The Experience Sampling Method (ESM) is increasingly recognized for its ability to capture fine-grained, real-time insights into individuals’ emotional and behavioral states in their everyday environments. While the utility of ESM in clinical contexts has been well-documented, its integration into practical tools for mental health professionals remains underexplored. This study investigates how meaningful insights can be derived and presented from ESM data to assist clinicians in the identification of mental health symptoms and intervention planning. A series of prototype visualizations, in the form of line charts, pie and bar charts and network diagrams, were developed and then evaluated for usefulness, user-friendliness and intuitiveness by a total of 8 participants. Results suggest that no single visualization is sufficient on its own, but that a combination of temporal, contextual, and relational visualizations provides a more complete view of a patient’s emotional patterns. While bar charts were preferred for clarity and comparison, pie charts were appreciated for quickly identifying dominant factors by a few participants. Network diagrams, though initially less intuitive, were still valued when paired with descriptive captions. These findings support the potential of multi-format visual tools to assist clinicians in extracting meaningful insights from ESM data.

Formative assessment has been shown to improve student engagement and learning outcomes across several subject domains in K-12 education. However, its effectiveness within the subject domain of digital tooling remains understudied. This research investigated the effect of computer assisted formative assessment on learning outcomes and behavioral student engagement, with the latter as a potential mediating variable, within the subject domain of digital tooling.

This research conducted a quasi-experiment with 122 second-grade students from a Havo/Vwo high school during a course on Google Spreadsheets. The experimental group had access to a button that let the computer instantly check their homework and provide feedback, while the control group did not. At the end of the course, students in the experimental group completed a questionnaire to share their experiences.

This research found no significant effects of the intervention on behavioral student engagement and learning outcomes, nor was a mediated relationship established. However, students did report several cognitive and metacognitive benefits. Namely: enhanced motivation, enhanced self-regulated learning, enhanced autonomy, feedback that helped them move forward and an improved understanding of the material. On the other hand, they reported some downsides. Namely: increased time consumption, a lack of added value and a lack of feedback quality.

Since this research did not find effects of computer assisted formative assessment similar to those observed in other subject areas, it raises the question of whether the subject domain of digital tooling interacts differently with this type of intervention. Further research with a greater sample size and statistical power is needed to explore this possibility. Future research could investigate which characteristics make this subject domain distinct, in which contexts within digital tooling this type of intervention is most effective and which benefits it should aim to provide.

Continual learning (CL) enables intelligent systems to continually acquire, adapt, and apply knowledge, representing a dynamic paradigm in AI. For embodied agents—interacting with their environment physically and cognitively—CL enhances adaptability and reduces training costs significantly. In this literature review, we contribute by focusing on the application of CL in such agents, showcasing the approaches, means of evaluation and practical uses of this cognitive framework in real-world scenarios. We conclude that while CL holds promise for embodied agents, there exists a notable gap between the theoretical evaluation of CL and the complex real-world scenarios these agents operate in.

Virtual agents have demonstrated remarkable progress in both competitive and cooperative en- vironments. Embodied agents, which enhance AI interactions with the physical world, show great promise for a variety of use cases in both virtual and non-virtual settings. This literature review ex- amines the intersection of embodied virtual agents with cognitive and social frameworks derived from human behavior, focusing on open-ended learning and intrinsic motivation. These paradigms, inspired by human learning and adaptability, offer a path to- wards addressing the limitations of current artificial systems. The literature survey provides a thorough analysis of the research landscape, discussing the definitions, applications, and benefits of embodied agents in virtual settings. Furthermore, it evaluates the methods and benchmarks used to assess the ca- pabilities of these agents, while offering possible solutions for developing the next generation of embodied agents.

In the future, autonomous social robots are expected to seamlessly integrate into our society. To be perceived as interactive partners rather than mere tools, these robots must be embodied and capable of navigating complex, dynamic environments. This study explores the critical role of embodiment and examines the application of human-inspired socio-cognitive mechanisms in embodied agents. We conducted a systematic scoping review of 36 publications sourced from Scopus, IEEE Xplore, Web of Science, and PubMed. Our findings reveal a diverse array of human-inspired implementations in embodied agents, including curiosity-driven learning, categorical thinking, and predictive learning. Notably, human-like eye-gaze plays a crucial role in enhancing the anthropomorphism of these agents. By drawing inspiration from human interactions, we can transition from pre-programmed robots to fully autonomous agents that exhibit emergent behaviours and adapt to ever-changing conditions. Embodiment allows agents to communicate their intentions and desires through various modes, facilitating richer interactions. Additionally, multiple studies underscore the importance of using embodied agents to study human behaviours. Furthermore, the choice of embodiment type must depend on the task at hand; for some tasks, virtual embodiment is more effective, while for others, physical embodiment is preferable. This review provides a comprehensive starting point for future researchers interested in developing human-inspired embodied agents.

