arXiv subjects

Rafael A. Calvo

Publications and source records attributed to Rafael A. Calvo.

15 recordsLinked to original sources

arXiv2026

All four leading LLMs talk more than they listen to personality-verified synthetic help-seekers

Large language models are increasingly consulted at moments of distress, yet single-turn benchmarks neither test sustained exchanges nor distinguish between users. We built a personality-aware evaluation in which four widely used models advised several synthetic help-seekers, each given a psychometrically specified profile, in an acute crisis: a caregiver learning of a relative's dementia diagnosis. Auditors blind to the profile prompt recovered the specified bands from dialogue alone with high agreement on every instrument (ICC(2,4) = 0.91; 0.79-0.96 by instrument; band-score r = 0.78), as expected for the Big Five but equally for coping style, coping self-efficacy, resilience and reactance, which the lexical approach never covered. Such evaluation therefore reaches beyond the Five Factor Model to motivational, regulatory and self-appraisal dispositions. The four models were not distinguishable on emotion stabilisation and failed alike, sharing three modes: verbosity, a talk-to-listen ratio above one, and problem-solving before the situation had been explored.

cs.HC↗

arXiv2026

Designing for Engagement: How Self-Determination Theory Can Guide Digital Health Design for User Motivation

User engagement is crucial for the efficacy of digital health and mental health interventions, yet existing design strategies for improving engagement remain heterogeneous, context-specific, and insufficiently grounded in motivational theory. In this paper, we propose a preliminary, theory-grounded design framework that draws on Self-Determination Theory (SDT) and its sub-theory, Organismic Integration Theory (OIT), to guide the design of digital health interventions for sustained user engagement. Informed by existing literature and our own empirical data from surveys (N = 438), interviews (N = 31), and co-design workshops (N = 59) with end users, the framework categorises design strategies across the adoption, interface, and task spheres of the user experience, distinguishing between those that primarily support intrinsic motivation and those that foster autonomous forms of extrinsic motivation. We argue that this distinction is critical: strategies commonly grouped under umbrella terms such as "gamification" in fact operate through different motivational channels and should be designed and evaluated accordingly. By clarifying these motivational pathways, our framework aims to support researchers and practitioners in designing digital health interventions that not only facilitate initial uptake but also enhance the internalisation of health behaviours for long-term, sustained engagement. We present this framework as a basis for discussion at this workshop, inviting expert feedback and critique to refine it as a contribution to the field.

cs.HC↗

arXiv2026

AI as Relational Translator: Rethinking Belonging and Mutual Legibility in Cross-Cultural Contexts

Against rising global loneliness, AI companions promise connection, yet accumulating evidence suggests that, for some users and contexts, intensive companion-style use can correlate with increased loneliness and reduced offline socialisation. This position paper challenges the dominant "AI as companion" paradigm by proposing a shift: from AI that simulates relationships with humans to AI that supports relationships between humans. We introduce Relational AI Translation, positioning AI as cultural-relational infrastructure that scaffolds human connection across cultural, generational, and geographical divides. Using first-generation East Asian migrants as a theoretically productive critical case, we outline a multi-agent architecture instantiating three translation operations: emotion-intent decoding, contextual reframing, and relational scaffolding. We articulate design provocations around measurement, safety architecture, and the tension between technological intervention and structural justice, and explicitly frame success as graduation toward renewed human-to-human support rather than sustained engagement with the system.

cs.HC↗

arXiv2026

SAGE: Sensor-Augmented Grounding Engine for LLM-Powered Sleep Care Agent

Sleep is vital for health, yet access to data alone does not guarantee improvement. While wearables and health apps enable tracking, users face a "Data-Action Gap," struggling to interpret metrics and translate them into action. Current interventions fail to bridge this: static dashboards lack context, rule-based agents rely on rigid scripts, and LLM-agents lack grounding in personal data, causing trust issues. We propose SAGE (Sensor-Augmented Grounding Engine) for an LLM-powered sleep care agent. SAGE normalizes continuous sleep, physiological, and activity data from the sensors into a queryable time-series layer. It supports (1) selective system-initiated monitoring that triggers notifications only upon detecting meaningful deviations against personal baselines to reduce alert fatigue, and (2) user-initiated Q&A where natural language is translated into executable database queries. By ensuring responses are grounded in precise period, comparison, and metric data, SAGE aims to enhance personalization, traceability, and trust, articulating a novel design space for evidence-based messaging in sleep care.

cs.HC↗

arXiv2026

Front-End Ethics for Sensor-Fused Health Conversational Agents: An Ethical Design Space for Biometrics

