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People of low literacy could benefit from automated support when learning about societal participation. We design an Embodied Conversational Agent (ECA) 'coach' that can provide effective learning support to low-literate learners, develop a prototype virtual learning environment, and evaluate this prototype with low-literate end users. First, we inventory the learning support benefits of ECA coaching. Second, we update existing requirements to better specify functional demands for the coach ECA. Third, we write use cases and develop the prototype. Finally, we evaluate the prototype with low-literate users in a mixed-method within-subjects experiment. Results show that the coach influences the subjective learning experience: Participants report higher positive affect, higher user-system engagement, and increased self-efficacy regarding online banking. These results particularly apply to the domain of challenging information skills exercises. Caveats apply: One of four exercises was significantly more difficult than the other three; and coach support rules were not clearly formalized. [ABSTRACT FROM AUTHOR]

In this study, we attempt to specify the cognitive support behavior of a previously designed embodied conversational agent coach that provides learning support to low-literates. Three knowledge gaps are identified in the existing work: an incomplete specification of the behaviors that make up 'support,' an incomplete specification of how this support can be personalized, and unclear speech recognition rules. We use the socio-cognitive engineering method to update our foundation of knowledge with new online banking exercises, low-level scaffolding and user modeling theory, and speech recognition. We then refine the design of our coach agent by creating comprehensive cognitive support rules that adapt support based on learner needs (the 'Generalized' approach) and attune the coach's support delay to user performance in previous exercises (the 'Individualized' approach). A prototype is evaluated in a 3-week within- and between-subjects experiment. Results show that the specified cognitive support is effective: Learners complete all exercises, interact meaningfully with the coach, and improve their online banking self-efficacy. Counter to hypotheses, the Individualized approach does not improve on the Generalized approach. Whether this indicates suboptimal operationalization or a deeper problem with the Individualized approach remains as future work. [ABSTRACT FROM AUTHOR]

Within current debates about the future impact of Artificial Intelligence (AI) on human society, roughly three different perspectives can be recognised: (1) the technology-centric perspective, claiming that AI will soon outperform humankind in all areas, and that the primary threat for humankind is superintelligence; (2) the human-centric perspective, claiming that humans will always remain superior to AI when it comes to social and societal aspects, and that the main threat of AI is that humankind's social nature is overlooked in technological designs; and (3) the collective intelligence-centric perspective, claiming that true intelligence lies in the collective of intelligent agents, both human and artificial, and that the main threat for humankind is that technological designs create problems at the collective, systemic level that are hard to oversee and control. The current paper offers the following contributions: (a) a clear description for each of the three perspectives, along with their history and background; (b) an analysis and interpretation of current applications of AI in human society according to each of the three perspectives, thereby disentangling miscommunication in the debate concerning threats of AI; and (c) a new integrated and comprehensive research design framework that addresses all aspects of the above three perspectives, and includes principles that support developers to reflect and anticipate upon potential effects of AI in society. [ABSTRACT FROM AUTHOR]

Recent advances in wearable sensor technology and smartphones enable simple and affordable collection of personal analytics. This paper reflects on the lessons learned in the SWELL project that addressed the design of user-centered ICT applications for self-management of vitality in the domain of knowledge workers. These workers often have a sedentary lifestyle and are susceptible to mental health effects due to a high workload. We present the sense–reason–act framework that is the basis of the SWELL approach and we provide an overview of the individual studies carried out in SWELL. In this paper, we revisit our work on reasoning: interpreting raw heterogeneous sensor data, and acting: providing personalized feedback to support behavioural change. We conclude that simple affordable sensors can be used to classify user behaviour and heath status in a physically non-intrusive way. The interpreted data can be used to inform personalized feedback strategies. Further longitudinal studies can now be initiated to assess the effectiveness of m-Health interventions using the SWELL methods. [ABSTRACT FROM AUTHOR]

