This thesis investigates how Reinforcement Learning (RL) can increase support effectiveness in virtual coach-based smoking cessation interventions. Such interventions have shown promise in helping people change behaviors such as smoking. However, personalizing the support they provide by accounting for people's current and future states might further increase their effectiveness. States thereby refer to people's relatively stable conditions at certain moments in time, capturing aspects such as motivation, knowledge, or the presence of personal reminders. After deriving general user needs for the support provided by a virtual coach-based smoking cessation intervention from a study with 671 daily smokers, we thus used RL to adapt the support to people's current and future states. Specifically, using data collected from three crowdsourcing studies with each more than 500 participants, we assessed the effectiveness of different RL model components in adapting 1) how people are persuaded, 2) what they are asked to do, and 3) who they are supported by. Our findings suggest that considering current and future states increases the effort smokers spend on smoking cessation activities and helps them build quitting-related competencies over time. Given that model components were derived from behavior change theories, this shows the potential of using psychology-informed RL to create smoking cessation support that is effective in the long run.@en

In this thesis, we focus on developing behaviours for socially interactive agents (SIAs). The context in which the agent is used is a self-regulated learning system for children. We focus on personalising learning objectives and interaction content within an intelligent tutoring system (ITS). We envision a system where children can train diabetes selfmanagement knowledge and skills independent of space and time and in collaboration with the health care professional, legal caretakers, and a SIA. To facilitate long-term interaction with such a system, relevant learning content and appropriate ‘intelligent’ social behaviour of the SIA are necessary. The envisioned system was developed within the Horizon 2020 PAL-project and evaluated in an iterative design process. The main contributions of the research described in this thesis are: insights into the behaviour design for a NAO robot and its virtual avatar, and the formalisation of learning objectives facilitating personalised learning content.
Most studies on SIA behaviour focus on the design of emotional expressions or implement roles (e.g., peer or tutor) that were not validated for perception. We argue that strategical pedagogical interaction style (i.e., style purposefully selected based on knowledge about the user, task and context such as done by teachers in traditional classroom settings) is necessary but not yet sufficiently studied to design meaningful interactions that surpass the initial novelty and fun. Further, we argue that learning content must be relevant to the child’s needs and developmental stage. These two challenges are the subjects of study in the two parts of this thesis.

The main research question addressed in part I is: How to design SIA behaviours that express different pedagogical styles and what is the effect on learning outcomes? We answer this question in the four included chapters.
In a systematic review we focus on non-verbal expressions by parameter-based manipulations of bodily shape and motion of humanoid robots and virtual agents, and how these manipulations are perceived by humans. We present a comprehensive review of peer-reviewed published articles and analyse and summarise the available work. Research in this field is multidisciplinary and shows a large variety in concept definitions, behavioural manipulations and evaluation methodologies. We developed the TAXMOD taxonomy as a starting point to develop a shared understanding and interpretation of research
objectives and outcomes, and to formulate a road-map. We applied TAXMOD to position and compare research, and to explicate progress in this area. We found structural support for the fact that some social signals can be displayed by behaviour manipulation in the form of posture- or motion modulation or designed key expressions (fixed behaviours with a specific target expression). Key findings include: 1) the expression of personality traits using virtual or robot bodies is limited to the trait extraversion; 2) the expression of social dimensions such as warmth, competence and dominance is possible, but only when using the whole body, and more research is needed to disentangle individual effects on friendliness, competence and dominance; 3) the expression
of emotion is restricted to generic positive versus negative signals; and 4) context seems important for users for the correct interpretation of the expressive behaviour.
In a first perception study we evaluate an educational robot displaying non-verbal behaviours expressing high or low warmth and competence with children at primary schools and a camp. We show that style expression by a humanoid robot is possible. Bodily posture, hand gestures and paralinguistic cues were manipulated to evoke an expression of a specific level of warmth and competence. The competence dimension in our model was successful. Warmth manipulations were perceived as intended only in combination with high-competence. Moreover, context influenced children’s perceptions:
at school the robot was perceived warmer and more competent than at camp.
In a second perception study we evaluate an educational robot displaying non-verbal behaviours expressing high or low dominance. We modulate bodily posture and movement, specifically by manipulating body expansiveness. We show the validity of body expansiveness modulation for dominance expression in both postures and gestures and showthat with a limited set of parameterswe can moderate dominance expression. Specific postures and gestures have a natural tendency towards being perceived as more or less dominant. Further, the manipulation effect is consistent for a variety of behaviours except a sitting pose. This study provides evidence that body expansiveness is an important factor for dominance expression and that this effect is independent of specific behaviours and view angle.
We study the effect of stylised behaviours on children’s learning approach and learning gain by having a NAO robot guide children while performing an inquiry-based science learning task where children roll rollers down a slope to discover laws ofmovement, friction and gravity. Robot style is implemented as variations in verbal strategy and nonverbal style expression, resulting in an expert or facilitator interaction style. No effect of robot interaction style on children’s learning approach or gain is reported. Based on only verbal behaviour variations children perceive the explaining robot (either the expert
style or explaining verbal strategy with neutral non-verbal behaviour) as more competent than the robot giving evidence descriptions (either the facilitator style or evidence descriptions verbal strategy with neutral non-verbal behaviour). These perception differences did not impact the learning approach or gain in the present study. We did not find perception differences based on the variation of non-verbal behaviour. We did find that the presence of a robot giving feedback on children during rolling trials did cause children to play longer and do more informative experiments compared to no feedback. However, this difference in learning approach did not impact learning gain.

