@inproceedings{RanzenbergerBockletFreisingeretal.2023, author = {Ranzenberger, Thomas and Bocklet, Tobias and Freisinger, Steffen and Frischholz, Lia and Georges, Munir and Glocker, Kevin and Herygers, Aaricia and Peinl, Ren{\´e} and Riedhammer, Korbinian and Schneider, Fabian and Simic, Christopher and Zakaria, Khabbab}, title = {The Hochschul-Assistenz-System HAnS: An ML-Based Learning Experience Platform}, booktitle = {Elektronische Sprachsignalverarbeitung 2023: Tagungsband der 34. Konferenz M{\"u}nchen, 1.-3. M{\"a}rz 2023}, editor = {Draxler, Christoph}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-95908-303-4}, url = {https://www.essv.de/paper.php?id=1188}, pages = {168 -- 169}, year = {2023}, language = {en} } @inproceedings{RanzenbergerFreierReinoldetal.2024, author = {Ranzenberger, Thomas and Freier, Carolin and Reinold, Luca and Riedhammer, Korbinian and Schneider, Fabian and Simic, Christopher and Simon, Claudia and Freisinger, Steffen and Georges, Munir and Bocklet, Tobias}, title = {A Multidisciplinary Approach to AI-based self-motivated Learning and Teaching with Large Language Models}, booktitle = {DELFI 2024, Die 22. Fachtagung Bildungstechnologien der Gesellschaft f{\"u}r Informatik e.V.}, editor = {Schulz, Sandra and Kiesler, Natalie}, publisher = {Gesellschaft f{\"u}r Informatik}, address = {Bonn}, isbn = {978-3-88579-255-0}, doi = {https://doi.org/10.18420/delfi2024_11}, pages = {133 -- 140}, year = {2024}, abstract = {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.}, language = {en} } @inproceedings{RanzenbergerBockletFreisingeretal.2024, author = {Ranzenberger, Thomas and Bocklet, Tobias and Freisinger, Steffen and Georges, Munir and Glocker, Kevin and Herygers, Aaricia and Riedhammer, Korbinian and Schneider, Fabian and Simic, Christopher and Zakaria, Khabbab}, title = {Extending HAnS: Large Language Models for Question Answering, Summarization, and Topic Segmentation in an ML-based Learning Experience Platform}, booktitle = {Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. M{\"a}rz 2024}, editor = {Baumann, Timo}, publisher = {TUDPress}, address = {Dresden}, isbn = {978-3-95908-325-6}, doi = {https://doi.org/10.35096/othr/pub-7103}, pages = {219 -- 224}, year = {2024}, language = {en} }