The integration of continuous data from built-in sensors and Large Language Models (LLMs) has fueled a surge of "Sensor-Fused LLM agents" for personal health and well-being support. While recent breakthroughs have demonstrated the technical feasibility of this fusion (e.g., Time-LLM, SensorLLM), research primarily focuses on "Ethical Back-End Design for Generative AI", concerns such as sensing accuracy, bias mitigation in training data, and multimodal fusion. This leaves a critical gap at the front end, where invisible biometrics are translated into language directly experienced by users. We argue that the "illusion of objectivity" provided by sensor data amplifies the risks of AI hallucinations, potentially turning errors into harmful medical mandates. This paper shifts the focus to "Ethical Front-End Design for AI", specifically, the ethics of biometric translation. We propose a design space comprising five dimensions: Biometric Disclosure, Monitoring Temporality, Interpretation Framing, AI Stance, and Contestability. We examine how these dimensions interact with context (user- vs. system-initiated) and identify the risk of biofeedback loops. Finally, we propose "Adaptive Disclosure" as a safety guardrail and offer design guidelines to help developers manage fallibility, ensuring that these cutting-edge health agents support, rather than destabilize, user autonomy.

cs.CY↗

arXiv2026

Understanding Workplace Relatedness Support among Healthcare Professionals: A Four-Layer Model and Implications for Technology Design

Healthcare professionals (HCPs) face increasing occupational stress and burnout. Supporting HCPs need for relatedness is fundamental to their psychological wellbeing and resilience. However, how technologies could support HCPs relatedness in the workplace remains less explored. This study incorporated semi-structured interviews (n = 15) and co-design workshops (n = 21) with HCPs working in the UK National Health Service (NHS), to explore their current practices and preferences for workplace relatedness support, and how technology could be utilized to benefit relatedness. Qualitative analysis yielded a four-layer model of HCPs relatedness need, which includes Informal Interactions, Camaraderie and Bond, Community and Organizational Care, and Shared Identity. Workshops generated eight design concepts (e.g., Playful Encounter, Collocated Action, and Memories and Stories) that operationalize the four relatedness need layers. We conclude by highlighting the theoretical relevance, practical design implications, and the necessity to strengthen relatedness support for HCPs in the era of digitalization and artificial intelligence.

cs.HC↗

arXiv2025

Synthetic social data: trials and tribulations

Large Language Models are being used in conversational agents that simulate human conversations and generate social studies data. While concerns about the models' biases have been raised and discussed in the literature, much about the data generated is still unknown. In this study we explore the statistical representation of social values across four countries (UK, Argentina, USA and China) for six LLMs, with equal representation for open and closed weights. By comparing machine-generated outputs with actual human survey data, we assess whether algorithmic biases in LLMs outweigh the biases inherent in real- world sampling, including demographic and response biases. Our findings suggest that, despite the logistical and financial constraints of human surveys, even a small, skewed sample of real respondents may provide more reliable insights than synthetic data produced by LLMs. These results highlight the limitations of using AI-generated text for social research and emphasize the continued importance of empirical human data collection.

cs.CY↗

arXiv2025

Convivial Conversational Agents -- shifting toward relationships

Conversational AI (CAI) systems offer opportunities to scale service provision to unprecedented levels and governments and corporations are already beginning to deploy them across services. The economic argument is similar across domains: use CAI to automate the time-consuming conversations required for customer, client or patient support. Herein we draw on our work in dementia care to explore some of the challenges and opportunities for CAI, and how a new way of conceptualising these systems could help ensure essential aspects for human thriving are not lost in the process of automation.

cs.HC↗

arXiv2025

AI-enhanced conversational agents for personalized asthma support Factors for engagement, value and efficacy

Asthma-related deaths in the UK are the highest in Europe, and only 30% of patients access basic care. There is a need for alternative approaches to reaching people with asthma in order to provide health education, self-management support and bridges to care. Automated conversational agents (specifically, mobile chatbots) present opportunities for providing alternative and individually tailored access to health education, self-management support and risk self-assessment. But would patients engage with a chatbot, and what factors influence engagement? We present results from a patient survey (N=1257) devised by a team of asthma clinicians, patients, and technology developers, conducted to identify optimal factors for efficacy, value and engagement for a chatbot. Results indicate that most adults with asthma (53%) are interested in using a chatbot and the patients most likely to do so are those who believe their asthma is more serious and who are less confident about self-management. Results also indicate enthusiasm for 24/7 access, personalisation, and for WhatsApp as the preferred access method (compared to app, voice assistant, SMS or website). Obstacles to uptake include security/privacy concerns and skepticism of technological capabilities. We present detailed findings and consolidate these into 7 recommendations for developers for optimising efficacy of chatbot-based health support.

cs.HC↗

arXiv2025

Towards culturally-appropriate conversational AI for health in the majority world: An exploratory study with citizens and professionals in Latin America

There is justifiable interest in leveraging conversational AI (CAI) for health across the majority world, but to be effective, CAI must respond appropriately within culturally and linguistically diverse contexts. Therefore, we need ways to address the fact that current LLMs exclude many lived experiences globally. Various advances are underway which focus on top-down approaches and increasing training data. In this paper, we aim to complement these with a bottom-up locally-grounded approach based on qualitative data collected during participatory workshops in Latin America. Our goal is to construct a rich and human-centred understanding of: a) potential areas of cultural misalignment in digital health; b) regional perspectives on chatbots for health and c)strategies for creating culturally-appropriate CAI; with a focus on the understudied Latin American context. Our findings show that academic boundaries on notions of culture lose meaning at the ground level and technologies will need to engage with a broader framework; one that encapsulates the way economics, politics, geography and local logistics are entangled in cultural experience. To this end, we introduce a framework for 'Pluriversal Conversational AI for Health' which allows for the possibility that more relationality and tolerance, rather than just more data, may be called for.

