Dementia is one of the most pressing health problems in the world. Still, the good news is that it is much better preventable than (advanced-stage) treatable. Over the years, a new narrative has come up: heart health = brain health. But its translation into healthcare interventions has been slow. In this design approach, we propose two empowerment options for patients, caregivers, and their health professionals. Firstly, we describe how cardiac health successes in enticing senior citizens to large lifestyle improvements may be used for treating early-stage dementia and cognitive decline. Biologically, this uses causality between blood pressure and cardiovascular health on the one hand and dementia outcomes on the other. Practically, it enables daily success feedback, which empowers patients in their health improvement experiments. Secondly, we describe and user-test an AI Health Research Assistant to extract the best available lifestyle findings from literature, to keep up with over 100,000 new health publications flooding us every year. Our user test highlights challenges and opportunities for a Health AI, especially regarding claim transparency, data quality, and risks of hallucinations. We suggest research metadata criteria to evaluate ambiguous or conflicting health science claims.

As robots and virtual agents are increasingly envisioned as long-term companions, understanding how trust develops becomes crucial for ensuring safe and appropriate human-robot relationships. This research investigates how affective and cognitive trust evolve in social human-robot interactions. Participants (n=40) engaged in a 2 (social attitude: social, baseline) × 3 (time: t1, t2, t3) mixed-design user study with a social robot, using a novel Card Divination Task developed to elicit both cognitive and affective trust dimensions. Results show that cognitive trust develops early while affective trust emerges gradually. Moreover, social cues enhance both cognitive trust, affective trust, and participants' certainty in trust judgment. These findings provide empirical support for the theoretical distinction between trust dimensions and highlight the role of social behavior in shaping trust over repeated interactions.

Music is widely used in human–computer interaction (HCI) to enhance engagement, sustain attention, and support cognitive stimulation. Yet its potential for deliberate mood regulation, particularly through personalized memory recall, remains largely unexplored. Music-evoked autobiographical memories (MEAMs) are often elicited by well-known, favorite songs, yielding stronger mood effects than music without personal memory associations. However, songs can also trigger distressing memories, and will never capture all positive personal memories. Since happy personal memories can enhance mood, broader methods for retrieval are needed. To address this, we introduce Constructed Music-Evoked Episodic Memories (CoMEEMs), a framework linking chosen episodic memories to music. By creating a personalized song-memory database, CoMEEMs enable autonomous mood regulation and communication in interactive systems, integrating memory cues—such as people and places—alongside mood congruence, to help choose songs with high mood regulatory impact. In an experiment with 71 Dutch and French adults, participants described 87 positive memories and received song recommendations based on associated people and places, with and without mood matching. Results showed that song familiarity and genre were the strongest predictors of perceived fit, while valence, arousal, tempo, and lyrics played smaller roles. Mood congruence, especially in valence, significantly influenced song relevance. Participants emphasized the need for user input on emotional states and memory context. Based on these findings, we propose design guidelines to improve future music recommendation systems targeting memories.

Recent energy justice scholarship has argued for the need to reflect more explicitly on the normative assumptions that underpin claims to justice in energy systems. While such reflections increasingly inform energy policy, less attention has been paid to how these assumptions shape the design of algorithmic systems central to energy system planning and operations. This paper explores how normative assumptions in the design of algorithmic systems used to request flexibility from electricity consumers and producers to manage grid congestion may influence distributive justice outcomes. By systematically reviewing the scientific literature presenting such systems, we define two categories of assumptions: (1) scope assumptions , which set the boundaries of the justice analysis by determining which burdens and benefits, scale, subjects, and timeframe are considered relevant; and (2) design assumptions , which specify how these considerations are translated into the structure of algorithmic systems, such as allocation principles, technical problem framing, data availability and evaluation metrics. We find that the particular assumptions adopted within each category determine the distributive outcomes of these algorithmic systems. Recognizing their normative character, we propose that scope assumptions should be informed by context-specific risks of injustice identified by policymakers, while engineers should reflect on and validate their design assumptions in relation to these risks.