The main research question addressed in part II is: How to personalise learning content based on personal learning objectives?
First, we look into how learning goals are formulated in pedagogy and ontologies for education: effective learning goals must attune to the appropriate and desired difficulty level. A way to structure this is Bloom’s taxonomy. A learning goal must also have attributes presenting relations between and descriptions of goals. Then, we model educational objectives (i.e., achievements, learning goals and accompanying tasks) in an ontology. The upper ontology structures the classes and relations and defines domain independent constructs (i.e., level and topic). The domain model specifies diabetes self-management training objectives for young children based on current checklists and expert input. The resulting knowledge base was considered relevant to, and covering,
the diabetes domain to a considerable extent. From this we conclude that our upper model adequately supports the formalisation of implicit knowledge of health care professionals on diabetes self-management training. A field study with children with type 1 diabetes in the Netherlands and Italy showed that an SIA-ITS offering tasks based on our model to support basic needs for autonomy, competence, and relatedness of children with diabetes. For the formalisation of domain specific learning goals, achievements, tasks and materials in the knowledge structure we recommend the following design
guidelines: work in a multidisciplinary team (to define an inventory of important learning goals and define learning activities include domain- and pedagogic experts next to knowledge engineers); formulate achievements from logical learning units (e.g., daily challenges) that require a subset of the knowledge and skills encapsulated in the goals to improve relevance; formulate achievements and goals from the perspective of the child to facilitate ownership and increase experienced relevance; and, define user characteristics relevant to goal and/or task selection. For the integration of the knowledge structure in a multi-modal intelligent tutoring system we recommend the following design guidelines: provide instruction and explanation to the child on how achievements, goals and tasks are selected and can be attained (i.e., that progress on a goal is gained by task completion, and benefits earned by this); embed the objectives in the ITS application to make them easily accessible to the (child) user and integrate them in other system functionality such as feedback on progress provided by a SIA; and offer sufficient learning content such as games and quizzes to maintain interest and engagement.
We developed an authoring tool with a tree-based interface adapted from game design for collaborative personal goal setting and monitoring that implements the ontology of diabetes self-management education, and we co-evaluated this interface with health care professionals. We propose the following design guidelines for an authoring tool: provide clear, visual feedback on goal structure, and active state and progress; consistently use a different representation (e.g., shape) for different concepts of the model (e.g., goal and achievement); cover the full domain and different skill levels with the finite
set of goals; and, support assessment of current abilities next to goal setting, progress monitoring and goal attainment registration.
We developed an mHealth dashboard as interface for personal goal and task selection and monitoring, and co-evaluated this interface with children with diabetes. The interface implements our ontology of diabetes self-management education. The following design elements were understandable for all children: colouring indicating status, and navigation between layers of information. Children experienced difficulties interpreting the meaning conveyed in iconic presentations, understanding of the layered information, and navigation. Based on reported usability issues, we present guidelines for
the design of a dashboard for children: provide descriptive labels next to visual elements because children lack experience using apps and thus understanding of icons and such; connect elements accordingly by placing them in close proximity and in boxes with appropriate labels; ease navigation between layers when hiding detailed information to avoid cognitive overload; and avoid cluttering elements such as navigation bars.

The work in this thesis shows that robots can express different pedagogical styles perceivable by young children. Dominance expression is mainly dependent on body expansiveness. Warmth and competence expression rely on a complex set of behaviour modulations. However, current style variations are too subtle to impact learning approach and gain. With respect to content personalisation, we show that a structure for and selection of learning objectives provide both a personalised learning path as well as personalised content.
Overall we conclude that to impact learning approach and gain not only SIA behaviour must be modulated, it must be noticed by the learner as well. Learning objectives and content should be formalised within a structure and a user-friendly interface is needed to select objectives and tasks with accompanying content, and monitor progress. The success of an SIA-ITS depends on the amount of available content and social interaction
capabilities of the SIA.@en

