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.

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.

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.

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.

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.

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.

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.

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.

In human-machine teams, the strengths and weaknesses of both team members result in dependencies, opportunities, and requirements to collaborate. Managing these interdependence relationships is crucial for teamwork, as it is argued that they facilitate accurate trust calibration. Unfortunately, empirical research on the influence of interdependence on trust calibration during human-machine teamwork is lacking. Therefore, we conducted an experiment (n=80) to study the effect of interdependence relationships (complete independence, complementary independence, optional interdependence, required interdependence) on human-machine trust calibration. Participants collaborated with a virtual agent during a simulated search and rescue task in teams characterized by one of the four interdependencies. A machine-induced trust violation was included in the task to facilitate dynamic trust calibration. Results show that the interdependence relationships during human-machine teamwork influence perceived trust calibration over time. Only in the teams with joint actions (optional and required interdependence) does perceived trust in the machine not recover to its initial pre-violated value. However, results show that the correlation between perceived trust in the machine and machine trustworthiness is strongest in these teams with joint actions, suggesting a more accurate trust calibration process. Overall, our findings provide some first evidence that interdependence relationships during human-machine teamwork influence human-machine trust calibration.

Introduction: Humans and robots are increasingly collaborating on complex tasks such as firefighting. As robots are becoming more autonomous, collaboration in human-robot teams should be combined with meaningful human control. Variable autonomy approaches can ensure meaningful human control over robots by satisfying accountability, responsibility, and transparency. To verify whether variable autonomy approaches truly ensure meaningful human control, the concept should be operationalized to allow its measurement. So far, designers of variable autonomy approaches lack metrics to systematically address meaningful human control.

Methods: Therefore, this qualitative focus group (n = 5 experts) explored quantitative operationalizations of meaningful human control during dynamic task allocation using variable autonomy in human-robot teams for firefighting. This variable autonomy approach requires dynamic allocation of moral decisions to humans and non-moral decisions to robots, using robot identification of moral sensitivity. We analyzed the data of the focus group using reflexive thematic analysis.

Results: Results highlight the usefulness of quantifying the traceability requirement of meaningful human control, and how situation awareness and performance can be used to objectively measure aspects of the traceability requirement. Moreover, results emphasize that team and robot outcomes can be used to verify meaningful human control but that identifying reasons underlying these outcomes determines the level of meaningful human control.

Discussion: Based on our results, we propose an evaluation method that can verify if dynamic task allocation using variable autonomy in human-robot teams for firefighting ensures meaningful human control over the robot. This method involves subjectively and objectively quantifying traceability using human responses during and after simulations of the collaboration. In addition, the method involves semi-structured interviews after the simulation to identify reasons underlying outcomes and suggestions to improve the variable autonomy approach.

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.

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.

Social interactions form an essential aspect of people’s life, however, it is quite challenging for individuals to handle a wide range of social situations. Therefore, a variety of training systems have been developed to improve their skills. This literature review seeks to give an overview of the state of the art of technology-supported systems for social skills training. The studies eligible for inclusion described a technology-supported system with the purpose of training social skills and included an experimental or observational study to evaluate the efficacy of the system. 225 studies (224 publications) with 216 systems were identified, characterized, and analyzed in this literature review. Using the taxonomy as put forward in this study, the analysis shows that the majority of these systems were screen-based applications, with virtual reality technology being the most frequently observed. The systems most often targeted communication skills that focus on transferring information to produce greater understanding, i.e. mending general communication impairments in children with autism. In terms of functions, support for learning-by-doing was the most observed function, while focusing on job interviews provided the largest number of functions. Finally, the studies reported overwhelmingly positively regarding the systems’ impact, including 76 studies with a randomized controlled trial design. Still, most studies only used a quasi-experimental design based on self-report measures. We anticipate the proposed taxonomy to be a starting point for researchers to position their work and that the review will help them with gaining inspiration for the design and evaluation of social skills training systems.

Artificial intelligence (AI) is increasingly prevalent in our daily lives, setting specific requirements for responsible development and deployment: The AI should be explainable and inclusive. Despite substantial research and development investment in explainable AI, there is a lack of effort into making AI explainable and inclusive to people with cognitive disabilities as well. In this paper, we present the first steps towards this research topic. We argue that three main questions guide this research, namely: 1) How explainable should a system be?; 2) What level of understanding can the user reach, and what is the right type of explanation to help them reach this level?; and 3) How can we implement an AI system that can generate the necessary explanations? We present the current state of the art in research on these three topics, the current open questions and the next steps. Finally, we present the challenges specific to bringing these three research topics together, in order to eventually be able to answer the question of how to make AI systems explainable also to people with cognitive disabilities.

Artificial Intelligence systems are more and more being introduced into first response; however, this introduction needs to be done responsibly. While generic claims on what this entails already exist, more details are required to understand the exact nature of responsible application of AI within the first response domain. The context in which AI systems are applied largely determines the ethical, legal, and societal impact and how to deal with this impact responsibly. For that reason, we empirically investigate relevant human values that are affected by the introduction of a specific AI-based Decision Aid (AIDA), a decision support system under development for Fire Services in the Netherlands. We held 10 expert group sessions and discussed the impact of AIDA on different stakeholders. This paper presents the design and implementation of the study and, as we are still in process of analyzing the sessions in detail, summarizes preliminary insights and steps forward.

Fire fighters operate in a dangerous, dynamic, and complex environment. Artificial Intelligence (AI) systems can contribute to improve fire fighters' situation awareness and decision-making. However, the introduction of AI systems needs to be done responsibly, taking (human) values into account, especially as the situation in which fire fighters operate is uncertain and decisions have a big impact. In this research, we investigate values that are affected by the introduction of AI systems for fire services by conducting several semi-structured focus group sessions with (operational) fire service personnel. The focus group outcomes are qualitatively analyzed and key values are identified and discussed. This research is a first step in an iterative process towards a generic framework of ethical aspects for the introduction of AI systems in first response, which will give insight into the relevant ethical aspects to take into account when developing AI systems for first responders.

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.

Fairness has recently gained significant attention in the scientific literature on algorithmic control systems for congestion management. However, many diverse conceptualizations of fairness have been presented. This paper aims to categorize these varying conceptualizations by reviewing existing literature on congestion management. It examines how researchers approach decisions concerning the scoping of fairness problems, the selection of fairness principles, and the choice of evaluation metrics. Findings highlight a need for more justification of fairness conceptualizations in literature as well as a need for standardized evaluation metrics and more empirical grounding and validation. The insights provided can help researchers and practitioners consider fairness comprehensively in the design of algorithmic control systems for congestion management.