Thesis Supervisions

The topics of the master's theses I supervise cover a broad spectrum of advanced subjects in AI, Data Science, and Machine Learning.

For students seeking thesis supervision: Before contacting me, please refer to the guide available on OLAT, which explains the entire process—from finding a thesis topic to submitting it to the examination office. I strongly recommend that students find their own topics, as this allows them to work on something they enjoy and are motivated by. However, if you have difficulty finding a topic, I can assist in formulating one that aligns with your interests. When reaching out, please include your most recent CV and a transcript of your studies.Open Thesis Topics:

Open Thesis Topics

1.AI-Driven Personalized Microlearning: Enhancing Student Skill Development Through Adaptive Bite-Sized Content Delivery (available from 01.2025)

The rapid evolution of educational technology has highlighted the need for personalized learning experiences that adapt to individual student needs. Microlearning, which delivers content in small, manageable chunks, is particularly effective for skill acquisition. However, the challenge lies in creating adaptive microlearning modules that respond dynamically to each student’s strengths and weaknesses. This research proposes to develop and evaluate AI-driven adaptive microlearning modules that optimize skill acquisition by personalizing content delivery for each learner.

Research Objectives

  1. To design AI-driven adaptive microlearning modules that tailor content to individual student needs.
  2. To evaluate the effectiveness of these modules in improving skill acquisition and retention compared to traditional learning methods.
  3. To assess student engagement and satisfaction with AI-driven adaptive microlearning.

2. EduCreativity: An AI-Powered Tool for Generating Creative Project Ideas Across Multiple Disciplines (availalable from 01.2025)

In today’s interdisciplinary educational landscape, the ability to generate creative and innovative project ideas is essential for fostering holistic learning. However, students often struggle to conceive original and cross-disciplinary projects due to limited exposure and resources. This research aims to develop and evaluate an AI-powered tool, EduCreativity, designed to generate creative project ideas tailored to students in various academic disciplines, enhancing interdisciplinary learning and innovation.

Research Objectives

  1. To develop an AI tool (EduCreativity) that generates creative and interdisciplinary project ideas based on user inputs from various academic disciplines.
  2. To evaluate the effectiveness of the EduCreativity tool in enhancing students' ability to develop innovative projects.
  3. To assess the impact of the tool on interdisciplinary collaboration and learning outcomes.

3. A Data-Driven Approach to Route Selection: Exploring Visualization Schemes for Aggregate Statistical Analysis of Urban Traffic Patterns (available from 01.2025)

    Urban navigation poses significant challenges due to the complexity of traffic networks and the various factors that can influence travel efficiency. Drivers often have multiple route options between two points, each with unique characteristics such as the number of junctions, traffic lights, and average speed. Traditional navigation systems typically recommend routes based on criteria such as shortest distance or fastest estimated time of arrival (ETA). However, these recommendations often fail to account for other important factors that can affect the overall driving experience, such as the frequency of stops, wait times, and route consistency.

    This thesis proposes a data-driven approach to route selection that aggregates various traffic metrics and explores innovative visualization schemes to present this information to users effectively. By evaluating not just the average travel time but also factors like the number of turns at junctions, number of traffic lights, average wait time at those lights, and other relevant statistics, this research aims to develop and evaluate different visualization techniques that can help users make more informed route decisions. Additionally, the research will explore the use of Large Language Models (LLMs) to automatically generate summary explanations of route comparisons based on these statistics.