This article examines the ethical and moral implications of automation bias in high-stakes decision-making contexts. Drawing on empirical studies, we distinguish between weak automation bias, where users follow system’s automated cues (or its silence) without consulting readily accessible evidence that contradicts them, and strong automation bias, where users follow such cues (or their absence) even when they are aware of such evidence. While weak automation bias, in our view, resembles automation-based complacency and is plausibly associated with negligence on the part of the human operator, strong automation bias reveals an excessive and unwarranted transfer of trust from operators to automated systems which results in epistemic deference of the former to the prompts of the latter. We argue that what is ethically and morally troubling about this form of deference, is that it interferes with the exercise of the operators’ autonomous agency as well as with their duty to exercise human judgment in high-stakes decision-making contexts. To mitigate these effects, we discuss two design-based tools introducing epistemic friction - Reflection Machines (RMs) and defeaters - which ultimately aim at cultivating critical trust in the interaction between human operators and decision-support systems.

Humans and artificial intelligence agents increasingly collaborate in morally sensitive situations such as firefighting. These agents can often perform tasks with minimal human control, challenging accountability and responsibility. Combining higher agent autonomy levels with meaningful human control can address such challenges. For example, agents can allocate decisions to themselves in less morally sensitive situations and to humans in more sensitive ones. However, how to responsibly and effectively design and implement agents for this dynamic task allocation remains unclear, with their autonomy level and provided explanations being crucial considerations. Therefore, we conducted experiments in simulated firefighting environments where participants (n = 72) collaborated with a more and less autonomous artificial moral agent. These agents provided no additional information, feature contributions, or potential consequences when allocating decision-making. Our results show that moral trust, agreement, and meaningful human control are higher when the agent is less autonomous. Furthermore, people disagree and reallocate decisions to themselves more when the agents explain potential consequences, especially when moral sensitivity is higher. Overall, our findings highlight that people prefer more involvement over higher agent autonomy and take on greater moral responsibility when agents explain potential consequences. These actionable insights are crucial for designing transparent artificial moral agents that enhance human moral awareness and responsibility. Ultimately, this supports the responsible implementation of dynamic task allocation in practice and enhances human-agent collaboration in morally sensitive situations.

As renewable energy and electrification expand rapidly, many electrical distribution grids experience grid congestion. This situation leads to long waiting lists for parties seeking a new grid connection or aiming to expand their existing grid connection. In addition to traditional grid enforcements, distribution system operators are developing ways to manage congestion by steering electricity supply and demand. As grid congestion limits the previously abundant resource of grid capacity, the challenge of how to fairly distribute this now-scarce resource raises new questions about nondiscrimination and broader notions of justice. This study, grounded in energy justice, explores the distributive and procedural injustices people experience with increasing grid congestion. Our research focuses on The Netherlands, where more than 10,000 parties await new grid connections. Through 16 semi-structured interviews with people either affected by or involved in mitigating grid congestion, our thematic analysis reveals three key categories: (1) injustices arising from legacy policies, legislation, and social norms; (2) injustices due to unclear regulations, inconsistent policies, and policy gaps; and (3) injustices related to changing relationships between DSOs and affected parties. These findings highlight that grid congestion is fundamentally sociotechnical; while congestion is both constrained and addressed by technical factors, institutional and social factors such as legacy policies, social norms and communication, significantly influence perceptions of injustice. Our findings call for a comprehensive integration of justice principles within the institutional (e.g. regulation, policy, markets, social norms), technical (e.g. grid infrastructure, IT systems), and social (e.g. community engagement, communication) components of grid infrastructure.

The rise of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) is accelerating the integration of social robots into education. These technologies enhance robots' abilities in natural language interaction, adaptive behaviour, and personalised learning support. To advance real-world implementation, it is essential to identify the main challenges and opportunities in this field. We conducted a two-round Delphi study with 16 experts in human-robot interaction and educational technology. In the first round, participants outlined opportunities, challenges, and potential robot roles expected in the short term (1 year) and medium term (5 years). Content analysis revealed 8 opportunities, 10 challenges and 10 roles. In the second round, experts ranked their importance and feasibility across both time horizons. The results show that the most critical opportunities and challenges are also the least feasible to achieve in practice. Conversely, the proposed roles of educational robots demonstrated alignment between importance and feasibility. Experts highlighted three promising roles for robots in the GenAI era: supporting teachers in boosting learner engagement, serving as conversational interfaces for students to access knowledge and assisting teachers in supporting disadvantaged learners. These findings provide a roadmap for prioritising feasible innovations in educational robotics.