This project aims to harness the potential of music-making and robotic interaction to enhance creative expression and cognitive function among individuals with cognitive impairment and dementia. With the aging population, there is a growing demand for innovative interventions that support cognitive health and active engagement. Music therapy has demonstrated effectiveness in stimulating cognitive function and emotional expression in individuals with dementia. Creative expression through music serves as a unique outlet, fostering cognitive functions and emotional well-being. This study explores the synergy between music therapy and Socially Assistive Robots (SARs) to develop a more immersive therapeutic intervention. It represents an exploratory investigation into an end-to-end robotic intervention, proposing various interaction elements and examining their functionalities. Each element is designed to foster engagement, enhance perceptions of collaboration, and promote feelings of creativity.
In this thesis, we propose an end-to-end interactive music-making experience designed for use with the Pepper robot, an SAR. The system features a user-friendly interface with eight color-coded boxes, each corresponding to a musical note. Users simply tap the boxes to create melodies. The Pepper robot acts as a guide, assisting users in interacting with the interface. It additionally implements an engagement tracking system by monitoring user interaction through the screen taps on the interface and provides real-time feedback and encouragement. If a period of inactivity is detected, Pepper gently nudges the user to re-engage. Furthermore, the robot functions as a collaborative musical partner, providing rhythmic accompaniment if the user desires. The system also records user-created music and provides playback functionality, allowing users to revisit their compositions.
Methodologically, the study involves an end-to-end system comprising an intelligent music-making interface and an interactive robot providing real-time feedback and rhythmic accompaniment. Insights from the exploratory study highlight the benefits of real-time feedback in enhancing engagement, particularly among participants with musical backgrounds. However, rhythmic accompaniment shows mixed results in fostering collaboration, indicating a preference among participants for emotional connection in collaborative settings. Since this is an exploratory study, the empirical study focuses on healthy older adults, a population with an increased risk of cognitive decline. This is because individuals with dementia are a vulnerable group. Music interventions have shown promise in improving cognitive function and engagement in individuals with dementia. Therefore, this study informs the design of future interventions for people with early-stage dementia.\\
Key findings underscore the potential of real-time feedback and interaction in promoting engagement in the activity. The intelligent music interface also shows potential to support creative exploration, albeit with improvements needed for advanced musical participants. Participants appreciate the playback feature, enhancing their sense of creative ownership and motivation. Despite promising outcomes, the study acknowledges limitations in sample size and participant demographics, primarily recruiting from music-engaged older adults rather than the target demographic of individuals with cognitive impairments.
Future research directions include expanding participant diversity, refining robot interaction capabilities, and addressing technical challenges to improve system usability and accessibility. Integrating findings from ongoing research on music and memory could further enhance personalized interventions. Ultimately, this study lays the groundwork for future developments in robotic interventions that promote well-being through music therapy for individuals with cognitive impairments.

This research explores the integration of episodic memory and visual short-term memory in social robots to enhance engagement and interaction with older adults, particularly those with dementia, through art activities such as painting. Dementia significantly impacts millions globally, being one of the leading causes of death worldwide. Furthermore, it creates a substantial burden on caregivers and healthcare systems. This research aims to leverage social robots to provide meaningful engagement, companionship, and emotional support to individuals with dementia.

The study involved developing a painting application using Android Studio and a conversational agent using generative AI. The AI model was enhanced with episodic memory and visual short-term memory, enabling it to reference past interactions through episodic memory and analyze painting progress through recent visual inputs with short-term memory. This qualitative study included 8 participants, and post-session interviews were conducted to gain deeper insights into the participants' experiences with the system.

The study suggests that memory-enhanced social robots can improve engagement and interaction quality among older adults interested in painting, but have limited impact on those uninterested in the activity. Mixed responses were observed regarding the robot's role as a companion, with some participants feeling a sense of company while others did not. Overall, participants did not experience frustration due to the robot's presence.

Challenges include response time and real-time awareness, highlighting the need for a more intelligent system. Given the small sample size of eight participants, future research should involve larger groups for more comprehensive and reliable data.

Recent studies have shown that gesture annotation schemes should account for the multidimensional nature of gestures and define their meaning in terms of referentiality and pragmatic meaning. However, accurately annotating gesture meaning in densely crowded social settings using such a coding scheme remains to be accomplished. This study uses the MultiModal MultiDimensional (M3D) labelling scheme and the EUDICO Linguistic Annotator (ELAN) tool to annotate video data from the Conference Living Lab (ConfLab) dataset. The ConfLab dataset contains 8 video recordings of standing conversations at a conference, captured from an overhead perspective, and low-frequency audio recordings of the conversations. A total of 1119 clips of individual gesture instances are generated. This data is then fed into a VideoMAE model pre-trained on the UCF101 dataset. The model achieves an overall accuracy score of 49% on the test set but shows a significant bias towards one class due to the imbalanced dataset. Due to the small size of the dataset and the similarities between gestures with different meanings, the model cannot identify different gesture types. The results demonstrate that high-frequency audio or transcripts of the conversations are vital to avoid strong and potentially incorrect assumptions when annotating gesture meaning. Further investigation is required into the annotation and classification of pragmatic meanings and Machine Learning solutions for multi-class, multi-label video classification problems.

