Generative AI systems are increasingly capable of expressing emotions through text, imagery, voice, and video. Effective emotional expression is particularly relevant for AI systems designed to provide care, support mental health, or promote wellbeing through emotional interactions. This research aims to enhance understanding of the alignment between AI-expressed emotions and human perception. How can we assess whether an AI system successfully conveys a specific emotion? To address this question, we designed a method to measure the alignment between emotions expressed by generative AI and human perceptions. Three generative image models—DALL-E 2, DALL-E 3, and Stable Diffusion v1—were used to generate 240 images expressing five positive and five negative emotions in both humans and robots. Twenty-four participants recruited via Prolific rated the alignment of AI-generated emotional expressions with a string of text (e.g., “A robot expressing the emotion of amusement”). Our results suggest that generative AI models can produce emotional expressions that align well with human emotions; however, the degree of alignment varies significantly depending on the AI model and the specific emotion expressed. We analyze these variations to identify areas for future improvement. The paper concludes with a discussion of the implications of our findings on the design of emotionally expressive AI systems.@en

This workshop explores the transformative potential of generative artificial intelligence (GenAI) in design research. GenAI, capable of creating new content such as images, text, music, video, and code, raises important questions about authorship, agency, and design practice. Inspired by Roland Barthes’ "The Death of the Author," this workshop examines how GenAI reshapes design research roles and methods. Key topics include best practices, ethical considerations, knowledge generation, and collaboration patterns between human and AI creatives.

Building on themes identified in the successful DIS 2023 workshop, this 2-day event invites designers and researchers to present completed projects, works-in-progress, and theoretical provocations. The structure allows time for both presentations and in-depth discussions, aiming to develop an online resource library and a collaborative publication. The workshop seeks to advance the discourse on GenAI, addressing its challenges and opportunities in design research.@en

The rise of Artificial Intelligence (AI), from curatorial AI in YouTube to generative AI in ChatGPT, demonstrates both potential for progress and risks of harm. Adopting a Positive Design approach aimed at directly enhancing human wellbeing, this dissertation develops the concept of Positive AI. It explores the role of designers in steering AI innovation towards holistically supporting flourishing, not just optimizing profit or user engagement. Through human-centered methods, this research seeks to advance understanding and techniques for assessing and iteratively improving AI's impact on wellbeing.Key questions address how wellbeing manifests in AI systems, how it can be measured, how to design interventions, and how to evaluate them. Outcomes include conceptual frameworks, case studies demonstrating approaches, proposed methods, and evaluations, aimed at laying a robust foundation for AI that fosters human flourishing. A cybernetics perspective organizes the challenges for designing AI for wellbeing, emphasizing the importance of feedback loops connecting assessments and actions.A longitudinal case study at Delft University of Technology presents "My Wellness Check," a cybernetic system for community wellbeing during COVID-19. The project, spanning two years and engaging 20,311 participants, demonstrates the application of cybernetic principles in a complex sociotechnical context. Building on these insights, a novel method is developed to systematically integrate wellbeing into AI design through distinct phases, from contextualizing wellbeing needs to continuously aligning AI behavior with wellbeing goals. The method's effectiveness is exemplified through diverse student projects, with expert evaluations providing evidence of its practicality and efficacy.Finally, the research synthesizes insights into a set of recommendations, charting concrete next steps for researchers and practitioners across fields to further mature these nascent perspectives and capabilities towards Positive AI. Key recommendations include integrating human-centered design methods, balancing immediate desires with long-term wellbeing, contextualizing wellbeing through participatory processes, establishing multidimensional feedback loops, shifting from harm mitigation to actively cultivating flourishing, and embracing Positive AI as an ongoing process. Through multifaceted efforts spanning advocacy, policy, and community building, the Positive AI agenda can progressively guide innovation trajectories towards enhancing societal wellbeing. While further work is needed to fully realize its potential, this dissertation makes important strides in laying the groundwork for AI that actively prioritizes human flourishing through integrative, collaborative design.@en

In an era where artificial intelligence (AI) permeates every facet of our lives, the imperative to steer AI development toward enhancing human wellbeing has never been more critical. However, the development of such positive AI poses substantial challenges due to the current lack of mature methods for addressing the complexities that designing AI for wellbeing poses. This article presents and evaluates the positive AI design method aimed at addressing this gap. The method provides a human-centered process for translating wellbeing aspirations into concrete interventions. First, we explain the method’s key steps: (1) contextualizing, (2) operationalizing, (3) designing, and (4) implementing supported by (5) continuous measurement for iterative feedback cycles. We then present a multi-case study where novice designers applied the method, revealing strengths and weaknesses related to efficacy and usability. Next, an expert evaluation study assessed the quality of the case studies’ outcomes, rating them moderately high for feasibility, desirability, and plausibility of achieving intended wellbeing benefits. Together, these studies provide preliminary validation of the method’s ability to improve AI design, while identifying opportunities for enhancement. Building on these insights, we propose adaptations for future iterations of the method, such as the inclusion of wellbeing-related heuristics, suggesting promising avenues for future work. This human-centered approach shows promise for realizing a vision of “AI for wellbeing” that does not just avoid harm, but actively promotes human flourishing.@en

