Generative Artificial Intelligence (GenAI) is increasingly integrated into photo applications on personal devices, making editing photographs easier than ever while potentially influencing the memories they represent. This study explores how and why people use GenAI to edit personal photos and how this shapes their remembering experience. We conducted a two-phase qualitative study with 12 participants: a photo editing session using a GenAI tool guided by the Remembering Experience (RX) dimensions, followed by semi-structured interviews where participants reflected on the editing process and results. Findings show that participants prioritised felt memory over factual accuracy. For different photo elements, environments were modified easily, however, editing was deemed unacceptable if it touched upon a person's identity. Editing processes brought positive and negative impacts, and itself also became a remembering experience. We further discuss potential benefits and risks of GenAI editing for remembering purposes and propose design implications for responsible GenAI.

Human Digital Twins (HDTs), building upon the concept of Digital Twins (DTs), offer transformative potential for seamless human interaction across physical and virtual worlds. This review aims to establish the groundwork for utilising HDTs in Long-Distance Relationships (LDRs) to enrich the quality of human connections over geographical separations. Searching from four academic databases, this paper examines current DT research in Human-Computer Interaction (HCI) and synthesises existing LDR solutions across commercial and research realms, including communication technologies, social media platforms, wearable and tangible interfaces, Extended Reality (XR) and immersive telepresence, and Artificial Intelligence (AI)-based companions. The findings reveal significant gaps in the research of broad DTs in HCI communities and highlight the limitations of current LDR solutions. To address these challenges, this study envisions HDTs encompassing both microscopic (individual) and macroscopic (dyadic) dimensions, offering a novel approach to enhancing emotional and experiential closeness despite geographical separation. Several potential scenarios for HDT integration in LDRs are presented, demonstrating its capability to enhance LDR interactions.

The integration of conversational agents into daily life has become increasingly common. However, sustaining deeply engaging and natural interactions remains challenging due to a lack of multimodal datasets capturing personal and emotional nuances. In this paper, we introduce MERCI (Multimodal dataset for Emotionally-aware peRsonalised Conversational In-teractions), a dataset derived from user-robot dialogues involving thirty participants who completed user profile questionnaires covering ten personal topics (e.g., hobbies, music). A conver-sational system called PERCY then engaged with each partici-pant in open-domain conversations, leveraging GPT-4, real-time facial-expression and sentiment analysis to generate contextu-ally appropriate, empathetic responses. MERCI contains 1860 utterances, equating to about 12.5 hours of aligned audio, three-view video, transcripts with timestamps, emotion labels, and sentiment scores. This dataset serves as a reproducible test-bed for tasks such as emotion-aware response generation, multimodal affect recognition, and personalised policy learning. Baseline performance results have been established using advanced models such as BERT, T5, BART, and GPT-3.5/4/4o-mini across gener-ation, regression, and classification. Evaluations through human and automated methods have demonstrated strong naturalness, relevance, and consistency in responses while indicating areas for enhanced personalisation and empathic depth. We expect that MERCI will enhance the development of emotionally intelligent, user-centric conversational AI applications, potentially ranging from social robotics to mental health support.

General Practitioners are among the primary users and curators of textual electronic health records, highlighting the need for technologies supporting record access and administration. Recent advancements in natural language processing facilitate the development of clinical systems, automating some time-consuming record-keeping tasks. However, it remains unclear what automation tasks would benefit clinicians most, what features such automation should exhibit, and how clinicians will interact with the automation. We conducted semi-structured interviews with General Practitioners uncovering their views and attitudes toward text automation. The main emerging theme was doctor-AI collaboration, addressing a reciprocal clinician-technology relationship that does not threaten to substitute clinicians, but rather establishes a constructive synergistic relationship. Other themes included: (i) desired features for clinical text automation; (ii) concerns around clinical text automation; and (iii) the consultation of the future. Our findings will inform the design of future natural language processing systems, to be implemented in general practice.