Cognitive Impairment, commonly termed as Dementia, affects a large number of older adults. People with dementia (PwD) experience cognitive decline that impacts their ability to perform daily activities and maintain social connections. The number of PwD is expected to rise, and unfortunately, there is still no cure. A number of non drug therapies such as music and art therapy are used to stimulate neurons and slow down the progress of the disease. When symptoms become severe, full-time care may be needed, requiring a move to a nursing home for formal care. However, studies show that interaction with peers is limited in a care home, leading to loneliness and reduced quality of life. Caregivers organize group activities like singing and baking to alleviate these feelings, but their busy schedule can limit the frequency of these exercises. This begs the question: Can a robot support caregivers in the provision of group activities in a care home? This would allow caregivers more time to attend to the personal needs of the residents. It not only has the potential to reduce workload of caregivers but also creates opportunities for residents to interact with each other.

In this thesis, we propose the design of a robot which moderates a sing-along activity for a group of people living in a nursing home. An activity in the realm of music therapy is chosen since it is one of the non-drug therapies which has shown to have numerous benefits like improving memory recall, eliciting emotions and aiding relaxation. The robot session is curated to give opportunities for interaction with group members and with the robot too. While the song is playing and participants are singing along with the music, the robot performs engagement detection, and encourages those who do not seem involved.

Multiple ways are proposed to detect engagement in HRI, such as emotion recognition and estimation of head pose. These approaches have limitations in the scope of this thesis, particularly because these methods are not designed for groups of older adults residing in a care home. Given these limitations and our focus on the target group, we propose a novel engagement detection methodology, which leverages the presence of a robot and assigns an active role to it. Instead of relying on conventional passive engagement detection methods, this approach uses an interactive probing technique. The robot prompts users to perform certain actions, and whether or not they respond to it is monitored using pose estimation. This is termed robot engagement. This approach is combined with activity engagement, which indicates whether the participant is singing or not. With this hybrid technique, we aim to increase the efficacy of engagement detection. If a participant is detected as disengaged, either in the activity or in the robot, we provide personalized encouragement and motivation by addressing participants by name. Along with this, the robot also compliments those who actively participate, motivating them further...

With the advancement of technology and the integration of Large Language Models, interactive robots are entrusted with more significant tasks that contribute to human well-being. This transition is particularly significant in educational settings, where social robots assume diverse roles such as teacher, tutor, and instructional tool, aiming to enhance learning experiences. Engagement emerges as a central theme in Human-Robot Interaction studies, especially within pedagogical contexts, where it has a significant impact on learning outcomes. However, optimizing engagement rather than maximizing it may yield superior learning results. This thesis investigates the correlation between the employment of engagement strategies and learning outcomes in prompt-driven discussions with robots, focusing on facilitating self-reflection and understanding personal values among children. Due to limitations with the target population, a total of 55 university students conversed with the robot in two different sessions, where they discussed different situations, how they would react in each, and the reasoning behind their behavior, using the Schwartz values as a basis for the choices. The participants were divided into two conditions, to examine the effect of techniques such as motivational interviewing, cues to images, feedback, and the use of a memory model on the quality of their arguments and their ability to recollect the interaction. Results suggest that the strategies lead to significantly more reflective arguments for the first session, but they equalize in the second interaction. No significant effect was found between condition and participants' identifying their value profile which led to an examination of the assumptions made by the robot. Participants who agreed with the assumptions were more likely to identify their values correctly, but, generally, the value model was unable to fully capture the intricacies of human motivation. Participants found the robot equally likable, a possible result of the novelty effect. On recollection quality, the data shows that the effect of the session is too strong to allow room for the condition. Overall, this study contributes valuable insights into the influence of engagement strategies on learning outcomes in educational interactions, offering guidance for designing more effective and engaging interactions in the future.

Several useful technologies to help people with dementia already exist. Robotic pets, for example, have been shown to alleviate negative symptoms associated with dementia. Most robotic pets, however, only have pet-like functionality. This is useful on its own, but adding additional care functionality would be helpful. A different useful tool for people with dementia is reminder systems. These systems can help them remember to do important daily tasks, like eating meals and taking medicine. These systems, however, usually use a phone as a medium. The user might leave their phone in another room, and if they do not take it to the location where the task is to be completed, they cannot check what the reminder was for.

