Problem Description

Video meetings have become a central part of modern work life, yet they often include discussions that are not relevant to all participants. This misalignment can lead to frequent multitasking and reduced effectiveness in both the meeting and parallel tasks. To address this, we propose a system that enables personalized meeting agendas. By leveraging Generative AI (GenAI), participants can define key terms or topics in advance, ensuring they receive notifications when their relevant discussion begins. This approach aims to help participants stay focused on meeting content that matters to them while enabling better productivity in concurrent tasks.

Goal of Thesis

The primary goal of this thesis is to design and implement a prototype of a personalized meeting agenda system with a Design Science Research approach. The system should:

1. Utilize GenAI to allow users to specify relevant keywords or topics before the meeting.
2. Integrate a notification mechanism that alerts participants when their topics are being discussed.
3. Offer a user-friendly web-based interface for meeting participants to set preferences and view notifications in real time.
4. Evaluate the system with consideration of the level of study:
   • For a Bachelor thesis: Focus on qualitative evaluation, including usability testing and participant feedback.
   • For a Master thesis: Conduct a quantitative evaluation through experiments, assessing the system's impact on both meeting awareness and productivity in parallel tasks.

Requirements

• Experience with Generative AI models and their integration into applications.
• Proficiency in web programming (e.g., HTML, CSS, JavaScript, Python).
• Understanding of the Design Science Research methodology and ability to conduct user evaluations.
• Strong time management and communication skills and proficiency in English.