Children’s self-report is essential for research, education, and healthcare, yet existing methods such as surveys and diaries can be experienced as tedious and so lead to disengagement and low-quality responses. Chatbots have been suggested as a way to support children through conversational interaction, using age-appropriate language and an empathetic tone. Here we explore what could be suitable conversational styles for such chatbots. Specifically, we explore rhyme as a child-centered conversational style. We first conducted a co-design workshop with 35 children, which revealed preferences for short, playful, and soothing conversational patterns. Building on these insights, we designed a voice-based sleep diary in rhyming style and compared it to a prose style in a within-subjects study involving 40 children aged 8-12. Results show that rhyming prompts significantly improved response quality across question types and age groups, while maintaining high engagement even among children who preferred the prose style. We contribute proof-of-concept empirical evidence and design insights demonstrating how phonological scaffolding exemplified through rhyme extends the design space of capability-adapted chatbots beyond semantic simplification alone. While limited to short-term, lab-based sessions, this work provides initial evidence that conversational style can function as a design lever.

As Human-Computer Interaction (HCI) researchers increasingly conduct studies involving populations with stigmatised identities, more researchers must focus on creating conversational spaces where participants feel comfortable discussing their experiences. This study unpacks how and why HCI researchers engage in self-disclosure to facilitate such spaces. We share eight autoethnographic personal stories of key moments when we disclosed or concealed personal information in our HCI studies with diverse participant groups (people with dementia, victims of online romance scams, children with autism, and North Korean defectors). Through analysing these stories, we found that these decisions were shaped by various goals: creating a welcoming environment, demonstrating credibility, establishing common ground, reducing stigma, and directing the interview’s focus. The acts of disclosure or concealment affected the interactions with participants, the collected data, and the well-being of the researcher and participant. We argue that greater awareness, reflection, guidance, and sharing are needed regarding self-disclosure in HCI research and offer a reflexive guide to help researchers prepare for and reflect on their self-disclosure practices. By making these practices visible and open to discussion, we aim to make researcher-participant interactions in sensitive settings more ethical, effective, and transparent. • HCI researchers dynamically disclose/conceal personal information with stigmatised groups. • Researchers differ in the who/what/when/why(not)/how of their self-disclosure. • HCI lacks awareness, guidelines, and transparency for researcher self-disclosure. • Reflexivity around self-disclosure is needed for ethical and effective research.

Socially Assistive Robotics (SAR) has shown promise in supporting emotion regulation for neurodivergent children. Recently, there has been increasing interest in leveraging advanced technologies to assist parents in co-regulating emotions with their children. However, limited research has explored the integration of large language models (LLMs) with SAR to facilitate emotion co-regulation between parents and children with neurodevelopmental disorders. To address this gap, we developed an LLM-powered social robot by deploying a speech communication module on the MiRo-E robotic platform. This supervised autonomous system integrates LLM prompts and robotic behaviors to deliver tailored interventions for both parents and neurodivergent children. Pilot tests were conducted with two parent-child dyads, followed by a qualitative analysis. The findings reveal MiRo-E’s positive impacts on interaction dynamics and its potential to facilitate emotion regulation, along with identified design and technical challenges. Based on these insights, we provide design implications to advance the future development of LLM-powered SAR for mental health applications.

Parent involvement in children’s homework has emerged as an important component of their education. However, this involvement can generate tension between parents and children, particularly when coupled with a heavy workload, potentially exacerbating stress levels in children. Some children desire to express and share their stress with their parents. However, limited emotional regulation abilities and a lack of stress awareness often hinder effective stress management and communication by children. To address this, we designed a stress biofeedback system named Stress Diffuser, comprising a wearable biosensor with interactive embodied devices, that display the child’s stress during homework sessions. The system intends to manage children’s stress by enhancing awareness among both the child and the parent, and influencing their educational strategies and the way of interaction. We conducted experiments with eight families, including primary school children aged eight to eleven and their parents. The results of our quantitative and qualitative analysis indicated that all participants enhanced their stress awareness while using Stress Diffuser during homework. Most parents adjusted their supervisory strategies and interaction styles in response to their children’s stress data. The study sheds light on children’s needs for a designed agent to communicate their stress and regulate their emotions. Furthermore, the study explores the use of interactive biofeedback technology in homework settings with parent involvement. It reveals a favorable influence on facilitating stress management in children and on enhancing the quality of supervision and interaction between children and parents.

Stress-related health problems in children have increased in recent years, resulting in significant negative physical and mental impacts on children’s daily lives. This systematic review explores the potential of embodied technologies, such as robots, smart wearables, and the Internet of Things (IoT), as tools for managing stress in children. The goal of this systematic review is to identify the design opportunities of embodied technologies in stress management, by looking for answers in terms of different technologies, users, issues, and challenges addressed in the 91 selected papers. Through the frequency and thematic analysis, we identified six main challenges and eight design opportunities for embodied technologies. Where there are gaps and opportunities in research, we propose to focus on connectivity and active sensing through connected objects, by exploring the potential of the Internet of Robotic Things (IoRT) as an M-health solution for providing real-time and personalized stress detection and interventions for children in various daily life settings.