This thesis aims to combine these two systems, and presents the design of an integrated robot reminder system for people with dementia who live at home. It aims to address the problem of people with dementia forgetting important things, improve their quality of life, and reduce the stress experienced by their informal caregivers. The system consists of an animal-like robotic pet, which delivers reminders for important activities of daily living directly to the user with dementia. It guides them to a location in the house, where a screen tells them what the reminder is for. Through sensors placed in the house, the system can automatically detect whether some reminders have been completed, and can use contextual clues to send reminders at the best time. The research question for this thesis is “How can an integrated robotic reminder system effectively support people with dementia living at home in completing daily tasks while alleviating stress of their informal caregiver?”

Three research topics are explored in the thesis. The first topic is that of value-sensitive design. The two main user groups, people with dementia and caregivers, both have values they want to see reflected in the system. Sometimes, these values may clash. The conflict between these values is explored in this thesis. Secondly, the interaction between the robot and the person in the context of a reminder system is researched. It should be clear to a user that the robot is trying to convey a reminder. The third topic relates to the software architecture and software engineering requirements. Private data should be stored securely, and the requirements should be written to make future development easy. Recommendations are made regarding the ideal setup of the system, ensuring proper security and usefulness. These technical choices were validated and improved through two interviews with experts in the field of privacy.

The system was designed through an iterative process. In order to improve the system and validate its acceptability, two design workshops were held, one with professional caregivers and one with people with dementia living in a care home. We showed them a high-fidelity robot prototype, and showed the caregivers a user interface prototype for a caregiver app. These workshops showed that the combination of the robot and the reminder functionality was appealing, though not everyone was as interested.

The promotion of desirable behaviours, such as socially appropriate or health-promoting actions, can be bolstered through a deeper understanding and awareness of the values that underpin the associated behavioural choices. Various implementations for promoting behaviour change based on goals already exist in Human-Robot Interaction, but, since values are the building blocks for our behaviour an agent can use values for behaviour change. By reflecting on value-related choices in a conversation, a conversational agent can assist in identifying the values at stake. This is particularly significant as these agents serve as accessible and non-judgemental platforms for discussing various concerns in a private setting. Such reflective conversations are time-consuming and may span multiple sessions, necessitating some form of memory (e.g., to reflect on earlier statements and compare the choices made for different situations).

While various robotic agents have been developed to provide behavioural support (e.g., for human health), the absence of a comprehensive memory structure and dialogue strategies capable of fostering personalised, reflective conversations based on the appreciation of certain values and actions in various scenarios through contextualised values remains a challenge. To address this, this study introduces the Contextualised Value Model – a dynamic memory model designed to facilitate value-based reflection and support personalised interactions between humans and robotic agents.

To realise this robotic memory, a conversational agent was designed that could elicit values from participants by discussing various scenarios that happen in daily life and reflecting on said values using perspective-taking and other dialogue strategies.

The evaluation of the Contextualised Value Model focused on three primary aspects: the model's accuracy, the influence on likeability and intelligence, and the effect on participants' value awareness. The model was evaluated during a between-subjects experiment (N=54), consisting of two conditions, one where the robot was able to update and use the Contextualised Value Model, and another one where the Contextualised Value Model was random throughout the conversation.

The outcome measures indicated that the integration of the memory model in conversations led to a personalised and relevant conversation, highlighting the potential of the Contextualised Value Model in enhancing conversation personalisation. Although participants' value awareness and perception of the robot's likeability and intelligence did not significantly differ based on the memory model, the study emphasised the need for extended observation to thoroughly evaluate long-term impacts.

Overall, the Contextualised Value Model presents a promising framework for enhancing personalised interactions in various real-world applications, emphasising the need for further research in this area. The ePartner4all project could be further developed to complement the efforts of primary school teachers and parents in supporting children's self-learning of socially, mentally, and physically desirable behaviours.

Scenario-based learning uses interactive scenarios to present the user with situations that need user input to be resolved in order to teach the user the correct behaviour. Traditionally these scenarios would be presented in a rigid order that is linearly increasing in difficulty. Every student however has a different learning rate. Studies have shown that students can lose motivation when challenged too much or too little. This project aims to improve learning efficiency and user satisfaction by adapting the scenario content to the user's skill and knowledge level.
A driving instructor application was developed where users are presented with short interactive scenarios in a 3D environment where they take control of the vehicle using a steering wheel and pedals and learn to make correct decisions when driving. For the research experiment the sessions were divided into two different modes, linear or adaptive. In the linear mode there was a strict gradual increase in difficulty. In the adaptive mode, the user's performance in the previous scenarios determined which scenario is presented next. The collected on user performance and self-efficacy showed no significant difference in user performance. There was however a significant increasement in self-efficacy when users played the adaptive session. This validates the positive value that personalization could bring to training applications.

