@misc{11405,
  abstract     = {{This paper presents the ongoing development of HAnS (Hochschul-Assistenz-System), an Intelligent Tutoring System (ITS) designed to support self-directed digital learning in higher education. Initiated by twelve collaborating German universities and research institutes, HAnS is developed 2021–2025 with the goal of utilizing artificial intelligence (AI) and Big Data in academic settings to enhance technology-based learning. The system employs AI for speech recognition and the indexing of existing learning resources, enabling users to search and compile these materials based on various parameters. Here, we provide an overview of the project, showcasing how iterative design and development processes contribute to innovative educational research in the evolving field of AI-based ITS in higher education. Notwithstanding the potential of HAnS, we also deliberate upon the challenges associated with ensuring a suitable dataset for training the AI, refining complex algorithms for personalization, and maintaining data privacy.}},
  author       = {{Schmohl, Tobias and Schelling, Kathrin and Go, Stefanie and Freier, Carolin and Hunger, Marianne and Hoffmann, Franziska and Helten, Anne-Kathrin and Richter, Florian}},
  booktitle    = {{Conference proceedings. 13th international conference "The future of education". Hybrid edition, 29-30 June 2023. Bologna }},
  isbn         = {{979-12-80225-59-7}},
  issn         = {{2384-9509}},
  keywords     = {{Künstliche Intelligenz, Intelligentes Tutorsystem, Adaptiver Unterricht, Lernen, Lehrmaterial, Lernmaterial, Digitale Medien, Hochschule, Hochschulbildung, Deutschland, Selbst gesteuertes Lernen, Technologieunterstütztes Lernen}},
  location     = {{Bologna}},
  pages        = {{4}},
  publisher    = {{?}},
  title        = {{{Combining NLP, speech recognition, and indexing. An AI-based learning assistant for higher education}}},
  doi          = {{10.25656/01:27908}},
  year         = {{2023}},
}

@inbook{11408,
  abstract     = {{Innovationen in der Hochschulbildung führen nicht per se zu gesteigerter Nachfrage oder Akzeptanz aufseiten der relevanten Bildungsgruppen (Krone & Pinkl, 2017). Im Fall KI-gestützter Innovationen im Kontext akademischen Lehrens und Lernens kommt erschwerend hinzu, dass weder Lernende noch Lehrende der Technik unvoreingenommen begegnen: Vorbehalte gegenüber KI werden hier zu kritischen Erfolgsfaktoren für die Implementierung innovativer Technologie (Schäfer & Keppler, 2013, S. 5). Während im technologischen, bildungswissenschaftlichen und didaktischen Bereich bereits Forschungsergebnisse und auch erste Implementationen vorliegen, ist die Akzeptanzforschung vor allem in Bezug auf die Studierendenperspektive gegenüber KI-Anwendungen noch vergleichsweise gering entwickelt (Kieslich et al., 2019). Der vorliegende Beitrag adressiert diesen »blinden Fleck« anhand einer Übersicht über grundlegende Konzepte und Modelle aus dem Bereich der Akzeptanzforschung. Anhand aktueller KI-Trends werden zunächst allgemeine Überlegungen zur Akzeptanz abgeleitet. Auf Basis theoretischer Grundlagen zum Gegenstand der Akzeptanz, der Exemplifikation an einem Fallbeispiel sowie einer Synopse relevanter Modelle wird anschließend kritisch diskutiert, welche Bedeutung der Akzeptanzforschung zum KI-Einsatz in der Hochschulbildung künftig zugemessen werden kann. Der Beitrag stellt eine Anregung und Orientierung für empirische Untersuchungen dar.}},
  author       = {{Watanabe, Alice and Schmohl, Tobias and Schelling, Kathrin}},
  booktitle    = {{Künstliche Intelligenz in der Bildung}},
  editor       = {{de Witt, Claudia  and Gloerfeld,  Christina  and Wrede,  Silke Elisabeth }},
  isbn         = {{978-3-658-40078-1}},
  pages        = {{263--289}},
  publisher    = {{Springer Fachmedien Wiesbaden}},
  title        = {{{Akzeptanzforschung zum Einsatz Künstlicher Intelligenz in der Hochschulbildung : Eine kritische Bestandsaufnahme}}},
  doi          = {{10.1007/978-3-658-40079-8_13}},
  year         = {{2023}},
}

