Ranzenberger, ThomasFreier, CarolinReinold, LucaRiedhammer, KorbinianSchneider, FabianSimic, ChristopherSimon, ClaudiaFreisinger, SteffenGeorges, MunirBocklet, TobiasSchulz, SandraKiesler, Natalie2024-09-032024-09-0320242944-7682https://dl.gi.de/handle/20.500.12116/44492We present a learning experience platform that uses machine learning methods to support students and lecturers in self-motivated online learning and teaching processes. The platform is being developed as an agile open-source collaborative project supported by multiple universities and partners. The development is guided didactically, reviewed, and scientifically evaluated in several cycles. Transparency, data protection and the copyright compliant use of the system is a central part of the project. The system further employs large language models (LLMs). Due to privacy concerns, we utilize locally hosted LLM instances and explicitly do not rely on available cloud products. Students and lecturers can interact with an LLM-based chatbot in the current prototype. The AI-generated outputs contain cross-references to the current educational video’s context, indicating if sections are based on the lectures context or world knowledge. We present the prototype and results of our qualitative evaluation from the perspective of lecturers and students.enArtificial Intelligence in EducationLearning Experience PlatformOpen Source SoftwareLarge Language ModelsText/Conference paper10.18420/delfi2024_112944-7682