In the Netherlands, there is a shortage of primary school teachers, due to this shortage, teachers often do not have a lot of one-on-one time with the students. A social robot could be the solution to creating more one-on-“one” time with the students. In addition to relieving some workload for the teachers, a social robot could assist in gaining insight into personal values. Gaining insight into personal values can contribute to the learning experience by increasing motivation. The value awareness of the children is the learning objective as value awareness is thought to increase the child’s competence and motivation. One of the options to gain insight into contextual values is Socratic Questioning. These questions are created to further the learning experience and are expected to increase value awareness. This thesis investigates how a social robot can assist children in attaining learning goals, reflecting on these goals, and exploring the decisions and values underlying their learning experiences related to neurodiversity. The effectiveness of the robot in achieving learning objectives, encouraging reflection on decisions and gaining insight into personal moral values is shown. To gain insight into personal values, two methods of Socratic Questioning have been compared; Clarification and Implications & Consequences. The participating children of this thesis play an educational game where moral dilemmas are presented. A social robot assists in the game and asks the Socratic Questions. The explanation to the Clarification has proven to be the most effective method out of the two to gain insight into moral values. Moreover, the interaction effect between question type and time shows that the impact of the question type on insight into values varies with the time a question is asked. Qualitative observations show the participants’ anticipation of the robot’s questions and show instances where responses are different from the self-reported value importance. Overall, this research sheds light on the potential of Socratic Questioning and its uses for understanding individual values, calling for further exploration in this area.

Robots in classroom settings can help teachers with providing personalised attention to children's health and development. As part of this personalisation, robots should store and use (verbal or multi-modal) information about the children they interact with. One aspect that has been unexplored in existing literature is the detection of memorable moments during these child-robot interaction. Eye-gaze tracking is a low cost and non-invasive method applied widely to gain insight into human's inner processes. This study has found that several state-of-the-art time series machine learning models perform better-than-chance on the detection of memorable moments using gaze tracking.
In addition, a shapelet-based transform classifier also performed better-than-chance in distinguishing memories according to $3$ different levels of recall detail. Manual data analysis has identified significantly different gaze behaviour during memorable moments and not memorable moments as well as in the gaze behaviour for different levels of recall detail. The comparison of the results with related literature leads to the hypothesis that memorable moments are likely to be moments of both high levels of engagement and deep thinking. The data analyses also provided insight into children's gaze behaviour for different reasons for remembering a moment. The results show that these reasons, or `internal processes', are distinguishable by gaze patterns and thus provide insight into items or concept that draw the child's attention. This study shows that memorable moments detection for children is a developing and promising field that could potentially provide a lot of insight into children's situated thought processes.

Explainable AI (XAI) has gained increasing attention from more and more researchers with an aim to improve human interaction with AI systems. In the context of human-agent teamwork (HAT), providing explainability to the agent helps to increase shared team knowledge and belief, therefore improving overall teamwork. With various backgrounds and characteristics of humans, expert video gamers are found to have better perception and cognitive ability. This study aims to study the effect of information amount in explanations on four factors: subjective workload, teamwork performance, trust, and explanation satisfaction in different expertise levels in human-agent teamwork. To investigate the research question, we designed a simulated search and rescue task, encompassing two types of explanations: the one containing less detailed information, and the other presenting more detailed information. After conducting the experiment with 42 participants, we first divided all participants into three expertise levels based on their self-reported game frequency and the mock task score in the tutorial. Then we statistically analyzed the effect of information amount and expertise levels on the subjective workload, team performance, trust, explanation satisfaction, and activity level. In conclusion, we did not find evidence that adapting the information amount in explanations to gaming expertise levels can yield an improvement in the user experience during simulated search and rescue tasks. However, subjective workload is found to have a negative effect on explanation satisfaction. For future studies, it may be worth investigating whether expert gamers require explanations with very detailed information in HAT.

Longer lifespans and an ageing population put tremendous pressure on the care of the elderly. With the technology of robotics breakthroughs, it appears that the use of robotics in elderly care is ready to take off. Interestingly, more and more robots are now being created with a human-like appearance and demeanour, yet this could lead to issues like inappropriate mental models of robots in the user's mind. On the other hand, due to their minimal communication challenges, robot-like robots may function effectively in the human-robot interaction. Up to now, far too little attention has been paid to the increasing trend of the human-like robot. This thesis compared the performance of these two types of robots, which may help to explain why the trend is the way it is. Two research questions are created to accomplish this goal: Which type of robot, robot-like or human-like, produces the most fluent interaction in the elderly care scenario? Does the robot-like or human-like robot bring about the best memory performance in the music listening activity?

