Transparent and consistent reporting of user studies is essential for advancing scientific knowledge. In human-robot interaction (HRI), studies are often reported incompletely, even in top-tier venues, limiting proper evaluation, replication, and practical application of findings in practice. This study aimed to generate expert consensus on a reporting checklist for HRI user studies and provide a validated tool to improve transparency, reproducibility, and methodological rigor in the field, leading to easier translation of research into practice. A two-round Delphi study was conducted with 34 HRI experts from academia and industry from over 12 countries. An international panel of nine interdisciplinary experts first synthesized a preliminary list of 116 reporting items from the literature. Experts rated the importance of each item and provided qualitative feed- back. Consensus was defined as 70% agreement, and items were iteratively refined through anonymous online surveys. Overall, consensus was achieved on 106 items, encompassing both essential and context-dependent elements in nine domains. The resulting RUSH checklist (Reporting User Studies in Human-Robot Inter- action) provides the first community-endorsed, consensus-based reporting guideline for HRI user studies.

Persuasive social robots have the ability to influence human behaviour through social interaction, which makes them a valuable technological solution for all applications aiming to support a person's behaviour change. The recently proposed Child-Robot Relational Norm Intervention (CRNI) model intro-duces a new approach for persuasive social robotics, leveraging children's reluctance to disturb robots to promote behaviour change. Unlike traditional methods that rely on direct feedback or reminders, CRNI encourages children to self-monitor and self-correct improper behaviour, by making the robot express mild distress whenever the child exhibits the incorrect behaviour. This paper proposes the first implementation of the CRNI approach in a real HRI context and evaluates its effectiveness in improving children's handwriting posture. The evaluation includes two user studies: (i) a multi-session study with five children investigating the long-term impact of the approach, (ii) a controlled study with 29 children comparing CRNI to direct robot reminders. The results indicate that the CRNI model leads to more sustained posture correction compared to direct interventions. More broadly, our findings suggest that relational norm-based approaches can offer an effective yet less intrusive method for fostering positive behaviours in children.

Creating and improvising scenarios for content approaching is an enriching technique in education. However, it comes with a significant increase in the time spent on its planning, which intensifies when using complex technologies, such as social robots. Furthermore, addressing multicultural integration is commonly embedded in regular activities due to the already tight curriculum. Addressing these issues with a single solution, we implemented an intuitive interface that allows teachers to create scenario-based activities from their regular curriculum using LLMs and social robots. We co-designed different frameworks of activities with 4 teachers and deployed it in a study with 27 students for 1 week. Beyond validating the system’s efficacy, our findings highlight the positive impact of integration policies perceived by the children and demonstrate the importance of scenario-based activities in students’ enjoyment, observed to be significantly higher when applying storytelling. Additionally, several implications of using LLMs and social robots in long-term classroom activities are discussed.

Reading is an essential skill that plays a crucial role in education, and it involves mastering various sub-skills, such as decoding tokens and acquiring phonological awareness. Research indicates that practising music in an educational setting can enhance reading skills, but implementing such interventions can be challenging. Interestingly, studies have found a correlation between individuals’ rhythm capabilities and reading sub-skills, which opens the door to more targeted and easy-to-implement educational activities. Digitalised rhythmic activities could be an effective solution, but only a few studies have explored their impact on learning to read. Specifically, several aspects have been left unexplored, and this paper aims to tackle them: the different aspects of rhythm and how to design them best to boost the interaction with children, especially in a school setting. We aim to investigate and evaluate the design of digital activities that can help us understand the connection between reading learning and rhythm. The paper presents the design of six activities and plans to conduct long-term experiments to validate the findings. The initial subjective validation with ten students in a school indicates promising outcomes, as the students were highly engaged and found the design intuitive.

Persuasive social robots employ their social influence to modulate children’s behaviours in child-robot interaction. In this work, we introduce the Child-Robot Relational Norm Intervention (CRNI) model, leveraging the passive role of social robots and children’s reluctance to inconvenience others to influence children’s behaviours. Unlike traditional persuasive strategies that employ robots in active roles, CRNI utilizes an indirect approach by generating a disturbance for the robot in response to improper child behaviours, thereby motivating behaviour change through the avoidance of norm violations. The feasibility of CRNI is explored with a focus on improving children’s handwriting posture. To this end, as a preliminary work, we conducted two participatory design workshops with 12 children and 1 teacher to identify effective disturbances that can promote posture correction.

Maintaining a proper body posture during interactions with educational tablet applications is crucial for children’s physical well-being and task performance, especially considering digital tablet’s increasingly pervasive use in classrooms. In this work we propose WriteUpRight, an interaction system for children’s self-regulation of posture while writing on a tablet. The system relies on slowly deforming visual stimuli appearing on the tablet screen and compares two posture correction strategies: the Error Amplification method (see Figure 1) seeks to induce self-correction by amplifying the postural error, while the Error Correction method seeks to unobtrusively nudge the child towards the correct posture. Through a formative design and a user study with 42 children, we demonstrate the effectiveness of our solution and the advantages of the Error Amplification method with respect to the Error Correction method. The system shows potential for helping children maintain a proper head-screen distance and head roll angle during reading and writing tasks on tablets.

Role-playing activities offer opportunities for developing individuals’ creativity, communication, and problem-solving skills. Recent advances in large language models (LLM) facilitate fluent conversations with machines. To investigate benefits and pitfalls of LLMs in a relatively unexplored context of human-agent role-play as a culturally contextualised activity, a dataset of twelve human-agent interactions produced by two researchers with two state-of-the-art LLMs was annotated based on a frame analysis scheme from literature. The pilot study shows that human-agent play has a similar complexity as human-human play in which players maintain identities of themselves, external observers and play characters simultaneously going beyond the pretend-reality dualism. Results suggest that, while the LLMs can maintain and shift between roles, they play some roles better than others, and display cultural and gender stereotypes. Additionally, the coding scheme shows potential to help identify LLM outputs that require embodied enactment, and to be used for LLM bench-marking for role-play.

