Education Technologies

In this thesis, the goal is to enhance the contextual awareness of a GPT-based educational chatbot, named IRIS, on the Artemis Learning Platform by incorporating lecture content. To do this, the lecture slides should be embedded into a vector database, and the chatbot should be able to retrieve the most relevant slides based on the user’s query in order to provide the most relevant answer.

The challenges encountered by students during the completion of exercises necessitate the implementation of a proactive assistance mechanism within Artemis. This could potentially be achieved through the integration of generative AI technologies such as ChatGPT. The objective of this thesis is to augment Artemis with the capability to provide automatic and proactive assistance to students when they encounter difficulties. The effectiveness and impact of this approach on the learning experience will be evaluated through a comprehensive assessment.

The thesis aims to enhance the instructor interface and student experience in the IRIS learning platform. The project will introduce a thumbnail grid for instructors to manage slides intuitively, ensuring persistent configurations even when slides are updated. The system will adapt to changes, such as the addition of new slides, by automatically adjusting the selection and maintaining the overall structure. Advanced features like automatic slide number extraction and adaptive reference management will be implemented to maintain continuity when slides are updated. The project also aims to improve the student experience by enabling direct browser viewing of lecture slides, moving away from traditional PDF downloads. This approach will provide a more seamless and integrated learning experience, with functionalities like slide hiding synchronized with exercise deadlines. The in-browser viewing experience will be interactive, allowing students to engage with the material dynamically. Additional features to enhance user experience include interactive annotations, real-time communication integration on lecture slides, and a dark mode option for PDF viewing in the browser. This project will significantly improve the usability and functionality of the IRIS learning platform, fostering a more structured and paced learning environment.

The goal of this thesis is to evaluate the effectiveness of IRIS, a GPT-based chatbot for higher education. The chatbot is integrated into the Artemis learning platform and is designed to provide assistance to students when they encounter difficulties. In addition, the chatbot is capable of answering questions related to the course content. Instructors can benefit from IRIS through assistance in exercise generation.

In this thesis, the effectiveness of IRIS will be evaluated through a combination of quantitative and qualitative methods. The quantitative evaluation will be conducted through a comprehensive assessment of the chatbot’s performance both in real course and experimental settings. The qualitative evaluation will be conducted through a survey of students and instructors and expert interviews. The results of the evaluation will be used to identify the strengths and weaknesses of the chatbot and to provide recommendations for future improvements.

Abstract TBA

Competency-based education gives students transparency and flexibility in their learning process. The usage can enhance motivation, especially in university settings where instructors address students with diverse backgrounds and prior knowledge together. Therefore, Artemis contains the adaptive learning system Atlas to support university teaching. To provide the best experience possible to students, it is essential to understand how students currently interact with the system integrated into Artemis and its impacts on their learning process.

The need for effective digital platforms that support pupils and educators in computer science education is rising. This thesis seeks to evaluate existing platforms, such as Code.org, CS Unplugged, and ScratchEd, based on criteria including accessibility, usability, and adaptability. It will also examine whether these platforms incorporate configurators that allow teachers to input parameters such as pupil age, class size, and lesson duration. Based on this analysis, the thesis will propose recommendations and develop a Minimum Viable Product (MVP) of a configurator tool. The tool will be tested and refined in collaboration with educators to support the creation of tailored lesson plans and improve engagement in diverse classroom settings.

Competency-based education(CBE) enables flexible, skill-centered learning by allowing students to progress based on mastery rather than fixed schedules. Atlas already utilizes machine learning techniques to support educators; nevertheless, further improvements are required to accelerate adoption and improve quality.

This thesis proposes an interactive AI-powered agent that assists instructors in creating, refining, and maintaining competency networks through natural language. Rather than relying on static, one-shot inputs, the agent engages in a dynamic conversation—asking clarifying questions, gathering relevant context, and presenting interactive proposals. This interaction model is designed to reduce redundant AI calls, avoid naive prompting, and ensure that the resulting competency networks align closely with course objectives.

The increasing importance of computational literacy drives efforts to introduce computer science concepts early in education. However, many primary school teachers lack formal technical training, making selecting engaging, pedagogically sound activities difficult. Existing tools often lack personalization, leading to ineffective choices, while disparities in technology access highlight the need to integrate unplugged, digital, and hybrid approaches.

This thesis proposes a recommendation system to help teachers select suitable computer science activities based on age group, format, resources, and educational objectives. The system will leverage a comprehensive tagging framework grounded in pedagogical principles and compare different recommendation approaches for accuracy and scalability. This research aims to empower teachers with a personalized tool that boosts confidence in teaching computer science while enhancing student motivation and creating more inclusive learning experiences.

Doctoral application portals manage admissions and shape candidate experience. Many systems create barriers through complex interfaces and inconsistent design. TUMApply at the Technical University of Munich replaces an earlier system but lacks a clear, inclusive, and consistent interface that supports efficient task completion and equitable participation.

This thesis applies Nielsen’s usability heuristics with gender-sensitive and accessible design practices to identify and address usability barriers and inclusivity weaknesses. The primary contribution is a redesigned interface that demonstrates how doctoral application portals can better serve diverse user needs while maintaining efficiency. This work offers practical insights for building academic platforms that support usability and inclusivity, grounded in systematic analysis, iterative design, and implementation.