Conversational agents (CAs) are systems that interact with humans through natural language user interfaces. They include systems with a range of conversational capabilities and modalities. For example, there are text- or voice-only question-answering interactions such as Apple Siri, Google Assistant, and Amazon Alexa, and there are also multimodal conversational AI agents that can engage users in long-term dialogues. Advances in speech recognition, natural language processing, and computer vision have resulted in a greater acceptance and use of CAs. CAs have already started to play important roles in various healthcare settings, including assisting clinicians during consultations, assisting consumers in changing health behaviours, and helping patients such as the elderly in their living environments. \n \nThere have been several systematic reviews on the use of CAs in health and wellbeing recently. Although the field still appears to be nascent, the emerging evidence has shown user acceptance of CAs in the healthcare domain as well as the early promises in boosting healthcare outcomes in both physical and mental health. Despite the increasing adoption and the benefits of using CAs to support health and wellbeing, the review studies also revealed (i) patient safety was rarely examined, (ii) health outcomes were inadequately measured, and (iii) no standardised evaluation methods were employed. There were also limitations in reporting the technical implementation details of CAs used, making the replicability of prior studies problematic. \n \nIn addition to addressing some of the current challenges and limitations, this special issue features cutting-edge research on designing, developing, and evaluating CAs for health and wellbeing that aim to improve health outcomes and services, and satisfy unique application needs (e.g., safety, trust, and user experience). The seven articles included in this special issue cover many application areas ranging from mental health and social support to information seeking to coaching. Amongst the accepted articles, mental health and social support themes represented the primary research foci. The articles also covered different population groups including older adults, young adults, and homeless people. Based on their foci, we have grouped the articles in this special issue by three areas: mental health, older adult wellbeing, and social support and coaching.

OBJECTIVE: The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners. MATERIALS AND METHODS: Co-design workshops with general practitioners were conducted. The workshops focused on (1) understanding the current consultation context and identifying existing problems, (2) ideating future solutions to these problems, and (3) discussing future roles for AI in primary care. The workshop activities included affinity diagramming, brainwriting, and video prototyping methods. The workshops were audio-recorded and transcribed verbatim. Inductive thematic analysis of the transcripts of conversations was performed. RESULTS: Two researchers facilitated 3 co-design workshops with 16 general practitioners. Three main themes emerged: professional autonomy, human-AI collaboration, and new models of care. Major implications identified within these themes included (1) concerns with medico-legal aspects arising from constant recording and accessibility of full consultation records, (2) future consultations taking place out of the exam rooms in a distributed system involving empowered patients, (3) human conversation and empathy remaining the core tasks of doctors in any future AI-enabled consultations, and (4) questioning the current focus of AI initiatives on improved efficiency as opposed to patient care. CONCLUSIONS: AI documentation assistants will likely to be integral to the future primary care consultations. However, these technologies will still need to be supervised by a human until strong evidence for reliable autonomous performance is available. Therefore, different human-AI collaboration models will need to be designed and evaluated to ensure patient safety, quality of care, doctor safety, and doctor autonomy.

Abstract Although various methods have been developed to evaluate conversational interfaces, there has been a lack of methods specifically focusing on evaluating user experience. This paper reviews the understandings of user experience (UX) in conversational interfaces literature and examines the six questionnaires commonly used for evaluating conversational systems in order to assess the potential suitability of these questionnaires to measure different UX dimensions in that context. The method to examine the questionnaires involved developing an assessment framework for main UX dimensions with relevant attributes and coding the items in the questionnaires according to the framework. The results show that (i) the understandings of UX notably differed in literature; (ii) four questionnaires included assessment items, in varying extents, to measure hedonic, aesthetic and pragmatic dimensions of UX; (iii) while the dimension of affect was covered by two questionnaires, playfulness, motivation, and frustration dimensions were covered by one questionnaire only. The largest coverage of UX dimensions has been provided by the Subjective Assessment of Speech System Interfaces (SASSI). We recommend using multiple questionnaires to obtain a more complete measurement of user experience or improve the assessment of a particular UX dimension. RESEARCH HIGHLIGHTS Varying understandings of UX in conversational interfaces literature. A UX assessment framework with UX dimensions and their relevant attributes. Descriptions of the six main questionnaires for evaluating conversational interfaces. A comparison of the six questionnaires based on their coverage of UX dimensions.

