Social robots can support children’s emotional skills development through playful interactions, yet skills like emotional self-disclosure remain underexplored. This study investigates the impact of a social robot designed to encourage emotional self-disclosure during an emotion-identification game with children aged 6-10. In a between-subjects design with 28 participants across two local schools, we compared a Reflective condition, where the robot actively encouraged emotional self-disclosure through question-asking and reciprocal sharing, to a Control condition, where the robot did not. Children in the Reflective condition engaged in emotional self-disclosure when prompted, and showed higher engagement than those in Control. Direct question-asking was more effective than reciprocal self-disclosure. Results suggested that children who perceived the robot as kinder disclosed more, whereas those who viewed it as more real disclosed less. These findings highlight the potential of social robots to foster emotional skills in children and inform the design of future child-robot interaction research.

In the era of artificial intelligence, AI-assisted decision-making has become a common paradigm.Explainable Artificial Intelligence has been one of the more explored factors in improving transparency of AI tools in AI-assisted decision-making, but sometimes with contradictory results.Furthermore, while individual AI-assisted decisionmaking has garnered substantial investigation, the domain of group AI-assisted decision-making remains notably underexplored.This research presents the first look at the impact of explainability and team composition on AI-assisted decision-making.With a controlled experiment on mushroom edibility classification, with 89 participants, we show that the impact of XAI is more pronounced in decision-making with groups (2-person) than in individual decisionmaking.Groups rely less on incorrect AI recommendations when explanations are available, but they rely more on incorrect AI recommendations when explanations are absent, compared to individual decision makers.This phenomenon underscores the amplified effect of explainability in AI-assisted decision-making in group settings.

Although research often explores how human-robot interaction influences cooperation in social dilemma scenarios such as the Public Goods Game, most research focuses on robots taking active roles in the game i.e. opponents or team-players. Considering the potential of assistant robots to influence human decision-making, this study explores how an assistant robot with prosocial or individualistic goals influences cooperation in a Public Goods Game. In a between-subjects study (N=60), participants interacted with the robot in one of three conditions: Prosocial, where the robot supported cooperative behavior; Individualistic, where it encouraged self-serving actions; and Control, where the robot provided feedback on the game state without expressing individual goals. Results revealed that participants in the Prosocial or Individualistic conditions contributed less to the public good compared to those in the Control condition. The Prosocial and Individualistic robots were perceived as warmer but evoking more discomfort compared to the Control. Notably, participants who played with the Prosocial robot reported increased trust, not only in the robot but also in their fellow players. These findings suggest that alignment between an assistant robot’s goals and expected social norms plays a key role in trust perception and it also shapes group dynamics. We discuss important considerations for designing assistant robots that provide moral recommendations.

State-of-the-art agent-modelling tools support the creation of powerful Socially Intelligent Agents (SIAs) capable of engaging in social interactions with participants in various roles and environments. However, their deployment demands a labourious authoring task as it is necessary to manually define behaviour rules and create content for different interaction scenarios. While Socially Intelligent Agents (SIAs) research has centred on the user experience, we shift focus to the authors. To understand the challenges faced by authors who create these agents, we performed an innovative analysis of the authoring experience in modern agent modelling tools. One key finding is that, while SIA concepts are generally understandable, emotional-based concepts are not as easily comprehended or used by authors. We propose a hybrid solution approach that culminated in the development of Authoring-Assisted FAtiMA-Toolkit. The augmented agent modelling tool incorporates a data-driven Authoring Assistant to boost author productivity while promoting transparency and authorial control. To evaluate the impact of this framework on the authoring experience, we conducted a user study. Results showed that authors using the Authoring-Assisted FAtiMA-Toolkit were on average able to create more SIA-related content in less time. Our findings suggest that data-augmented, theory-grounded agent modelling tools can support the development of affective social agents by reducing the authoring burden without sacrificing the framework’s clarity or the authors’ control over the content.

Current Higher Education Institutions' mental health support systems lack the capabilities to cope with the growing need and demand for mental health support from students. We introduce MHeVA -- Mental Health Virtual Assistant -- which was designed with the goal of creating an intelligent virtual agent that could serve as a first-line diagnostic-aid tool for mental health services across universities and faculties. Students interact with the agent which attempts to establish rapport and promotes disclosure through mental health state evaluation questions. In addition to this, MHeVA has the ability to assess self-reported anxiety levels, provide health improvement tips and flag the most severe cases.

