Every year an estimated 200,000 people go missing in the UK alone. Missing persons investigations involve challenging time-critical sensemaking tasks based on fragmented data sources. This paper describes a mixed-methods participatory study evaluating data science and AI-driven techniques (summarisation, fact extraction, and data visualisation) for supporting these investigations as part of a human-centered workflow. A series of human-AI interfaces were iteratively designed and tested with search officers and domain experts at Police Scotland. Based on findings, we describe: (1) user and information needs for missing persons investigations; (2) insights on the benefits and challenges of applying LLM-based techniques in high-risk contexts; and (3) lessons for integrating AI for sensemaking tasks in policing more broadly. We highlight that in high-stakes contexts, where accuracy and context-sensitivity are paramount, AI techniques must be balanced with other approaches and designed in close partnership with end-users.

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.

Abstract Self-determination theory (SDT) has been widely successful in human–computer interaction (HCI). It offers ready concepts, measures, and theoretical propositions for third wave HCI topics such as user experience, fun, wellbeing, motivation, or user autonomy. Still, HCI applications of SDT have been partial, at times superficial, and disconnecting—leaving great unfulfilled potential which motivated the present special issue. In this introduction, we present SDT to interested scholars, chart its use across HCI to date, and outline six advances to move HCI toward more intentional applications of SDT. As the articles from this issue illustrate, future growth areas of SDT in HCI are in extending domain-specific models and applications, harnessing underused parts of theory, computational formalization, extending levels of analysis, facilitating design translation, and engaging in a cross-disciplinary dialogue on autonomy.

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.

This article introduces a method for conducting a fully online collaborative design workshop requiring no facilitation which we refer to as a Self-guided Collaborative Online Workshop (SCOW).The article provides three main contributions.Firstly, we present a process for the conversion of a faceto-face facilitated design workshop into a SCOW using a method we call the "playboard" which draws on concepts from CSCL literature.Secondly, we evaluate the efficacy of SCOWs using an iterative evaluation with 75 participants, including measures for participant satisfaction, subjective and objective learning outcomes, quality of the online and self-guided experience, and comparison with face-to-face workshops.Results across all measures indicate that the self-guided workshop was as successful as the in-person facilitated original.Moreover, participants reported advantages of the more scalable format including improved access to those with non-visible disabilities and in the Global South.Finally, based on our findings, we present a set of recommendations for others interested in using SCOWs as an inclusive and scalable way to support collaborative experiences.

While human beings have a right to digital experiences that support, rather than diminish, their psychological wellbeing, technology designers lack research-based practices for ensuring psychological needs are met. To help address this gap, we draw on findings from over 30 years of research in psychology (specifically, self-determination theory) that has identified contextual factors shown to support psychological wellbeing. We translate these findings into a list of 15 heuristics and 30 design strategies to provide technology makers with theoretically grounded, research-based, and actionable ways to support wellbeing in user experience.