Discover Bachelor & Master projects

Development of a Real-Time Energy Visualisation App for the Mobile MINT "Energy Lab"

As part of the regional MINT initiative led in collaboration with the Regionalmanagement KUUSK, a mobile laboratory ("MINT Energy Mobile Lab") is being developed to bring hands-on STEM learning on energy and climate directly to schools. Within this project, your task is to design and implement an application that collects and aggregates real-time watt data from five ergometer bikes during the weekly "Energy Challenge," displaying the results in an engaging and motivating way on a large screen. The project aims to combine physical activity, knowledge building, and teamwork while making energy generation tangible through physical effort. This thesis includes active participation in the project team; a one-year employment of approximately 5 hours per week is planned in addition to the bachelor's thesis.


Developing Unplugged AI Learning Materials for K-12 Students: A Competency-Based Approach

This thesis examines K-12 AI competencies—including algorithmic thinking and ethics—within the context of current educational research [1-3]. Based on a critical review of existing literature, the study develops unplugged learning activities [4,5]. These activities serve as a practical application of theoretical AI literacy frameworks, allowing for a systematic analysis of how foundational concepts can be effectively taught to young learners.

[1] https://dl.acm.org/doi/full/10.1145/3685680
[2] https://dl.acm.org/doi/10.1145/3313831.3376727
[3] https://ojs.aaai.org/index.php/AAAI/article/view/5053
[4] https://www.aiunplugged.org/
[5] https://www.i-am.ai/de/build-your-own-ai.html


Enhancing Pre-Service Teachers' Explanations of Computer Science Concepts through AI-Agent Feedback

This thesis investigates how a visual or acoustic AI-agent system (such as D-ID's AI Agents) can be used in experiments with pre-service teachers to improve their explanations of K-12 computer science content. Building on research about professional knowledge and explaining skills [1-3], the study engages pre-service teachers in explaining selected CS topics (e.g. algorithms, binary numbers, or AI basics), receiving AI-driven feedback, and iteratively refining their explanations. The goal is to evaluate whether interaction with the AI system supports the development of clearer, more structured and pedagogically effective explanations.

[1] Kulgemeyer, C., et al. Professional knowledge affects action-related skills: The development of preservice physics teachers' explaining skills during a field experience. DOI: 10.1002/tea.21632
[2] Findeisen, S., Deutscher, V. K., & Seifried, J. Fostering prospective teachers' explaining skills during university education - Evaluation of a training module. DOI: 10.1007/s10734-020-00601-7
[3] https://link.springer.com/article/10.1007/s10758-025-09875-1
Using: D-ID AI Agents


Designing Inclusive Learning Environments: Assessing Unplugged Computational Thinking Interventions in Early Childhood

This bachelor thesis contributes to the field of inclusive informatics education by investigating how family-centered, unplugged environments can mitigate gender stereotypes in early childhood. Using a systematic design-based approach, the study develops a workshop framework that integrates computational thinking through board games and everyday play. Building on existing strategies for encouraging girls’ engagement in informatics [1,2], the thesis evaluates how targeted interventions can challenge early gender biases.

[1] Miliszewska, I., & Moore, A. (2010). Encouraging girls to consider a career in ICT: A review of strategies. Journal of Information Technology Education.

[2] Şahin Timar, Z., & Mısırlı, Ö. (2023). Effective strategies for encouraging girls in informatics. International Conference on Human-Computer Interaction. Springer.


Developing an AI-Driven Classroom Simulator: Integrating Automatic Speech Recognition, Language Models, and Text to Speech models for Educational Practice

Recent advances in automatic speech recognition, large language models (LLMs), and text-to-speech synthesis have opened new possibilities for creating interactive and realistic educational simulations. This master's thesis explores the development of a simulated classroom environment populated by artificial student agents. These agents are capable of voice-based interaction using state-of-the-art automatic speech recognition (ASR), generative LLMs for dialogue, and expressive text-to-speech (TTS) systems. The primary goal is to identify and integrate the most effective combination of models to simulate realistic classroom conversations, questions, and misunderstandings. Building on recent research in AI-based tutors and synthetic student modelling the thesis includes a benchmarking study of various ASR, LLM, and TTS systems in educational contexts, an implementation of a small-scale simulation prototype, and an evaluation of the system's realism and educational value. The work has implications for teacher training, educational software testing, and research on human-AI interaction in learning environments.

Literature: https://www.sciencedirect.com/science/article/pii/S1096751624000526

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