Healthy lifestyle behaviours are effective in preventing and treating cardiovascular disease. However, the growing body of scientific literature and the prevalence of conflicting studies make it challenging for healthcare practitioners and patients to stay informed. Large Language Models (LLMs), combined with Retrieval-Augmented Generation (RAG), enable automated claim verification and summarization. We enhanced RAG-LLM with extra modules and evaluated performance. Inclusion-Criteria-based filtering of PubMed papers improved verdict performance. Next, for health claims, PICO-based (Population, Intervention, Comparison, Outcome) paper mapping and summarization improves transparency of evidence used for verdict generation (like ‘Berries reduce blood pressure’). Still, the RAG-LLM models we tested have biases towards positivity (too many foods deemed heart healthy) and neutrality (no clear direction). We discuss mechanisms at play and challenges on the route forward.

Background
Reaching personal goals typically requires building competencies (e.g., insights into personal strengths), but expert health professionals and non-expert clients often think differently about which competencies are needed. Just having a virtual coach advise activities for "expert-devised" competencies may not motivate clients to carry them out, while advising only "non-expert devised" activities may not result in all required competencies being built.

Methods
We integrated the client and health expert worldviews in our modeling method for informing the activity selection by a virtual coach: We created a pipeline to build a reinforcement learning model for proposing activities in the context of preparing for quitting smoking. This model considers smokers’ current and future levels for expert-devised competencies as well as their beliefs about the usefulness of different competencies when choosing activities. To train the model, we conducted a micro-randomized trial in which 542 smokers interacted with a virtual coach in five sessions spread over at least nine days and received a randomly chosen activity in each session. Using data from this study, we performed simulations to systematically assess the impact of the different model components on the competencies built by smokers. Moreover, we performed paired Bayesian t-tests to determine the effect of persuasive activities on smokers’ usefulness beliefs.

Results
Our simulations show that smokers’ current levels for the expert competencies and their usefulness beliefs are important to consider when building expert competencies. In fact, we saw improvements of up to 22% when considering current competencies, and an additional 13% when also accounting for usefulness beliefs. Furthermore, although we found credible evidence that persuasive activities changed smokers’ usefulness beliefs, the effects might be too small to contribute in an optimal strategy for building competencies.

Conclusion
The worldviews of both health experts and smokers are important to consider when proposing activities for preparing for quitting smoking. We have presented a reinforcement learning model that combines these worldviews, and we hope that our work can be an example of incorporating different worldviews in a reinforcement learning model for building competencies. Our code and dataset are publicly available.

Lifestyle-related diseases like type 2 diabetes mellitus (T2DM) and chronic obstructive pulmonary disease (COPD), have a major impact on society, asking for comprehensive disease management support. While AI technology has advanced for diagnosis and disease detection, its implementation into eHealth and mHealth applications remains limited, with low adoption rates and limited evidence of effectiveness. To achieve the necessary levels of client engagement and self-efficacy in chronic disease lifestyle management (CDLM), Artificial Intelligence (AI) support must demonstrate social competencies throughout its entire lifecycle—an under-researched topic. This paper introduces a novel Social AI Competence framework designed to provide durable personalized CDLM-support. The framework defines four complementary core competencies: (1) supporting meaningful activities, (2) providing responsible actionable explanations, (3) engaging persons in reflective interactions, and (4) strengthening and leveraging support networks. Underlying these competencies are eleven key social skills, detailed in terms of their foundation, functionality, state-of-the-art advancements, and research and development challenges. The CDLM system under development employs interactive modeling techniques to incorporate the experience and expertise of both experts and clients into these skills, supported by a modular architecture that ensures adaptability and scalability. Integrating social AI functions into the competency framework enables systematic assessment and optimization of their proportional effectiveness in real-world use cases.

Humans and intelligent machines increasingly collaborate on complex tasks, although significant challenges remain before machines can function as effective teammates. The human-machine teaming research community attempts to address these challenges by developing and testing methods that identify and enhance the factors essential for successful teaming. However, this community suffers from a lack of requirements for effective research, numerous methods without centralized documentation, and a disconnect between research and real-world applications. These challenges hinder progress and limit the generalizability of research outcomes. To address these issues, we argue that the human-machine teaming research community should establish a more structured and systematic approach to studying and advancing the field. This paper identifies and discusses several key research directions and actionable outputs for such an approach. These include taxonomies and guidelines to streamline research, team design patterns to describe reusable solutions, modular testbeds to facilitate comparability and reuse, and study templates to foster creativity and encourage sharing. We believe that these elements can help formulate requirements for effective human-machine teaming research and foster the development of modular and well-documented testbeds. Achieving these goals can contribute to more ecologically valid human-machine teaming research and, thus, a stronger connection between research and real-world applications.