This thesis project aims to contribute to the development of the social robot in elderly care. In this thesis, a music assistance robot was developed, it can converse with the user while listening to music and recalling memories associated with the song. The elder's health and well being are benefited by this pastime. To address the question of which type of robot performs better, two types of robots, a human-like robot and a robot-like robot, were created to compare in terms of fluency in human-robot interactions and performance of memory recall during music listening activities. Four key elements: appearance, behaviour, voice and dialogue were used in the construction of two robot identities. Based on the robot we created, a joint music listening experiment was carried out. For evaluation, a questionnaire and video recordings of the interaction were both employed. This was tested with 30 volunteers (The young adults were chosen for the experiment due to COVID restrictions and time limitation), and it was discovered that the human-like robot performed better overall.

Problem Statement. The responsibility problem in the AI field has been taken seriously. The point of the problem is how to prevent AI systems from making moral decisions for whom they cannot be held accountable. One of the directions to address the responsibility problem is Meaningful Human Control (MHC). Most of the work focused on theoretical definition and measurement ex- plorations and few researchers investigate it by combining application scenarios. Research Question. One of the previous works identified that explanations could be used to improve the subjective feeling of MHC when the effect of con- trol is delayed. Therefore, we wanted to study what type of explanations could facilitate the improvement and how explanations achieved that. Method. We conducted an expert study to obtain advice on selecting explanation types by a ranking question questionnaire and structured questions. Due to the findings that information sharing could improve Situation Awareness (SA), we conducted a pilot study and a user study to study the effect of explanations on different levels of SA and the human feeling of MHC. We used the Situation Awareness Global Assessment Technique (SAGAT) and a Five-point Likert scale question- naire to measure the effect of explanations on SA and MHC. Moreover, we compared the effect of sub-explanations we used in the user study (consequen- tial and counterfactual explanations) with a questionnaire. Results. It was shown that the explanations help to get more overall SA and SA in the pro- jection level. It was also shown that higher SA scores are associated with a better feeling of MHC. However, our result showed that the explanations have no significant effect on the subjective experience of MHC. The findings also in- dicated that a high frequency of playing computer games can result in a good subjective experience of control. Comparisons of the sub-explanations on quali- tative and quantitative analysis demonstrated that counterfactual explanations made a better impression on participants in most respects. Discussion and Conclusion. From the results, we concluded that explanations do increase the degree of SA of the task to a certain extent, but they do not affect the experi- enced control. We also found that the computer gaming experience may provide higher cooperation engagement and cohesion, resulting in increased experienced control. As for the sub-explanations, the counterfactual ones were overall bet- ter than the consequential ones by providing more information and making the participants feel the robot’s intelligence.

This thesis investigates how artificial intelligence can support e-mental health for depression, i.e. the delivery of treatment and prevention interventions for depression using technology. E-mental health for depression is a promising means for bridging the treatment gap since it addresses many of the barriers that prevent people in need of help from seeking or obtaining it. Additionally, many systems have been found to be effective in controlled trials. However, as human support for e-health interventions decreases so do their effectiveness and users’ adherence. While one possible explanation is that human support is a necessary ingredient of a successful intervention, another is that the technology is not satisfying the needs of users to the best of its abilities. This finding inspired us to take a closer look at the technological implementation of the functionality of these systems. To this end, we developed a set of scales that assess the technological sophistication of the functional components of systems, the e-mental health degree of technological sophistication (eHDTS) scales. In a systematic literature review of the field, we then divided all systems developed between 2000 and 2017 for the prevention or treatment of depression reported in the scientific literature into their functional components and rated those components with the eHDTS scales. We found that most systems that had been developed until 2017 were low-tech implementations, consisting mostly of psychoeducation and having a one-way information stream from system to user. This clearly contrasts with face-to-face therapy in which the therapist closely attends to the patient and provides his or her knowledge and insight strategically to signal understanding and empathy, foster self-reflection, teach, or obtain more information. Based on this consideration, we set out to develop a conversational agent capable of signaling to the user that it had processed the content of what it had been told when completing a thought record together with a user in dialog with the hypothesis that this would be able to motivate the user to complete more thought records and feel more engaged. Thought recording is a core technique of cognitive therapy in which patients are asked to systematically monitor their thinking in situations that caused a maladaptive response. Cognitive theory posits that the negative, cognitive appraisals that are responsible for the low mood experienced in patients with depression stem from maladaptive schemas, i.e., beliefs that we hold as truths about the world, ourselves, and the future. To get the conversational agent to “understand” the thoughts provided by the user from this cognitive theory perspective, we collected a corpus of thought records from Amazon Mechanical Turk workers, manually coded the thoughts with respect to the underlying schema, and trained various machine learning models to do the same labeling. A set of deep neural networks outperformed the other algorithms and was then deployed in the conversational agent. We used a between-subjects design to expose 308 participants recruited from Prolific to the conversational agent. The three conditions differed with respect to the feedback-giving capabilities of the conversational agent in response to a thought record: low feedback richness entailed an acknowledgment of the completion of the thought record (thanking the user), medium feedback richness entailed the acknowledgment plus feedback on the process (how many steps the user did in relation to his or her previous thought records), and rich feedback richness entailed medium feedback richness combined with feedback on the content (an interpretation of the thought record with respect to the underlying schema). While all users were able to complete the thought records with the conversational agent, we did not find supportive evidence that the agent’s feedback strategy could increase users’ motivation to complete more thought records or their self-reported engagement in self-reflection. Future research may investigate why we observed these null results by studying whether the feedback is processed correctly, whether a population with depression that is motivated by a wish to get healthy might behave or experience the system differently from our sample that was recruited online and did not meet diagnostic criteria for depression, or whether more advanced social and interaction capabilities need to accompany the complex feedback for it to be believable.@en