The introduction of artificial teammates in the form of autonomous social robots, with fewer social abilities compared to humans, presents new challenges for human–robot team dynamics. A key characteristic of high performing human-only teams is their ability to establish, develop, and calibrate trust over long periods of time, making the establishment of longitudinal human–robot team trust calibration a crucial part of these challenges. This paper presents a novel integrative model that takes a longitudinal perspective on trust development and calibration in human–robot teams. A key new proposed factor in this model is the introduction of the concept relationship equity. Relationship equity is an emotional resource that predicts the degree of goodwill between two actors. Relationship equity can help predict the future health of a long-term relationship. Our model is descriptive of current trust dynamics, predictive of the impact on trust of interactions within a human–robot team, and prescriptive with respect to the types of interventions and transparency methods promoting trust calibration. We describe the interplay between team trust dynamics and the establishment of work agreements that guide and improve human–robot collaboration. Furthermore, we introduce methods for dampening (reducing overtrust) and repairing (reducing undertrust) mis-calibrated trust between team members as well as methods for transparency and explanation. We conclude with a description of the implications of our model and a research agenda to jump-start a new comprehensive research program in this area. [ABSTRACT FROM AUTHOR]

Patient reported outcome measures (PROMs) are an essential means for collecting information on the effectiveness of hospital care as perceived by the patients themselves. Especially older adult patients often require help from nursing staff to successfully complete PROMs, but this staff already has a high work load. Therefore, a social robot is introduced to perform the PROM questioning and recording task. The study objective was to design a multimodal dialogue for a social robot to acquire PROMs for older patients. The primary outcomes were the effectiveness, the efficiency, and the subjective usability as perceived by older adults of acquiring PROMs by a social robot. The robot dialogue design included a personalized welcome, PROM questions, confirmation requests, affective statements, use of a support screen on the robot displaying the answer options, and accompanying robot gestures. The design was tested in a crossover study with 31 community-dwelling persons aged 70 years or above. Answers obtained with the robot were compared with those obtained by a questionnaire taken by humans. First results indicated that PROM data collection in older persons may be carried out effectively and efficiently by a social robot. The robot's subjective usability was on average scored as 80.1 (± 11.6) on a scale from 0 to 100. The recorded data reliability was 99.6%. A first relevant step has been made on the design trajectory for a robot to obtain PROMs from older adults. Practice variation in subjective usability scores still asks for technical dialogue improvements. [ABSTRACT FROM AUTHOR]

Personalization of eHealth systems is a promising technique for improving patients' adherence. This paper explores the possibility of personalisation based on the patients' medical health situation and on their health literacy. The study is set within the context of a self-management support system (SMSS) for renal transplant patients. A SMSS is designed with layering, nudging, emphaticizing, and focusing principles. It has two communication styles: (1) a guided style that provided more interpretation support and addressed emotional needs; and (2) a factual style that showed only measurement history, medical information, and recommendations. To evaluate the design, 49 renal transplant patients with three different experience levels participated in a lab study, in which they used the system in imaginary scenarios to deal with three medical health situations (alright, mild concern, and concern). A 96% understanding and 87% adherence rate was observed, with a significant interaction effect on adherence between patient group and health situation. Furthermore, compared to recently transplanted patients, not recently transplanted patients were relatively more positive towards the factual than the guided communication style in the "alright" condition. Furthermore, additional medical information was searched more often in health situations that causes mild concern and a majority of patients did not change the communication style to their preferred styles. By attuning the communication style to patient's experience and medical health situation according to the applied principles and acquired insights, SMSSs are expected to be better used. [ABSTRACT FROM AUTHOR]