Socially Intelligent Agents (SIAs) have become increasingly popular in various contexts, including education and entertainment. However, creating complex social scenarios tailored to a designer's specific goals remains a significant challenge. The authoring burden can be substantial, limiting the potential of SIAs to deliver rich, engaging experiences. In this work, we propose leveraging the extensive knowledge stored within Large Language Models and use theory-driven prompting to extract social practices and identify appropriate social affordances for a scenario description. Our prompting approach aims to guide the system into considering the essential components (beliefs and desires) necessary to produce intentions, actions, and emotions1. Results show that our approach produces large amounts of accurate and new information that can add value to the scenario. However, the process can introduce inaccuracies without human supervision.

Social sharing of emotions (SSE) occurs when one communicates their feelings and reactions to a certain event in the course of a social interaction. The phenomenon is part of our social fabric and plays an important role in creating empathetic responses and establishing rapport. Intelligent social agents capable of SSE will have a mechanism to create and build long-term interaction with humans. In this paper, we present the Emotional Episode Generation (EEG) model, a fine-tuned GPT-2 model capable of generating emotional social talk regarding multiple event tuples in a human-like manner. Human evaluation results show that the model successfully translates one or more event-tuples into emotional episodes, reaching quality levels close to human performance. Furthermore, the model clearly expresses one emotion in each episode as well as humans. To train this model we used a public dataset and built upon it using event extraction techniques1.

There are over 80 million stroke survivors globally, making it the main cause of long-term disability worldwide. Not only do the challenges associated with stroke affect the quality of life (QoL) of survivors, but also of their families. To explore these challenges and define design opportunities for technologies to improve the QoL of both stakeholders, we conducted semi-structured interviews with 10 survivors and one of their family members. We uncovered three major interlinked themes: strategies to cope with technological barriers, the (in)adequacy of assistive technologies, and limitations of the rehabilitation process. Findings highlight multiple design opportunities, including the need for meaningful patient-centered tools and methods to improve rehabilitation effectiveness, emotion-aware computing for family emotional support, and re-thinking the nature of assistive technologies to consider the perception of transitory stroke-related disabilities. We thus argue for a new class of dual-purpose technologies that fit survivors' abilities while promoting the regain of function.

Automatic personality analysis has gained attention in the last years as a fundamental dimension in human-To-human and human-To-machine interaction. However, it still suffers from limited number and size of speech corpora for specific domains, such as the assessment of children's personality. This paper investigates a semi-supervised training approach to tackle this scenario. We devise an experimental setup with age and language mismatch and two training sets: A small labeled training set from the Interspeech 2012 Personality Sub-challenge, containing French adult speech labeled with personality OCEAN traits, and a large unlabeled training set of Portuguese children's speech. As test set, a corpus of Portuguese children's speech labeled with OCEAN traits is used. Based on this setting, we investigate a weak supervision approach that iteratively refines an initial model trained with the labeled data-set using the unlabeled data-set. We also investigate knowledge-based features, which leverage expert knowledge in acoustic-prosodic cues and thus need no extra data. Results show that, despite the large mismatch imposed by language and age differences, it is possible to attain improvements with these techniques, pointing both to the benefits of using a weak supervision and expert-based acoustic-prosodic features across age and language.

The way gaze cues are used in social interactions is by no means irrelevant because they are fundamentally important for understanding social interactions. In this paper, we argue that social conflict is a form of relating and that gaze clues are critical to understanding the underlying cognitive processes in this phenomenon. To learn more about conflict, we created an experimental setting that reduces real life to a mixed-motive game. We analyse the gaze patterns of 22 10- to 12-year-old children in specific game moments that could have been conductive to conflict. Our aim is to understand how subtle forms of conflict unfold, by analysing micro-level behaviours and establishing a link to high-level psychological constructs. Their gazes show that children are being more competitive or cooperative at different stages of the game. Children tend to avoid confrontation by averting face-directed gazes when they are asking for larger profits, and they gaze longer to attempt to persuade the other child.

Conflict is an unavoidable feature of life, but the development of conflict resolution management skills can facilitate the parties involved in resolving their conflicts in a positive manner. The goal of our research is to develop a serious game in which children may experiment with conflict resolution strategies and learn how to work towards positive conflict outcomes. While serious games related to conflict exist at present, our work represents the first attempt to teach conflict resolution skills through a game in a manner informed by sociological and psychological theories of conflict and current best practice for conflict resolution. In this paper, we present a computational approach to conflict generation and resolution. We describe the five phases involved in our conflict modeling process: conflict situation creation, conflict detection, player modeling and conflict strategy prediction, conflict management, and conflict resolution, and discuss the three major elements of our player model: assertiveness, cooperativeness, and relationship. Finally, we overview a simple resource management game we have developed in which we have begun experimenting with our conflict model concepts.