Integrating human support with chatbot-based behavior change interventions raises three challenges: (1) attuning the support to an individual’s state (e.g., motivation) for enhanced engagement, (2) limiting the use of the concerning human resources for enhanced efficiency, and (3) optimizing outcomes on ethical aspects (e.g., fairness). Therefore, we conducted a study in which 679 smokers and vapers had a 20% chance of receiving human feedback between five chatbot sessions. We find that having received feedback increases retention and effort spent on preparatory activities. However, analyzing a reinforcement learning (RL) model fit on the data shows there are also states where not providing feedback is better. Even this “standard” benefit-maximizing RL model is value-laden. It not only prioritizes people who would benefit most, but also those who are already doing well and want feedback. We show how four other ethical principles can be incorporated to favor other smoker subgroups, yet, interdependencies exist.

Background: Smoking and physical inactivity compromise health, especially in combination. Interventions to promote smoking cessation and increased physical activity (PA) often lack impact, especially in the long term. Digital future-self interventions (FSIs), which prompt individuals to imagine who they do and do not want to become (ie, their desired and undesired future selves), show promise in encouraging sustainable changes in both behaviors. However, knowledge of user experiences with digital FSIs is limited. A deeper understanding of these experiences could help optimize FSIs, enhancing their efficacy in supporting smoking cessation and increased PA sustainably. Objective: This study examined behavioral, cognitive, and affective experiences with digital FSIs focused on smoking, PA, or both. Potential differences in user experiences based on behavior (smoking vs PA), polarity (desired vs undesired future self), and modality (verbal vs visual description of future selves) were explored. Methods: Secondary analyses of quantitative and qualitative survey data from 3 studies using digital FSIs as a means to encourage smoking cessation or increase PA were conducted. In study 1, participants (N=144) thought about how it would be to complete the FSI. In studies 2 (N=447) and 3 (N=87), they completed an FSI. Each study highlighted different aspects of user experiences with FSIs, namely, behavioral (eg, time spent), cognitive (eg, mental effort exerted), or affective (eg, emotions) experiences. Quantitative and qualitative findings were integrated for a comprehensive interpretation. Results: Regarding behavioral experiences, participants completed future-self tasks promptly (mean 6.64, SD 8.30 minutes), spent less time completing the desired- versus undesired-future-self (P<.001; η _p ²=0.227) and verbal versus visual (P=.03; η _p ²=0.060; quantitative) tasks, and integrated the tasks into their lives (qualitative). Despite tasks being preparatory and not actively encouraging behavior change, multiple participants reported implementing changes in their smoking or PA (qualitative). Regarding cognitive experiences, moderate effort (mean 5.85/10, SD 2.56) was exerted on the tasks regardless of behavior (P=.69; η _p ²=0.002), modality (P=.45; η _p ²=0.004), or polarity (P=.69; η _p ²=0.002; quantitative). Experiences of task difficulty were inconsistent across studies, individuals, and tasks, although mental visualization and describing one’s future self using images were consistently reported as challenging (quantitative and qualitative). Future-self tasks were reported to prompt cognitive processes such as contemplating consequences of smoking and PA behavior (qualitative). Regarding affective experiences, desired- and undesired-future-self tasks elicited different emotions (P<.001; η _p ²=0.630; quantitative). Desired-future-self tasks were perceived as enjoyable and happiness inducing, whereas undesired-future-self tasks were perceived as confronting and unpleasant, evoking feelings of sadness, fear, and anger (quantitative and qualitative). Conclusions: Digital FSIs appeared to be a time-efficient, feasible, and acceptable way of strengthening identities as a means to encourage smoking cessation and PA. Findings support continued implementation of digital FSIs, although further research is required to optimize their operationalization. Avenues in that regard are proposed and discussed.

Hypertension is a major risk factor worldwide for early death. Well-established interventions like the Dash diet on average have modest results (5 mmHg systolic and 3 mmHg diastolic pressure improvement). We compare three employee eHealth intervention pilots with results that are three to six times larger, analysing them for eSupport design lessons. In these pilots, various tools and daily microlearning strategies have been used. Small-scale Self-Management Support (SMS) groups for hypertension control foster high degrees of learning, interaction, and personalization. Average blood pressure improvements in the pilots were 161/112 to 129/90 mmHg, resp. 145/92 to 126/86 mmHg, and 155/95 to 139/85 mmHg. User evaluation (n=20) showed the importance of core SMS components: information transfer, daily monitoring, promoting health competences and follow-up. A cross-case finding is that more daily social learning and ICT-enabled microlearning feedback increases success: for competence building and for blood pressure results.