Autonomous products (e.g., home cleaning robots, smart fridges, or autonomous vehicles) take over tasks that require time and effort from their users, redefining both the user roles and context around a product. Consequently, meaningful user experiences should be designed to overcome the risk of relegating humans to undesirable tasks and to take the opportunity of employing users’ newly available time, in contexts such as highly automated vehicles. Meaningful experiences are provided when fundamental user needs (i.e., universal needs that directly contribute to our wellbeing) are fulfilled. Nevertheless, designers face challenges in anticipating and fulfilling user needs related to autonomous products, since autonomous technology continues evolving towards products that are not yet in existence. In this paper, we employ a co-creation workshop method to explore how the typology of thirteen fundamental needs can serve as a starting point to design meaningful user experiences associated with autonomous vehicles. Specifically, our goal is to understand how the typology of thirteen fundamental needs (e.g., autonomy, beauty, comfort...) could help in (1) identifying how deep user needs manifest themselves in a given context and (2) conceptualizing meaningful experiences with autonomous devices. In this aim, we elaborate on the challenge of designing meaningful non-driving- related experiences in fully autonomous vehicles, which could emerge in the future if driving tasks become obsolete. The results propose new clusters of activities and a scenario for each fundamental need, and ultimately show that engaging with the fundamental needs could be a valuable foundation for designing rich human interactions with future technologies.@en

This paper explores the potential implications of embodied conversational agents (ECAs) in healthcare, focusing on the impact of appearance and conversation style on trustworthiness. We conducted a Research through Design investigation of ECAs for supporting women during the periconception period and in pregnancy. The paper presents the results of a Wizard of Oz study in which two alternative prototypes, a chatbot, and an ECA, were tested in a tertiary hospital by 25 participants. Reflecting on the results we suggest that limited patients' trust in ECAs maybe be beneficial for achieving trustworthy use of these agents in the healthcare context.

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This one day workshop will explore the use of Generative Artificial Intelligence (GenAI) in design research and practice. Generative technologies are developing rapidly and many designers are using them. Yet, there remains little published work on the use of GenAI in design. Our goal is to not only showcase the potential of GenAI for design, but to engage in discussions of its shortcomings and opportunities as they have been already articulated by scholars. By synthesizing both published and unpublished works, we will develop best practices, ethical considerations, and future research directions for the use of GenAI in design. We will explore a range of topics and themes, including leveraging the characteristics of GenAI for design, mapping the diverse applications of GenAI in design, envisioning a framework for design, and guiding future work on GenAI in design research. Ultimately, we hope to provide a roadmap for the integration of GenAI into the design research process and to encourage designers and researchers to explore the potential of GenAI in a thoughtful and deliberate way.

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Design fixation, a phenomenon describing designers' adherence to pre-existing ideas or concepts that constrain design outcomes, is particularly prevalent in human-robot interaction (HRI), for example, due to collectively held and stabilised imaginations of what a robot should look like or behave. In this paper, we explore the contribution of creative AI tools to overcome design fixation and enhance creative processes in HRI design. In a four weeks long design exploration, we used generative text-to-image models to ideate and visualise robotic artefacts and robot sociotechnical imaginaries. We exchanged results along with reflections through a digital postcard format. We demonstrate the usefulness of our approach to imagining novel robot concepts, surfacing existing assumptionsand robot stereotypes, and situating robotic artefacts in context.We discuss the contribution to designerly HRI practices and conclude with lessons learnt for using creative AI tools as an emerging design practice in HRI research and beyond.

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Good mental health is imperative for one’s wellbeing. While clinical mental disorder treatments exist, self-care is an essential aspect of mental health. This paper explores the use and perceived trust of conversational agents, chatbots, in the context of crowdsourced self-care through a between-subjects study (N = 80). One group used a standalone system with a conventional web interface to discover self-care methods. The other group used the same system wrapped in a chatbot interface, facilitating utterances and turn-taking between the user and a chatbot. We identify the security and integrity of the systems as critical factors that affect users’ trust. The chatbot interface scored lower on both these factors, and we contemplate the potential underlying reasons for this. We complement the quantitative data with qualitative analysis and synthesize our findings to identify suggestions for using chatbots in mental health contexts.