The experiment presented in this paper investigated the effects of different kinds of reminders on adherence to automated parts of a cognitive behavioural therapy for insomnia (CBT-I) delivered via a mobile device. Previous studies report that computerized health interventions can be effective. However, treatment adherence is still an issue. Reminders are a simple technique that could improve adherence. A minimal intervention prototype in the realm of sleep treatment was developed to test the effects of reminders on adherence. Two prominent ways to determine the reminder-time are: a) ask users when they want to be reminded, and b) let an algorithm decide when to remind users. The prototype consisted of a sleep diary, a relaxation exercise and reminders. A within subject design was used in which the effect of reminders and two underlying principles were tested by 45 participants that all received the following three different conditions (in random order): a) event-based reminders b) time-based reminders c) no reminders. Both types of reminders improved adherence compared to no reminders. No differences were found between the two types of reminders. Opportunity and self-empowerment could partly mediate adherence to filling out the sleep diary, but not to the number of relaxation exercises conducted. Although the study focussed on CBT-I, we expect that designers of other computerized health interventions benefit from the tested opportunity and self-empowerment principles for reminders to improve adherence, as well. [ABSTRACT FROM AUTHOR]

Uneven workload distributions in teams can lead to suboptimal team performance. This paper therefore describes the design of a virtual assistant that supports workload harmonization in teams by measuring workload, informing team members about their own and other team members' workload, and supporting team members in the redistribution of workload. The virtual assistant was developed in the context of train traffic control according to the situated Cognitive Engineering (sCE) methodology, which was extended to allow for a value sensitive design process. More specifically, the values 'insight,' 'helping others' and 'privacy' were explicitly accounted for throughout the design of the virtual assistant. A prototype of the virtual assistant was evaluated positively in a focus group. Thereby, the contribution of the paper is twofold. First, an improvement in a human-centered development methodology-sCE with values-is described and its use is demonstrated in an actual design case. Second, a novel, positively evaluated solution for workload harmonization in teams is presented. [ABSTRACT FROM AUTHOR]

Although post-traumatic stress disorder (PTSD) is well treatable, many people do not get the desired treatment due to barriers to care (such as stigma and cost). This paper presents a system that bridges this gap by enabling patients to follow therapy at home. A therapist is only involved remotely, to monitor progress and serve as a safety net. With this system, patients can recollect their memories in a digital diary and recreate them in a 3D WorldBuilder. Throughout the therapy, a virtual agent is present to inform and guide patients through the sessions, employing an ontology-based question module for recollecting traumatic memories to further elicit a detailed memory recollection. In a usability study with former PTSD patients ( n = 4), these questions were found useful for memory recollection. Moreover, the usability of the whole system was rated positively. This system has the potential to be a valuable addition to the spectrum of PTSD treatments, offering a novel type of home therapy assisted by a virtual agent. [ABSTRACT FROM AUTHOR]

People of low literacy experience difficulties while participating in society. Learning support software could help alleviate these difficulties. However, there is currently no overview of theoretically and empirically sound requirements for this kind of support. This paper uses the situated cognitive engineering method to create a requirements baseline for a virtual environment to support the societal participation education of low-literates (VESSEL), based on an analysis of the domain, human factors, and current applications. Four major outcomes are presented. First, a comprehensive overview is collected of the operational demands and human factors knowledge relevant to societal participation learning for low-literate citizens. Second, this overview is translated into a list of eight functional requirements: focused on low-literate learners, set in the context of societal participation, and supported by claims of cognitive, affective, and social benefits to learning. Third, a sample of Dutch societal participation learning support programs is assessed using these requirements, to highlight both current technology best practices and discrepancies between theory and practice. Fourth, virtual learning environment technology is suggested as an 'enabling' technology; an overview is shown of how virtual environments, actors, and objects can beneficially enable meeting the requirements baseline. Finally, directions for future study are discussed. [ABSTRACT FROM AUTHOR]

