Organizations: Social Cognition in Human-Robot Interaction, Italian Institute of Technology, Genova, 16163 Italy
Abstract
Qualitative methods are important to use alongside quantitative methods to improve Human-Robot Interaction (HRI), yet they are often applied in static or one-off formats that cannot capture how stakeholder perspectives evolve over time. This limitation is especially evident in clinical contexts, where families and patients face heavy burdens and cannot easily participate in repeated research encounters. To address this gap, we introduce continuous focus groups, a longitudinal and co-agential method designed to sustain dialogue with assistive care professionals working with children with autism spectrum disorder (ASD). Three focus groups were organized across successive phases of a robot-assisted therapeutic protocol, enabling participants to revisit and refine earlier views as the intervention progressed. Results show that continuity fostered trust, supported the integration of tacit clinical expertise into design decisions, and functioned as an ethical safeguard by allowing participants to renegotiate involvement and surface new concerns. By bridging the therapeutic iteration of families, children, and clinicians with the research-design iteration of researchers and developers, continuous focus groups provide a methodological contribution that is both feasible in practice and rigorous in design. Beyond autism care, this approach offers a transferable framework for advancing qualitative research in HRI, particularly in sensitive domains where direct user participation is limited and continuity is essential.
Collecting participants' lived experiences is central to design research. Focus groups are uniquely valuable because participants not only share individual accounts but also respond to one another, surfacing comparison, disagreement, and collective sensemaking. However, focus groups are resource-intensive and highly sensitive to facilitation: moderators must probe for specificity, balance participation, manage topic flow, and sustain psychological safety, and subtle facilitation choices can shape what becomes salient. Recent HCI work and commercial meeting tools show that generative AI can scaffold live conversation through prompting, turn regulation, thematic mapping, and real-time summarization. Yet UXR teams lack a clear map of what these capabilities mean in focus groups and what methodological risks they introduce. We synthesize AI supports for live conversation and translate them into a focus-group-specific playbook organized by AI role (tool, co-host, host) and modality (text, voice, embodied).We synthesize prior work on AI-supported live conversation and propose a focus-group-specific playbook of AI supports organized by role (tool, co-host, host) and modality (text, voice, embodied). We characterize interactional trade-offs and identify open questions for evaluating AI-supported focus groups as methodological configurations.
Thematic analysis (TA) is a widely used qualitative approach for uncovering latent meanings in unstructured text data. TA provides valuable insights in healthcare but is resource-intensive. Large Language Models (LLMs) have been introduced to perform TA, yet their applications in high-stakes healthcare settings, particularly for qualitative clinical interview analysis, remain limited. Here, we propose TAMA: A Human-AI Collaborative Thematic Analysis framework using Multi-Agent LLMs for clinical interviews. We leverage the scalability and coherence of multi-agent systems through structured conversations between agents and coordinate the expertise of cardiac experts in TA. Using interview transcripts from parents of children with Anomalous Aortic Origin of a Coronary Artery (AAOCA), a rare congenital heart disease, we demonstrate that TAMA outperforms single-agent LLM TA approaches, achieving higher thematic hit rate, coverage, and distinctiveness. TAMA demonstrates strong potential for automated TA in clinical settings by leveraging multi-agent LLM systems with human-in-the-loop integration by enhancing quality while significantly reducing manual workload. The full implementation is publicly available at https://github.com/Charlie-Yi-SJ/TAMA.
User Experience Research (UXR) Points of View (POVs) distil complex and often fragmented research evidence into actionable perspectives that guide how teams interpret user needs, frame design decisions, and align stakeholders. Although POVs are widely used in industry practice, there are few published examples that explicitly document how POVs are constructed, particularly in culturally sensitive and low-resource contexts. This paper presents an exemplar case study demonstrating how a culturally grounded, AI-augmented UXR POV was developed to inform TeleDeCa, a telemedicine dementia care framework for family caregivers in Nigeria. Building on the UXR POV Playbook and pyramid framework, we illustrate how mixed-methods research, hypothesis generation, and ontology-based modelling can be combined to form a defensible POV without requiring a fully finalised system or validated outcomes. Generative AI (GenAI) is integrated across the UXR POV framework as a bounded research collaborator, supporting synthesis, hypothesis exploration, and narrative construction while preserving human judgment, ethical accountability, and cultural sensitivity. The contribution of this paper lies in the extraction of reusable Play Cards and a Play that extend the UXR POV Playbook and serve as exemplar material for the CHI 2026 workshop on developing AI-powered UXR POVs.