OBJECTIVE: The objective of this study is to characterize the dynamic structure of primary care consultations by identifying typical activities and their inter-relationships to inform the design of automated approaches to clinical documentation using natural language processing and summarization methods. MATERIALS AND METHODS: This is an observational study in Australian general practice involving 31 consultations with 4 primary care physicians. Consultations were audio-recorded, and computer interactions were recorded using screen capture. Physical interactions in consultation rooms were noted by observers. Brief interviews were conducted after consultations. Conversational transcripts were analyzed to identify different activities and their speech content as well as verbal cues signaling activity transitions. An activity transition analysis was then undertaken to generate a network of activities and transitions. RESULTS: Observed activity classes followed those described in well-known primary care consultation models. Activities were often fragmented across consultations, did not flow necessarily in a defined order, and the flow between activities was nonlinear. Modeling activities as a network revealed that discussing a patient's present complaint was the most central activity and was highly connected to medical history taking, physical examination, and assessment, forming a highly interrelated bundle. Family history, allergy, and investigation discussions were less connected suggesting less dependency on other activities. Clear verbal signs were often identifiable at transitions between activities. DISCUSSION: Primary care consultations do not appear to follow a classic linear model of defined information seeking activities; rather, they are fragmented, highly interdependent, and can be reactively triggered. CONCLUSION: The nonlinearity of activities has significant implications for the design of automated information capture. Whereas dictation systems generate literal translation of speech into text, speech-based clinical summary systems will need to link disparate information fragments, merge their content, and abstract coherent information summaries.

Objective: Our objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes. Methods: We searched PubMed, Embase, CINAHL, PsycInfo, and ACM Digital using a predefined search strategy. Studies were included if they focused on consumers or healthcare professionals; involved a conversational agent using any unconstrained natural language input; and reported evaluation measures resulting from user interaction with the system. Studies were screened by independent reviewers and Cohen's kappa measured inter-coder agreement. Results: The database search retrieved 1513 citations; 17 articles (14 different conversational agents) met the inclusion criteria. Dialogue management strategies were mostly finite-state and frame-based (6 and 7 conversational agents, respectively); agent-based strategies were present in one type of system. Two studies were randomized controlled trials (RCTs), 1 was cross-sectional, and the remaining were quasi-experimental. Half of the conversational agents supported consumers with health tasks such as self-care. The only RCT evaluating the efficacy of a conversational agent found a significant effect in reducing depression symptoms (effect size d = 0.44, p = .04). Patient safety was rarely evaluated in the included studies. Conclusions: The use of conversational agents with unconstrained natural language input capabilities for health-related purposes is an emerging field of research, where the few published studies were mainly quasi-experimental, and rarely evaluated efficacy or safety. Future studies would benefit from more robust experimental designs and standardized reporting. Protocol Registration: The protocol for this systematic review is registered at PROSPERO with the number CRD42017065917.

In this pictorial we explain the use of photography in a performative way to rethink human-technology-world relations. Our approach emphasises the role of taking photographs as an act of constructing realities. This is typically in contrast to the documentary role of photography aiming to capture the realities as they are. Our approach involves the process of taking photographs as a means of ideation. We describe various aspects of constructing realities through photography and demonstrate the use of performative photographs as inspirational resources for design and research ideas.

We are exploring new possibilities for bodily-focused aesthetic experiences within participatory live-art contexts. As artist-researchers, we are interested in how we can understand and shape bodily experience and imagination as primary components of an interactive aesthetic experience, sonically mediated by digital biofeedback technologies. Through the making of a participatory live-art installation, we illustrate how we used the Bodyweather performance methodology to inform the design of ritual interactions intended to reframe the audience experience of self, body and the world through imaginative processes of scaling and metaphor. We report on the insights into the varieties of audience experience gathered from audience testing of the prototype artwork, with a particular focus on the relationship between the embodied imagination and felt sensation; the influence of objects and costume; and the sonically mediated experience of physiological processes of breathing and heartbeat. We offer some reflections on the use of ritual and scripted interactions as a strategy for facilitating coherent forms of bodily experience.

Technological artefacts can mediate the relations between humans and the environment: mediation changes our agency, which can be defined as our capacity for action. There can be different types of technological mediation and each type shapes our agency differently. Our model of wearable environments, which combines wearable computing and smart environment approaches, is useful for exploring new types of relations and, by extension, new forms of agency. In this paper, we present the first stage of developing a wearable environment system involving a series of workshops using two prototype devices. We evaluated the workshop activities according to a post-phenomenological account: this has allowed us to analyse the transformation of machine-mediated agency vis-à-vis two dimensions: perception and praxis. Our findings showed that interpretations of sonic and tactile feedback were highly dependent upon the placement of the sensing and effecting capacities of the system.