Persistent progress in the self-management of their disease is important and challenging for children with diabetes. The European ALIZ-e project developed and tested a set of core functions for a social robot that may help to establish such progress. These functions were studied in different set-ups and with different groups of children (e.g. classmates at a school, or participants of a diabetes camp). This paper takes the lessons learned from these studies to design a general scenario for educational and enjoying child-robot activities during returning hospital visits. The resulting scenario entailed three sessions, each lasting almost one hour, with three educational child-robot activities (quiz, sorting game and video watching), two intervening child-robot interactions (small talk and walking), and specific tests to assess the children and their experiences. Seventeen children (age 6-10) participated in the evaluation of this scenario, which provided new insights of the combined social robot support in the real environment. Overall, the children, but also their parents and formal caregivers, showed positive experiences. Children enjoyed the variety of activities, built a relationship with the robot and had a small knowledge gain. Parents and hospital staff pointed out that the robot had positive effects on child's mood and openness, which may be helpful for self-management. Based on the evaluation results, we derived five user profiles for further personalization of the robot, and general requirements for mediating the support of parents and caregivers. [ABSTRACT FROM AUTHOR]

The aim of our work is to design bodily mood expressions of humanoid robots for interactive settings that can be recognized by users and have (positive) effects on people who interact with the robots. To this end, we develop a parameterized behavior model for humanoid robots to express mood through body language. Different settings of the parameters, which control the spatial extent and motion dynamics of a behavior, result in different behavior appearances expressing different moods. In this study, we applied the behavior model to the gestures of the imitation game performed by the NAO robot to display either a positive or a negative mood. We address the question whether robot mood displayed simultaneously with the execution of functional behaviors in a task can (a) be recognized by participants and (b) produce contagion effects. Mood contagion is an automatic mechanism that induces a congruent mood state by means of the observation of another person's emotional expression. In addition, we varied task difficulty to investigate how the task load mediates the effects. Our results show that participants are able to differentiate between positive and negative robot mood and they are able to recognize the behavioral cues (the parameters) we manipulated. Moreover, self-reported mood matches the mood expressed by the robot in the easy task condition. Additional evidence for mood contagion is provided by the fact that we were able to replicate an expected effect of negative mood on task performance: in the negative mood condition participants performed better on difficult tasks than in the positive mood condition, even though participants' self-reported mood did not match that of the robot. [ABSTRACT FROM AUTHOR]

Professionals working in risky or emergency situations have to make very accurate decisions, while the quality of the decisions might be affected by the stress that these situations bring about. Integrating task feedback and biofeedback into computer-based training environments could improve trainees' stress-coping behaviour. This paper presents and assesses a refined version of the cognitive performance and error (COPE) model that describes the effects of stressful events on decisions as a foundation for such a support tool. Within a high-fidelity simulator of a ship's bridge at the Royal Netherlands Naval College, students of the naval college ( n = 10) were observed while completing a 2-h-long shadowing and boarding operation combined with a search-and-rescue operation. For every action, variables were measured: objective and subjective task demand, challenge and threat appraisal, and arousal based on heart rate and heart rate variability. The data supported the COPE model and were used to create predictive models. The variables could provide minute-by-minute predictions of performance that can be divided into performance rated by experts and errors. The predictions for performance rated by experts correlated with the observed data ( r = 0.77), and 68.3 % of the predicted errors were correct. The error predictions concern the chances of making specific errors of communication, planning, speed, and task allocation. These models will be implemented into a real-time feedback system for trainees performing in stressful simulated training tasks. [ABSTRACT FROM AUTHOR]

This paper describes the project TRADR: Long-Term Human-Robot Teaming for Robot Assisted Disaster Response. Experience shows that any incident serious enough to require robot involvement will most likely involve a sequence of sorties over several hours, days and even months. TRADR focuses on the challenges that thus arise for the persistence of environment models, multi-robot action models, and human-robot teaming, in order to allow incremental capability improvement over the duration of a mission. TRADR applies a user centric design approach to disaster response robotics, with use cases involving the response to a medium to large scale industrial accident by teams consisting of human rescuers and several robots (both ground and airborne). This paper describes the fundamentals of the project: the motivation, objectives and approach in contrast to related work. [ABSTRACT FROM AUTHOR]