This research paper aims to present how Theory of Mind (ToM) - the ability that allows humans to attribute mental states to others - can be used in the context of physically and virtually embodied computational agents. The focus is on using ToM for perspective-taking in environments with multiple computational agents interacting together. A Systematic Literature Review (SLR) was conducted providing a structured search process for collecting the literature. The findings from this review were obtained from 38 selected papers found in the literature since 2018. The review presented how computational ToM was inspired by the human ToM. Further, it summarised the current applications for perspective-taking for multi-agent settings, and it collected insights on high-level implementations of ToM agents. The findings highlight the complexity of ToM agents and the importance of ToM for agent interactions.

Active inference is a theory of the human brain characterising behaviour that minimises surprise. The free energy principle accounts for the adaptive behaviours of organisms through action, perception, and learning aimed at optimising reward or surprise. This study systematically reviews relevant literature to address their methodologies, relevance to mimicking social human behaviour, challenges, and limitations to guide future research by succinctly reporting previous findings and research gaps. Active inference models are extended with deep active inference, free-energy models, multimodal deep belief networks, predictive coding, and probabilistic programming. These models employ goal-directed, epistemic, reward-seeking, and decision-making behaviours and simulate cumulative culture. However, some of these models do not translate well to complex real-life applications due to their simplicity, computational demands, or the assumptions upon which they are based. Challenges with real-life applications include difficulty scaling to high-dimensional data and model simplicity. Furthermore, some experiments did not have enough data to validate or train their models.

Due to climate change, man-made conflicts, and rising inflation, a growing number of people around the world are struggling to have consistent access to safe and nutritious food. This phenomenon is known as food insecurity (FI). Therefore, we take in this thesis the first steps towards developing a monitoring process for assessing FI using Human-Centered AI (HCAI). We developed this process for, and in collaboration with, the Integrated Food Security Phase Classification (IPC). The IPC is an organization that helps countries classify levels of food insecurity in their regions to inform humanitarian decision-making. During our research process, we found that any form of HCAI for the IPC would need to be informed by input from their domain experts, and we concluded that we could not start implementing HCAI until we found a way to formalize their input in a way that was robust and suited their technical capabilities. To this end, we ran an experiment with 18 IPC experts in Malawi to see whether they could quantify their assumptions by setting thresholds for food security drivers. The results are encouraging but show that there is still much to be done to bridge the gap between domain knowledge and technical expertise. We also show in this thesis that there is a lack of real-life case studies on HCAI development and share therefore our lessons learned from our real-world HCAI case study on FI monitoring. In this way, we hope to promote the development of a practice-informed methodology for HCAI.

The phenomenon of one element moving and progressively overlaying another is common in nature, such as waves swashing and backwashing, or eyelids moving over eyeballs while blinking. Folding Texture, which was proposed by Thorben, can simulate this texture “folding” visual effect in real-time without changing geometry.
However, to date, no tool has been developed to assist in the design and synthesis of folding textures. Applications of the technique so far are achieved through manual creation of the folding texture, which is a tedious process.
This thesis explores the problem of folding-texture design and synthesis. A novel approach is proposed for animating still images based on the folding texture technique. The approach uses a semi-automatic, user-assisted method that combines texture editing, motion profile specification, and folding texture synthesis into one seamless process, reducing the need for extensive manual work. It enables novice users to utilize the technique with a fair level of prior knowledge of folding texture.

Software testing, a critical phase in the software development lifecycle, is often hindered by the time-intensive and costly manual creation of test cases. While automating test case generation could mitigate these challenges, its adoption in the industry has been limited due to difficulties in comprehending the generated test cases. To address this, our study presents an approach for clustering test cases and evaluates its impact on the comprehensibility of test suites through empirical research. Our approach clusters test cases based on their covered objectives, grouping together those with similar attributes to enhance developer understanding. The core of our empirical research evaluates developer agreement with our clustering method and contrasts the comprehensibility of clustered versus non-clustered test suites. Findings suggest a broad agreement among developers in favor of our clustering approach, with clustered test suites facilitating faster software maintenance tasks. Notably, the effectiveness of task completion remained comparable between both suite types. In summary, our research introduces and validates an innovative test case clustering strategy, striving to enhance the comprehensibility of automatically generated test suites.

Reading is an essential skill for any child to learn, and finding enjoyment in it can greatly contribute to developing proper reading comprehension. Finding the books they like could prove to be difficult. Utilizing collaborative filtering recommender systems to recommend books to children is a tricky task, the lack of user feedback makes it difficult to accurately recommend books they would enjoy. Using content based recommender systems might be preferable, but what book features could a recommender system like this base recommendations on? This research explores the idea of utilizing the textual complexity of books and their descriptions as such a possible feature. By evaluating how accurate readability formulas can predict the age a book is intended for, how the variability and length of sentences vary per age and analysing the difficulty of words used, this paper finds that the descriptions of books intended for younger audiences might not be aimed at them, but instead at their parents. These findings imply that basing recommendations of the textual complexity of book descriptions might not be the most useful feature to base recommendations of.

