CR skill outcomes and long-term transfer to clinical practice. Unlike previous qualitative studies examining each platform separately, this study provides the first quantitative comparison of design characteristics between AI-enhanced social robotic and conventional computer-based VPs.

Turn-taking is a fundamental aspect of conversation, but current Human-Robot Interaction (HRI) systems often rely on simplistic, silence-based models, leading to unnatural pauses and interruptions. This paper investigates, for the first time, the application of general turn-taking models, specifically TurnGPT and Voice Activity Projection (VAP), to improve conversational dynamics in HRI. These models are trained on human-human dialogue data using self-supervised learning objectives, without requiring domain-specific fine-tuning. We propose methods for using these models in tandem to predict when a robot should begin preparing responses, take turns, and handle potential interruptions. We evaluated the proposed system in a within-subject study against a traditional baseline system, using the Furhat robot with 39 adults in a conversational setting, in combination with a large language model for autonomous response generation. The results show that participants significantly prefer the proposed system, and it significantly reduces response delays and interruptions.

Throughout our lives, we interact daily in conversations with our friends and family, covering a wide range of topics, known as open-domain dialogue. As we age, these interactions may diminish due to changes in social and personal relationships, leading to loneliness in older adults. Conversational companion robots can alleviate this issue by providing daily social support. Large language models (LLMs) offer flexibility for enabling open-domain dialogue in these robots. However, LLMs are typically trained and evaluated on textual data, while robots introduce additional complexity through multi-modal interactions, which has not been explored in prior studies. Moreover, it is crucial to involve older adults in the development of robots to ensure alignment with their needs and expectations. Correspondingly, using iterative participatory design approaches, this paper exposes the challenges of integrating LLMs into conversational robots, deriving from 34 Swedish-speaking older adults' (one-to-one) interactions with a personalized companion robot, built on Furhat robot with GPT-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}3.5. These challenges encompass disruptions in conversations, including frequent interruptions, slow, repetitive, superficial, incoherent, and disengaging responses, language barriers, hallucinations, and outdated information, leading to frustration, confusion, and worry among older adults. Drawing on insights from these challenges, we offer recommendations to enhance the integration of LLMs into conversational robots, encompassing both general suggestions and those tailored to companion robots for older adults.