Hypertension is a condition affecting most people over 45 years old. Health Self-Management offers many opportunities for prevention and cure. However, most scientific health literature is unknown by health professionals and/or patients. Per year about 200.000 new scientific papers on cardiovascular health appear, which is too much for a human to read. Hence, an LLM-based Health AI research assistant is developed for mining scientific literature on blood pressure and food. A user evaluation was conducted with n=8 participants who just completed an intensive lifestyle intervention for blood pressure self-management. They highlighted several challenges and opportunities for a Health AI, especially regarding claim transparency, data quality and risks of hallucinations. In the discussion we propose seven criteria using metadata and information characteristics to help evaluate ambiguous or conflicting health science claims.

To increase the effectiveness of behavior change applications, a large variety of algorithms has been developed to adapt what the applications offer, when, how, and with whom. Given the multitude of challenges related to the concept of algorithmic behavior change support, its development, evaluation, and impact on behavior change, this workshop aims to strengthen the community of people with diverse backgrounds (e.g., computer science, psychology, human-computer interaction) and roles in behavior change support (e.g., researcher, designer, practitioner). Combining keynotes of leading researchers with sessions in which individual workshop participants present their work and discuss problems with the audience, the workshop encouraged a lively exchange of ideas that benefits current and future research on algorithmic behavior change support.

When a human makes a decision, an observer may want to understand the reasons and motivations behind the decision. This understanding is important when IVAs are involved in contextual decision-making or coaching practices. To address this challenge, we propose that an agent’s understanding of its user should include knowledge of the user’s underlying values. Humans prioritise different values – sometimes contradictory – in a manner that depends on the context. We present a method where the agent and user build the required context-sensitive value model together. We use Schwartz’s value theory, which places individuals’ values into ten categories. In a between-subject experiment, with three sessions on different days, we elicit user values by presenting them with moral dilemmas in different contexts on the first day, refine the model by asking users to argue about contradictions on the second day, and let them reflect on the model that they have built together with the system on the third day. We find that users exposed to a value-aware condition are more likely to agree with the robot’s representations of their values post-reflection than those in a baseline. Participants also prioritise different values depending on the context, agreeing with previous findings.

Whereas the reception task is a promising application domain for social robots, knowledge is lacking about how to design the appropriate re-usable communication styles for a reception robot. This paper presents the use and evaluation of an iterative interaction-design (ID) method with which task- and person-oriented multi-modal communication styles have been designed for such a robot. First, we report on an evaluation study of the ID-method with Industrial Design students (N =13) who designed these two communication styles for a Pepper robot. This provided a set of distinct designs of the two styles, for which the differences in design parameters were in line with social science theory. The task-oriented style showed a more formal, shorter and less chatty communication. Second, we present findings from a Mechanical Turk study conducted to evaluate the perception of these style designs. Participants (N =301) were presented with videos showing the robot acting as a receptionist and were asked to rate their perception of the robot, the service experience and the orientation of the designs. Overall, the interaction with the robot was appreciated well. The robot with a person-oriented style was perceived to be more animate and likeable. Analysis showed that chit-chat was the main contributor to the perceived difference between the person-oriented and task-oriented styles. This is an important finding as it gives interaction designers a validated best-practice approach to make interaction style more or less personal.

Despite a large body of research on robot learning, it has not yet been thoroughly studied how collaborating humans and robots learn reciprocally. In such situations, both humans and robots continuously learn about each other and the task through interaction. This letter addresses the research question: "How can human-robot co-learning be facilitated in physically embodied collaborative tasks?". First, we derived five requirements for successful human-robot co-learning from literature: shared goal, synchrony, interdependence, adaptability, and transparency. Based on these requirements, we designed a collaborative human-robot handover task and a robot Q-learning method. In an evaluation with six human participants co-learning was indeed found to emerge in the hand-over task. Particularly, for three of the human-robot dyads, our designed setup proved to facilitate co-learning in a way that met all five requirements. The task and robot learning method presented in this letter demonstrate how human-robot co-learning can be enabled in physically embodied tasks.