TY - CHAP A1 - Ranzenberger, Thomas A1 - Freier, Carolin A1 - Reinold, Luca A1 - Riedhammer, Korbinian A1 - Schneider, Fabian A1 - Simic, Christopher A1 - Simon, Claudia A1 - Freisinger, Steffen A1 - Georges, Munir A1 - Bocklet, Tobias ED - Schulz, Sandra ED - Kiesler, Natalie T1 - A Multidisciplinary Approach to AI-based self-motivated Learning and Teaching with Large Language Models T2 - DELFI 2024, Die 22. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V. N2 - We 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. UR - https://doi.org/10.18420/delfi2024_11 Y1 - 2024 UR - https://doi.org/10.18420/delfi2024_11 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-51089 SN - 978-3-88579-255-0 SP - 133 EP - 140 PB - Gesellschaft für Informatik CY - Bonn ER - TY - CHAP A1 - Ranzenberger, Thomas A1 - Bocklet, Tobias A1 - Freisinger, Steffen A1 - Georges, Munir A1 - Glocker, Kevin A1 - Herygers, Aaricia A1 - Riedhammer, Korbinian A1 - Schneider, Fabian A1 - Simic, Christopher A1 - Zakaria, Khabbab ED - Baumann, Timo T1 - Extending HAnS: Large Language Models for Question Answering, Summarization, and Topic Segmentation in an ML-based Learning Experience Platform T2 - Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. März 2024 UR - https://doi.org/10.35096/othr/pub-7103 KW - hans KW - learning experience platform KW - speech processing KW - natural language processing KW - large language models Y1 - 2024 UR - https://doi.org/10.35096/othr/pub-7103 SN - 978-3-95908-325-6 SP - 219 EP - 224 PB - TUDPress CY - Dresden ER -