To collaborate effectively, humans and AI agents need to trust each other. Communication between teammates is an essential component to achieve this, as it makes the AI system more understandable to humans. However, previous research lacks a focus on ways to build an AI agent's trust in its human teammate and, consequently, on how the AI's beliefs can be communicated to the human. As such, this study explores how real-time visual explanations of the AI agent's trust in its human teammate influence human trust and overall satisfaction. Through a user experiment (n=46) conducted on an Urban Search and Rescue simulation, integrating trust explanations was compared against a baseline containing no such information. Results show a statistically significant increase in both human trust and satisfaction when the explanations are provided, highlighting the need for further exploration into methods of communicating trust.

This thesis proposes and develops an interface and model in which advanced optimisation for general employee scheduling is made available to non-experts in computer science or optimisation. The interface teaches, guides, configures, dynamically creates a constraint programming (CP) model, iteratively improves, decreases black box properties, increases trust in the outcome, and complies with relevant European Union Artificial Intelligence law. The objective of this study is to allow a wider range of organisations to take advantage of CP techniques, with the potential to greatly improve efficiency, reduce unfairness, meet company goals, and improve employee satisfaction.

Employees are assigned to personalised shifts based on the expected demands of departments within an organisation, these are set by the domain expert in the field of employee scheduling. Next, to tailor the model to the organisation’s needs, the domain expert is guided in setting both the restrictive assumptions and priorities of shift assignment. To optimise the generated optimal schedule, it is encouraged to create and compare multiple sets of configurations and subsequent schedules. Historical demand data, organisation structure, contract information, and employee preferences are included. Multiple visual design iterations have been made, after which a working interface has been developed and improved iteratively in conjunction with experts in the field. Validations with external domain experts from various industries and organisations have shown that the interface performed effectively in supporting the objectives. Further research can be done to improve the speed of solving, implement diversity of solutions, support for highly custom constraints based on natural language, or interface reusability for other optimisation problems.

To aid the teachings of machine learning (ML), the usage of elaborative interrogative practice questions (EIPQ) is proposed to increase the long-term memory retention of said teaching. Firstly, the existing expectations of students in the current educational landscape are analyzed by taking a look at the undergraduate course present in Delft University of Technology's own Computer Science and Engineering program (CSE2510). Then, relevant theories and techniques for long-term memory retention through practice questions are introduced and applied to CSE2510 content. Finally, an experiment was carried out where roughly half of the participants made use of these newly created EIPQ, while the other half mostly used existing questions, serving as a control group (CQ). The results showed that, compared to the existing practice questions, the use of the newly created EIPQ had a profound impact on the long-term knowledge retention of the learning content. The participants who made use of EIPQ had an average retention ratio of 0.82, compared to the participants who made use of CQ, who had an average retention ratio of 0.57. Therefore, it is suggested that including EIPQ in our current educational model has favorable benefits to the students' knowledge retention of the learned content. A recommendation is made on how to carry out these methods in practice, keeping compatibility with existing learning objectives in mind.

The increasing presence of Machine Learning in all fields of study requires an improvement in how it is taught. Previous research on this topic examined how to teach ML concepts and highlighted the importance of using technology and leveraging relevant pedagogical content knowledge. It did not compare the impact of previous programming knowledge on the students' approach to solving ML problems. This paper explores the differences in implementation of 60 Machine Learning coding assignments using metrics that were determined by previous research to be a good indicator of code quality and computational thinking. By analysing the code submissions with these metrics, the results show several interesting insights about the students' use of functions, variables and the explanations of their thought process. However, results of the metrics are mostly inconclusive. The results from this study highlight the need for additional research on this topic to ensure that people with limited Computer Science knowledge are able to learn about it and implement it in their disciplines.

This research investigates the impact of goal-oriented visualization on machine learning knowl-edge acquisition, particularly exploring its poten-tial to address procrastination in academic settings. By examining participants with no prior machine learning experience, the study employs compara-tive and correlational measures to analyze quiz per-
formance, study times, and visualization practices. While encouraging trends are observed, the small sample size emphasizes the need for further re-search to definitively establish the impact of visu-alization on academic performance. The study ad-heres to ethical guidelines, ensuring participant pri-vacy and obtaining informed consent, contributing to responsible research practices.