Recently, a few children-centered recommendation systems have been created and evaluated. How- ever, these systems required user interaction to cre- ate ground truth to evaluate the result. This research aims to compare some of the traditional recommen- dation models and explore which trait could impact the recommendation process most for different age group users. The result shows the children friendly model does not achieve higher accuracy than the traditional recommendation model. But the book length model and emotion analysis model shows the potential of a good RS that can help children choose books, and cover image recommendation models are only working with younger age

De Kindertelefoon is a children's helpline aimed at providing (pre-)adolescents with a person to talk to for a variety of subjects such as bullying, sex, and abuse. These people who talk for De Kindertelefoon need proper training and guidance. Among the tools that De Kindertelefoon provides is the 5-phase model, a conversational model. Researchers set up a simulation of a virtual child and De Kindertelefoon to help teach the 5-phase model. However, simulation alone was not enough, and we wanted to see if a feedback system could improve the results, with immediate feedback being the focus.
Immediate feedback is when the user fills in an answer and the system immediately provides information to strengthen knowledge.
When applied to an academic environment, both immediate feedback and student engagement have been proven to be important for completing a task.

Using a literature review, we found that immediate feedback can be directly linked to educational techniques such as self-improvement, self-efficacy, and the Self-Determination Theory. Through a focus group, we also found that constructive immediate feedback is an important pillar of De Kindertelefoon.

The design was achieved by looking at the existing and limited 5-phase model as a graph and trying to find an optimal path through that graph.

After conducting a within-subjects study experiment with 34 participants, the results were inconclusive, with neither condition appearing to be more useful for the group of participants, nor either condition being better at teaching them the 5-phase model.
As the results were inconclusive, the data was explored more by looking at it as a between-subject study, which showed that the explanation sheet might perform better for knowledge.

The research shows the possible strength of feedback in a practical manner. From the results, the immediate feedback is neither more nor less resonant than a classical approach to teaching the 5-phase model.

In our digital society, having computer science skills is becoming imperative, yet there is a shortage of computer science professionals and teachers. This shortage is linked to the perception people have of computer science and computer science professionals. This research paper answers two questions through a mixed-method study: to what extent do 7th-grade students with dissimilar backgrounds differ in their perception of computer science?” and “which characteristics of an intervention influence students’ perception of computer science?” The first question is answered by the distribution of a survey for 7th-grade students and the second by letting a smaller group play a serious game and asking open-ended questions afterward. The quantitative results showed that students’ gender influences the perception of computer science. The qualitative results showed that the factors backstory, interaction, and easiness increased students’ interest in computer science but their intention to pursue a career in computer science remained unchanged.

Increased levels of user control and feedback incorporation in learning systems is commonly cited as good AI development practice. However, the evidence as to the exact effect of perceived control over trust in these systems is mixed. This study investigates the relationship between different dimensions of trust and perceived control in postgraduate student burnout support chatbots. We present an in-between subject controlled experiment using simulated therapy-goal learning to study the effects of goal editing and feedback incorporation on perceived agent benevolence and competence. Our results showed that perceived control was moderately positively correlated with benevolence (r = 0.448, BF10 = 7.150), and weakly correlated with competence, and general trust.

Earthquake prediction is the field of seismology concerned with predicting the time, location, and magnitude of earthquakes within a small time frame, usually defined in terms of minutes or seconds before an event. Such predictions can have a large impact on minimizing the damage caused by these seismic events, by providing early warnings to the affected population and allowing them to respond appropriately. Although the methods used to predict earthquakes are often limited to larger magnitude events, predicting micro-earthquakes is also an important task, especially in locations that are more vulnerable to seismic shocks. Concentrations of localized micro-earthquakes can also hint at larger future seismic events, and their location can be used to locate moving fault lines underground. Deep learning methods perform particularly well in this context, due to their ability to quickly identify patterns in large volumes of data, and Long-Short Term Memory (LSTM) neural networks are very well suited to handling time-sequenced data such as the seismic waves used in earthquake prediction problems. In this paper, an LSTM network is trained to predict micro-earthquakes three seconds before the event, using seismic recordings from the New Zealand dataset: the goal is to find the optimal size of these recordings and understand how different values affect the model. Our results suggest that larger recordings do not provide any benefit in performance and that high levels of accuracy can be reached with smaller samples. This means that in the context of micro-earthquake prediction, primary waves can be easily detected in short recordings, and are likely to travel close to shear waves. This can be attributed to the low strength of signals that micro-earthquakes generate, as they will travel shorter distances than major earthquakes.

Due to the devastating consequences of earthquakes, predicting their occurrence before the first strike has been a long standing research topic. Deep learning models have been used to facilitate prediction, using seismograph data to attempt to classify an earthquake right before it happens. However, this is a difficult task and research needs to be conducted into how properties of earthquakes impact the accuracy of models. Thus earthquake focal depth was studied as a factor in prediction accuracy, specifically comparing deep and shallow earthquakes, split along a depth of 70km. An LSTM model was trained using these different data sets, providing 30 seconds of seismic waveform data and given the task to predict the occurrence of an earthquake 3 seconds in the future. Training this model 20 times with each data set resulted in the accuracies of 0.869 for shallow earthquakes and 0.850 for deep ones. Thus the results show that both shallow and deep earthquake trained models performed similarly well.