@inbook{11418,
  abstract     = {{This paper presents the ongoing development of HAnS (Hochschul-Assistenz-System), an Intelligent Tutoring System (ITS) designed to support self-directed digital learning in higher education. Initiated by twelve collaborating German universities and research institutes, HAnS is developed 2021–2025 with the goal of utilizing artificial intelligence (AI) and Big Data in academic settings to enhance technology-based learning. The system employs AI for speech recognition and the indexing of existing learning resources, enabling users to search and compile these materials based on various parameters. Here, we provide an overview of the project, showcasing how iterative design and development processes contribute to innovative educational research in the evolving field of AI-based ITS in higher education. Notwithstanding the potential of HAnS, we also deliberate upon the challenges associated with ensuring a suitable dataset for training the AI, refining complex algorithms for personalization, and maintaining data privacy.}},
  author       = {{Helten, Anne-Kathrin and Schmohl, Tobias and Schelling, Kathrin and Go, Stefanie and Freier, Carolin and Hunger, Marianne and Hoffmann, Franziska and Richter, Florian}},
  booktitle    = {{Conference proceedings. 13th international conference "The future of education". Hybrid edition, 29-30 June 2023}},
  isbn         = {{979-12-80225-59-7}},
  issn         = {{2384-9509}},
  keywords     = {{Künstliche Intelligenz, Intelligentes Tutorsystem, Adaptiver Unterricht, Lernen, Lehrmaterial, Lernmaterial, Digitale Medien, Hochschule, Hochschulbildung, Deutschland, Selbst gesteuertes Lernen, Technologieunterstütztes Lernen}},
  location     = {{Bologna}},
  publisher    = {{Filodiritto Editore}},
  title        = {{{Combining NLP, Speech Recognition, and Indexing}}},
  doi          = {{10.25656/01:27908}},
  year         = {{2023}},
}

@misc{9930,
  author       = {{Lange-Hegermann, Markus and Schmohl, Tobias and Watanabe, Alice and Schelling, Kathrin and Heiss, Stefan and Rubart, Jessica}},
  booktitle    = {{Künstliche Intelligenz in der Hochschulbildung: Chancen und Grenzen des KI-gestützten Lernens und Lehrens}},
  editor       = {{Schmohl, Tobias and Watanabe, Alice and Schelling, Kathrin}},
  isbn         = {{978-3-8376-5769-2}},
  pages        = {{161--172}},
  publisher    = {{transcript Verlag}},
  title        = {{{KI-basierte Erstellung individualisierter Mathematikaufgaben für MINT-Fächer}}},
  doi          = {{10.14361/9783839457696-009}},
  volume       = {{4}},
  year         = {{2023}},
}

@book{9984,
  editor       = {{Schmohl, Tobias and Watanabe, Alice and Schelling, Kathrin}},
  isbn         = {{978-3-8376-5769-2}},
  pages        = {{ 286}},
  publisher    = {{transcript Verlag}},
  title        = {{{Künstliche Intelligenz in der Hochschulbildung : Chancen und Grenzen des KI-gestützten Lernens und Lehrens }}},
  volume       = {{4}},
  year         = {{2023}},
}

@misc{12796,
  abstract     = {{This Design-Based Research (DBR) project aims to develop an intelligent tutoring system (ITS) for higher education. The system will collect teaching and learning materials in audio and video formats (e.g., podcasts, lecture recordings, screencasts, and explainer videos), and store them on a learning experience platform (LXP). Then, the ITS will process them with the help of speech recognition to gain data which, in turn, will be used to power further applications: Using artificial intelligence (AI), the platform will allow users to search the materials, automatically compiling them according to criteria like lesson subject, language, medium, or required prior knowledge. By the end of the last DBR cycle, the ITS will also provide a more active form of support: It will automatically generate exercises based on predefined patterns and teaching materials, thus allowing learners to check up on their learning progress autonomously. In order to closely match the ITS's features to the needs and learning habits of students in higher education, the development of this AI-based tutoring system is accompanied by an interdisciplinary team which will continuously re-evaluate and adapt the concept over the course of several DBR cycles. Our goal is to derive implications for the system's technical development by collecting and evaluating educational research data (mixed methods design; primary and secondary research methods).}},
  author       = {{Schmohl, Tobias and Schelling, Kathrin and Go, Stefanie and Thaler, Katrin Jana and Watanabe, Alice}},
  booktitle    = {{Proceedings of the 14th International Conference on Computer Supported Education - Vol. 2}},
  editor       = {{Cukurova, Mutlu  and Rummel, Nikol  and Gillet, Denis  and McLaren, Bruce  and Uhomoibhi, James }},
  keywords     = {{Artificial Intelligence in Higher Education, Design-based Research, Intelligent Tutoring System, Participatory Technology Design, Scoping Review}},
  location     = {{Online}},
  pages        = {{179--186}},
  publisher    = {{SCITEPRESS - Science and Technology Publications}},
  title        = {{{Development, Implementation and Acceptance of an AI-based Tutoring System: A Research-Led Methodology}}},
  doi          = {{10.5220/0011068500003182}},
  year         = {{2022}},
}