Social robots are widely used for educational activities, especially to attract children’s attention. As a side effect, pupils’ excitement can suppress their focus on the tutors and the content being approached. A potential solution to tackle this issue is to equip the robot with storytelling strategies, which have been growing remarkably in recent years thanks to advances in Large Language Models. However, few studies are still addressing the resulting application in real-world conditions. In this work, we are exploring the GPT-3.5 model for story generation based on content to be approached in maker-space classes. To achieve our goals, we implemented a web application for content insertion that connects to the robot through ROS. The proposal was validated in two phases: a first phase of interviews with 5 tutors of maker-space to present our solution and get their feedback, and two 90-minute sessions with pupils for real-world validation. Results suggested the proposal has high potential for supporting multiple languages and generating suitable stories for diverse contexts. Furthermore, adding social behaviors, as encouragement and sentiment analysis, can help in the students’ expectation handling.

Children’s retention of a proper body posture while interacting with educational tablet applications is important for both their physical health and task performance. In this work, we propose a new approach to unobtrusively induce postural changes in children by applying a slowly deforming visual stimulus appearing on the tablet screen. To preliminarily validate our approach we designed a reading-and-writing tablet application for children, during which 8 different slow visual stimuli would be provided, and monitored the children’s posture via a vision-based automated posture tracking system. Results from 10 children aged 6-11 suggest that the proposed approach is suitable for unobtrusively changing children’s postures and will stand as the basis for the future design of an adaptive unobtrusive posture regulation system.

Performing breaks during long periods of mentally demanding activities is a simple and effective solution to briefly rest the brain and regain concentration. Furthermore, the way we use this break time may have different impacts on the task outcomes. Social assistive robots are commonly used to coach users and boost their motivation, but little is explored about their capability of guiding and supervising effective pauses focused on performance gain in cognitive tasks. This study investigated the effects of two different types of breaks, stretching/breathing exercises versus free-time, taking place in a Human-Robot Interaction of short-term memory games. The stretching was autonomously guided and supervised by a robot using machine learning methods to recognise the poses. Results showed that the type of break affects the performance differently according to the type of task, both types of breaks decreased participants’ stress levels. However, the stretching/breathing intervention had a higher significant reduction in their stress and was also reported as the preferred one by the participants. No correlation between stress and performance was found.

This paper describes the first steps of an ongoing participatory design with teachers from Switzerland to co-create Human-Robot Interaction setups for integrating children with migration history. The herein presented phase had two main goals: (i) initially mapping the current issues and the teachers’ strategy when integrating these children, and (ii) understanding teachers’ perceptions regarding social robots for this goal. Results show that teachers we interviewed are already using technology to communicate with immigrant children, not necessarily for inclusion or promote socialisation with their peers, but simply to understand them. Findings also point to a well-defined application of social robots in inclusion activities, even when never seeing or using them, which contradicts previous results in the literature and which gives potential ways to unfold the next steps of the participatory design.

Handwriting practising, as any other repetitive task, often leads the practiser to an overconcentration state where their performance might be affected by postural and mental fatigue. Short breaks to perform unrelated activities, especially relaxation exercises, have shown to be a simple alternative to soften or postpone this phenomenon. Therefore, in this paper we are investigating the immediate effects of different types of short-duration relaxation exercises in the handwriting and posture of children aged from 8 to 10 in handwriting training. We divided 40 children in two groups performing the sessions, guided by a social robot, with small exercises of mindfulness or stretching in the middle of their training. Additionally, we analysed participants’ perceptions towards the robot leading these interactions. Results showed improvements in participants’ handwriting quality and posture maintenance regardless of the condition. Additionally, more positive feedback about the pause was reported from individuals in the mindfulness condition.

Handwriting learning is a long and complex process that takes about ten years to be fully mastered. Nearly one-third of all children aged 4-12 experiences handwriting difficulties and, sadly, most of them are left to fight them on their own, due to the scarcity of tools for the detection and remediation of such difficulties. Building on state-of-the-art digital solutions for automated handwriting assessment and the training of specific handwriting-related skills, in this article we discuss requirements, rationale, and architecture of a system for handwriting training, which relies on a social robot as a mediator agent, offering personalized training and suggestions. The system is envisioned to operate autonomously and to support long-term interactions via personalization. Preliminary validation of the system in an experiment with 31 children showed its potential not only for autonomously guiding handwriting training sessions, but also for its inclusion in the teachers’ practice.

The presence of robots in human lifestyle is no longer a distant reality, as robots are being employed in several fields, including educational purposes. However, most of the research in educational robotics does not use autonomous social behavior, but rather techniques like Wizard of Oz (WoZ). This paper presents the very first test in a school environment of a robotic architecture to control an autonomous system for educational interactions, evaluated from user's perspective when compared to a teleoperated situation. The architecture aims to manage three main communication robot resources - speech, vision and gesture - in an autonomous way and provide an interaction as acceptable as when someone controls the robot. The experiment was performed randomly assigning 82 students aged between 7 and 11 to interact with a NAO robot in two conditions of robot operation: autonomous and teleoperated. The results suggest that there is no significant difference between the conditions in user's enjoyment and system time response, but they decreased their perception regarding robot's intelligence after knowing about the teleoperation.