Locally Checkable Labelings for Leader Election in Programmable Matter and Beyond 

Maria Kokkou. 18.03.2026, 14:00 Uhr, F2.419. 

A Locally Checkable Labeling (LCL) is a specification describing a set of labels that are valid with respect to a set of conditions that characterize a local part of a solution to a global problem. Conditions can only refer to nodes and labels within a constant radius neighborhood of each node. In this talk, we will discuss local labeling schemes whose global consistency implies solutions to leader election which is a classical problem in distributed computing. Contrary to previous LCLs for leader election we assume that nodes do not have unique identifiers (i.e., nodes are anonymous) and we give (i) a labeling scheme of constant size per node for simply connected induced subgraphs of the triangular grid for nodes that can check conditions at distance one, (ii) a labeling scheme of constant size per node for meshed graphs assuming that nodes can check conditions at distance two and (iii) a labeling scheme of constant size per edge for chordal and K_4-free dismantlable graphs for nodes that can check conditions at distance two. 

This talk is based on joint work with Jérémie Chalopin, Victor Chepoi and Shantanu Das.

Final presentation of the VirtuTutor 2 project group

24.03.2026, 14:00 Uhr, F2.540.

This presentation provides an overview of our VirtuTutor2 project group and the work over the past year. The main objective of our project was to improve the interaction between users and the virtual tutor in the serious game “VirtuChemLab” by implementing several key features, including an adaptive feedback mechanism, interactive question handling, context-aware explanations, and a system for estimating user skill levels in categories based on performance.

We will start the presentation with a brief review of the most important organizational milestones and events throughout the project year. We then highlight the key features developed by the team and explain their role in enhancing the virtual tutor. Finally, we reflect on our experiences in the project group, share the main lessons learned, and provide an overall evaluation of the project.

Agentic VirtuTutor: Evaluating the use of an integrated agentic AI learning assistant in the serious game VirtuChemLab

Eric Freier. Masterarbeitsantrittsvortrag. 25.03.2026, 14:00 Uhr, F2.419.

My initial thesis presentation will show how serious games are becoming an important direction for education and how the rise of generative AI enables more personalized tutoring within serious games. This benefits both students’ learning progress and the efficient use of teacher resources. However, single GenAI solutions face limitations when used as tutoring systems in serious games. Agentic AI may address these limitations using specialized agents. 

I will also present my research questions that aim to compare a single GenAI system with an agentic solution regarding the following metrics: correctness, didactical response quality, tool use, and computational resource utilization. The evaluation will be conducted with the VirtuChemLab, which currently employs a single LLM as its virtual tutor. Additionally, I will briefly introduce agentic AI, present the planned evaluation methodology and give an outline on the workplan.