This study delves into machine learning (ML) education by conducting a comprehensive literature review, a targeted survey of ML lecturers in Dutch universities, and a comparative experiment. These methods aid in addressing the challenges of aligning teaching methods with the evolving nature of ML and the growing demands of the field, and fill in knowledge gaps on the success of different teaching methods in ML education. The paper investigates whether traditional methods are effective in equipping future engineers with the necessary skills for tomorrow's challenges, amidst the rapid advancement of ML and its applications. The literature review explores the range of teaching methods in ML education and not only, emphasizing a shift towards technology-enhanced and active learning approaches when teaching ML. A survey of ML lecturers explores the landscape of ML education in Dutch universities. The study investigates teaching methodologies, tools, and challenges, providing valuable insights into the evolving practices of ML instruction. Findings indicate a predominant trend towards adopting a blended approach, with lectures, projects, and group work forming core instructional methods. Virtual environments, active learning strategies, and staying informed through community engagement are highlighted. Word frequency and thematic analyses reveal key themes, emphasizing student-centric learning, practical application, and the integration of diverse teaching methods. Additionally, an experimental comparison of two teaching methods, lecture and jigsaw, sheds light on their seemingly similar efficacy when applied to the domain of ML education. The research contributes to the optimization of ML education practices, offering comprehensive insights for educators and policymakers.

Intelligent agents are increasingly required to engage in collaboration with humans in the context of human-agent teams (HATs) to achieve shared goals. Interdependence is a fundamental concept in teamwork. It enables humans and robots to leverage their capabilities and collaboratively work towards a shared goal, fostering the development of trust through joint activities. Considering the great importance of trust, the effectiveness of trust repair strategies is crucial as they help mitigate the negative consequences of errors, enabling efficient collaboration between humans and robots. For this reason, the effectiveness of the trust repair strategies must be examined comprehensively by taking into consideration multiple factors, including the interdependence relationships within HATs. This paper aims to examine the impact of a mix of interdependence and independence relationships on trust violation and repair, but also on collaboration fluency. Thus, an experiment (n = 30) was conducted to study how interdependence affects trust violation, trust repair and collaboration fluency. Participants collaborated with a robot during a search and rescue mission in a simulated environment. Results show that there is a significant influence of interdependence on trust violations, but not on collaboration fluency or trust repair. Furthermore, the paper also emphasises the need for future research that investigates the effectiveness of trust repair strategies for HATs in different interdependence relationships.

ChatGPT, a cutting-edge technology based on LLM, demonstrated great potential in search tasks. While the importance and potential of ChatGPT are growing, the gap in the understanding of how users interact and engage in ChatGPT search remains open. Past research has extensively examined traditional information search, but there is a need for investigation into user behaviour and engagement in LLM contexts like ChatGPT. To address this gap, our study aims to examine the impact of ChatGPT expertise, topical expertise, and topical interest on user behaviour and user engagement in ChatGPT search as we assume these factors play an important role in shaping user interactions with ChatGPT. We conducted an experiment to investigate the answer by inviting users (N=198) via the crowdsourcing platform to communicate with the mock ChatGPT application and their interactions were recorded for subsequent analysis. Prior to and after their interaction with the mock ChatGPT application, users are requested to complete a questionnaire to gather information about their user profiles and quantify their engagement. Our finding indicates that ChatGPT expertise has a partial influence on user engagement in ChatGPT search, which may highlight the importance of AI expertise in shaping user interactions. Furthermore, our research also indicates that the Affinity for Technology Interaction (ATI) has an impact on user engagement, which underscores the importance of understanding the psychological aspects in the age of artificial intelligence. The results of this study will not only address the current knowledge gap in ChatGPT search but also provide valuable information on how to improve ChatGPT to enhance user interaction experience.

With the growing importance of teamwork in higher education, effective communication and goal congruence have become vital in improving the effectiveness of student teamwork. This study aims todesign and implement an innovative system that combines a goal-setting chatbot and an effort visualizer to facilitate effective collaboration in student teams. We found that unclear goals and lack ofengagement were the main challenges in teamwork. To address these challenges, we developed asystem that combines a chatbot and an effort visualizer designed to enhance team transparency andgoal alignment through technology. The chatbot guides users to set specific collaborative goals throughthe SMART framework. At the same time, the effort visualizer displays each member’s contribution,thus increasing accountability among team members and facilitating greater participation. The systemwe designed was validated in a creative writing task (N=84) in a collaborative scenario. The resultsshowed that either the chatbot or the effort visualizer alone positively impacted student engagementand collaboration. However, when using the chatbot and effort visualizer in combination, there neededto be more clear evidence to improve engagement and collaboration effects further. This finding suggests that we pay more attention to the characteristics of individual tools and combination strategieswhen designing collaboration enhancement tools. This study provides valuable insights into using chatbots and visualization techniques to improve team collaboration and suggests new directions for futureresearch in educational technology.