Dementia care is a growing problem, both due to a rising number of cases and due to a shortage in healthcare workers. Aside from cognitive symptoms persons with dementia (PwDs) often deal with psychological symptoms such as agitation. The individualized music intervention (IMI) by Linda Gerdner has been proposed to reduce these. This is the basis for our first research question “How can we incorporate the IMI into technology to care for PwDs?". We answer this by dividing the guidelines for IMI into functional components with their own responsibilities. This allows for more independent development of these components. We also research one of the components, a playlist generator. This leads us to our second research question "Assuming knowledge of a list of preferred music for a user, can we generate playlists which better regulate emotions than a random shuffle?". Our generator is based on a Markov Decision Process (MDP) and allows for a target to be set in the 2D valence-arousal space. This was tested in an online experiment with 22 participants. The control group listened to a random shuffle of their preferred music. The experimental group listened to a playlist generated by the MDP. The target was set to a valence > 0.75 (valence ranging from 0 to 1). A significantly higher final valence for the experimental group was observed, showing a clear improvement over the control group when the goal is to positively influence the valence. The dataset obtained through this experiment is available for future research.

In this work, a conversational interaction was designed and implemented to test the effect of references to past events or shared experiences rephrased into motivational phrases within the context of working towards a diet related goal that can assist with type II diabetes over multiple sessions.
Prior works that utilized a memory typically did not utilize a memory to refer to the past within conversations and when past events were referred to within a conversation, they were not utilized with motivational rephrasing. Most prior works did not analyze long term interaction or how references to past events should have been utilized. To further research in this area, the following research questions were posed.
1.) Does referring to previous sessions improve goal attainment?
2.) Does referring to previous sessions improve user experience?
3.) Does a variety of references differ from making the same reference?
To determine the answers to these questions, interactions were carried out through Zoom in a between subjects experiment with three groups of participants.
The results, once analyzed, found that there was a significant difference between interactions that only make references to the same shared experience and interactions that make references to various shared experiences where various shared experiences resulted in a significant increase in the change in motivation from prior to the experiment to during the experiment.
This offers the following contributions:
1.) Provide clarity on how shared experiences should be used and not only whether they should be used.
2.) A motivational memory utilizing shared experiences with intrinsic motivational value
3.) Determine the effect of motivational references on goal achievement and user experience.
This holds promise for the use of such a memory for goal based efforts and future work can further the domains of application and effectiveness of the motivational memory and shared experiences.

Due to the rapid growth of diseases like diabetes, machine-learning (ML) advanced clinical decision support systems (CDSS) that support doctors or patients in their care might prove to be a valuable asset. In the battle to make healthcare more accessible, adopting ML techniques could be supportive in increasing the overall
effectiveness and efficiency of healthcare. However, the development of a CDSS is troubled by multiple problems.

People with diabetes often show symptoms heavily associated with depression, but these symptoms are often caused by the burden of daily diabetes management. The negative feelings caused by this burden are defined as "diabetes burnout''. Some of these negative feelings are caused by social issues. People with diabetes often resort to online sources to find out how to deal with these social issues since health care providers do not focus on this side of diabetes. Furthermore, the social stigma surrounding treatment for mental problems stops people with diabetes from going to a psychologist. In this project, a conversational agent is designed, implemented and evaluated to investigate whether it is capable of reducing social diabetes distress. The agent was designed to give personalized tips based on a social issue the person with diabetes shares. A longitudinal experiment was done over three sessions to evaluate the agent. The results show that the agent is able to reduce the diabetes distress more than a plain textual delivery of tips. The successful application shows the value of conversational agents and provide a basis to deploy such conversational agents in the e-mental health domain. The design we created can be used in future work, where a further personalized approach and a tool measuring the personalization should be investigated in order to better understand why the conversational agent is able to reduce the diabetes distress.