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Introduction: Designing artificial intelligence (AI) to support health and wellbeing is an important and broad challenge for technologists, designers, and policymakers. Drawing upon theories of AI and cybernetics, this article offers a design framework for designing intelligent systems to optimize human wellbeing. We focus on the production of wellbeing information feedback loops in complex community settings, and discuss the case study of My Wellness Check, an intelligent system designed to support the mental health and wellbeing needs of university students and staff during the COVID-19 pandemic. Methods: The basis for our discussion is the community-led design of My Wellness Check, an intelligent system that supported the mental health and wellbeing needs of university students and staff during the COVID-19 pandemic. Our system was designed to create an intelligent feedback loop to assess community wellbeing needs and to inform community action. This article provides an overview of our longitudinal assessment of students and staff wellbeing (n = 20,311) across two years of the COVID-19 pandemic. Results: We further share the results of a controlled experiment (n = 1,719) demonstrating the enhanced sensitivity and user experience of our context-sensitive wellbeing assessment. Discussion: Our approach to designing “AI for community wellbeing,” may generalize to the systematic improvement of human wellbeing in other human-computer systems for large-scale governance (e.g., schools, businesses, NGOs, platforms). The two main contributions are: 1) showcasing a simple way to draw from AI theory to produce more intelligent human systems, and 2) introducing a human-centered, community-led approach that may be beneficial to the field of AI.

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The COVID-19 pandemic has put wellbeing on the global agenda like never before. Many businesses, organizations, and even governments have recognized wellbeing as a formal policy goal. This paper addresses the question of how to design complex systems to improve the wellbeing of their stakeholders. We present a case of helping a university adopt a systematic approach to wellbeing assessment and improvement during the COVID-19 crisis. To support the improvement of student and staff wellbeing, we adopted a cybernetic perspective. Practically, this involved focusing on the design of a feedback loop that used wellbeing assessments to inform organizational actions. We argue that “off-the-shelf” assessments of wellbeing are often insufficient for supporting a systemic response to data because they lack context-sensitivity and actionability. While a “cybernetic perspective” may evoke a sense of the inhuman or mechanical in the optimization of wellbeing, our case study suggests otherwise. At least from our perspective, a society that aims to improve wellbeing may look more like a deliberative or dialogical democracy than an automated AI system.@en

Resonance, a powerful and pervasive phenomenon, appears to play a major role in human interactions. This article investigates the relationship between the physical mechanism of resonance and the human experience of resonance, and considers possibilities for enhancing the experience of resonance within human–robot interactions. We first introduce resonance as a widespread cultural and scientific metaphor. Then, we review the nature of “sympathetic resonance” as a physical mechanism. Following this introduction, the remainder of the article is organized in two parts. In part one, we review the role of resonance (including synchronization and rhythmic entrainment) in human cognition and social interactions. Then, in part two, we review resonance-related phenomena in robotics and artificial intelligence (AI). These two reviews serve as ground for the introduction of a design strategy and combinatorial design space for shaping resonant interactions with robots and AI. We conclude by posing hypotheses and research questions for future empirical studies and discuss a range of ethical and aesthetic issues associated with resonance in human–robot interactions.

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“My Wellness Check” is a well-being assessment system designed to support wellbeing feedback loops within large organizations, like universities. In this paper, we present a narrative describing the human-centred design process used to develop a context-sensitive well-being feedback system within a large technical university during the COVID-19 pandemic. We share quantitative and qualitative findings from the first two feedback cycles, where well-being assessments were sent to over 30,000 students and staff. By involving community members and decision-makers in the qualitative data analysis, we successfully translated results into administrative policy and community action. Our ongoing design research project highlights the desirability and feasibility of well-being feedback loops within large complex systems.@en

Human-centred design (HCD) is a powerful methodology that might play an important role in the development of real-world intelligent systems. However, present conceptualisations of artificial intelligence (AI) tend to emphasise autonomous, algorithmic systems. If humans are not involved in AI system design, what role can HCD play? This paper considers perspectives that reframe the role of AI in smart systems design, with the intention of creating space for human-centred design methodologies. These perspectives naturally give rise to opportunities for HCD by considering human and artificial intelligence in tandem. Informed by cybernetic theory, we define smart systems as "the use of outcome data to inform successful system action". To illustrate the practicality of this view, we share three case studies, each representing a different smart system configuration: artificial intelligence, human intelligence and combined artificial-human intelligence. We describe Battleship Numberline, an educational game with autonomous artificial intelligence. We then describe Zensus, a smart system for health and well-being that leverages human intelligence alone. Finally, we describe FactFlow, educational software that combines artificial and human intelligence. By examining the cybernetic feedback loops observed in these systems, we contribute a practical framework for the use of human-centred design methodology in smart systems design. This framework is intended as both a generative tool for designers and a basis for future research in the field of smart systems.@en