cs.HC↗

arXiv2025

Beyond Wellbeing Apps: Co-Designing Immersive, Embodied, and Collective Digital Wellbeing Interventions for Healthcare Professionals

Healthcare professionals (HCPs) face increasing levels of stress and burnout. Technological wellbeing interventions provide accessible and flexible support for HCPs. While most studies have focused on mobile- and web-based programs, alternative technologies like virtual reality (VR), augmented reality (AR), tangible interfaces, and embodied technologies are emerging as engaging and effective tools for wellbeing interventions. However, there is still a lack of research on how such technologies are perceived among HCPs. This study explored HCPs' perceptions and preferences for various types of wellbeing technologies, by conducting a 2-phase co-design study involving 26 HCPs in idea generation, concept evaluation, prototype testing, and design iteration. From our findings, HCPs highly valued the potential of technologies to support mental health with immersive, embodied, and collective experiences. Furthermore, we provided design recommendations for wellbeing technologies for HCPs that sustain user engagement by meeting their needs for autonomy, competence, and relatedness in the experiences.

cs.HC↗

arXiv2023

The Value-Sensitive Conversational Agent Co-Design Framework

Conversational agents (CAs) are gaining traction in both industry and academia, especially with the advent of generative AI and large language models. As these agents are used more broadly by members of the general public and take on a number of critical use cases and social roles, it becomes important to consider the values embedded in these systems. This consideration includes answering questions such as 'whose values get embedded in these agents?' and 'how do those values manifest in the agents being designed?' Accordingly, the aim of this paper is to present the Value-Sensitive Conversational Agent (VSCA) Framework for enabling the collaborative design (co-design) of value-sensitive CAs with relevant stakeholders. Firstly, requirements for co-designing value-sensitive CAs which were identified in previous works are summarised here. Secondly, the practical framework is presented and discussed, including its operationalisation into a design toolkit. The framework facilitates the co-design of three artefacts that elicit stakeholder values and have a technical utility to CA teams to guide CA implementation, enabling the creation of value-embodied CA prototypes. Finally, an evaluation protocol for the framework is proposed where the effects of the framework and toolkit are explored in a design workshop setting to evaluate both the process followed and the outcomes produced.

cs.HC↗

arXiv2021

MONAH: Multi-Modal Narratives for Humans to analyze conversations

In conversational analyses, humans manually weave multimodal information into the transcripts, which is significantly time-consuming. We introduce a system that automatically expands the verbatim transcripts of video-recorded conversations using multimodal data streams. This system uses a set of preprocessing rules to weave multimodal annotations into the verbatim transcripts and promote interpretability. Our feature engineering contributions are two-fold: firstly, we identify the range of multimodal features relevant to detect rapport-building; secondly, we expand the range of multimodal annotations and show that the expansion leads to statistically significant improvements in detecting rapport-building.

cs.CL↗

arXiv2019

A Review on Dyadic Conversation Visualizations - Purposes, Data, Lens of Analysis

Many professional services are provided through text and voice systems, from voice calls over the internet to messaging and emails. There is a growing need for both individuals and organizations to understand these online conversations better and find actionable insights. One method that allows the user to explore insights is to build intuitive and rich visualizations that illustrate the content of the conversation. In this paper, we present a systematic survey of the various methods of visualizing a conversation and research papers involving interactive visualizations and human participants. Findings from the survey show that there have been attempts to visualize most, if not all, of the types of conversation that are taking place digitally, from speech to messages and emails. Through this survey, we make two contributions. One, we summarize the current practices in the domain of visualizing dyadic conversations. Two, we provide suggestions for future dialogue visualization research.

cs.HC↗

arXiv2019

A Comparison of Online Automatic Speech Recognition Systems and the Nonverbal Responses to Unintelligible Speech

Automatic Speech Recognition (ASR) systems have proliferated over the recent years to the point that free platforms such as YouTube now provide speech recognition services. Given the wide selection of ASR systems, we contribute to the field of automatic speech recognition by comparing the relative performance of two sets of manual transcriptions and five sets of automatic transcriptions (Google Cloud, IBM Watson, Microsoft Azure, Trint, and YouTube) to help researchers to select accurate transcription services. In addition, we identify nonverbal behaviors that are associated with unintelligible speech, as indicated by high word error rates. We show that manual transcriptions remain superior to current automatic transcriptions. Amongst the automatic transcription services, YouTube offers the most accurate transcription service. For non-verbal behavioral involvement, we provide evidence that the variability of smile intensities from the listener is high (low) when the speaker is clear (unintelligible). These findings are derived from videoconferencing interactions between student doctors and simulated patients; therefore, we contribute towards both the ASR literature and the healthcare communication skills teaching community.

cs.SD↗