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    <title language="deu">Wie kann videogestütztes Lernen die Erwartungen Studierender und Dozierender erfüllen?</title>
    <abstract language="deu">Im BMBF-Verbundprojekt HAnS entwickeln und implementieren neun Hochschulen sowie drei hochschulübergreifende Einrichtungen ein intelligentes Hochschul-Assistenz-System als Open-Source-Lösung. Videobasierte Lehrmaterialien werden verschriftlicht und durch eine Indexierung Stichwortsuchen ermöglicht; geplant ist, über einen KI-Tutor automatisiert Übungsaufgaben zu generieren. Studierende sollen so in ihrem Selbststudium digital unterstützt werden. Die technische Entwicklung wird interdisziplinär – auch sozialwissenschaftlich und pädagogisch – begleitet und in einem iterativen Vorgehen evidenzbasiert entsprechend Design-Based-Research angepasst. Wissen und Wertesystem der Anwender*innen, Didaktik, Ethik, Akzeptanz und Datenschutz werden dabei im Entwicklungsprozess einbezogen.</abstract>
    <abstract language="eng">In the Project HAnS nine universities and three cross-institutional initiatives have teamed up on behalf of the German Federal Ministry of Education and Research (BMBF) to design and implement an intelligent university assistance system as open source. The goal is to develop an artificial intelligence (AI) tutor for higher education that transcribes video-based teaching material and enables keyword searches via indexing, but furthermore automatically generates exercises. As a result, students will be digitally assisted in their self-study. According to the design-based research idea, technological development is accompanied by an interdisciplinary approach and is checked and continuously altered throughout the developmental process in terms of user knowledge and values, pedagogical knowledge, ethics, acceptability and data protection.</abstract>
    <parentTitle language="deu">Soziale Passagen</parentTitle>
    <additionalTitle language="eng">How can video-assisted learning fulfil the expectations of students and lecturers?</additionalTitle>
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    <title language="eng">Unsupervised Multilingual Topic Segmentation of Video Lectures: What can Hierarchical Labels tell us about the Performance?</title>
    <parentTitle language="eng">Proceedings 9th Workshop on Speech and Language Technology in Education (SLaTE)</parentTitle>
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    <title language="eng">A Multidisciplinary Approach to AI-based self-motivated Learning and Teaching with Large Language Models</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="deu">DELFI 2024, Die 22. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V.</parentTitle>
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    <author>
      <first_name>Thomas</first_name>
      <last_name>Ranzenberger</last_name>
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      <first_name>Sandra</first_name>
      <last_name>Schulz</last_name>
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      <first_name>Carolin</first_name>
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      <first_name>Christopher</first_name>
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      <first_name>Claudia</first_name>
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    <title language="eng">Extending HAnS: Large Language Models for Question Answering, Summarization, and Topic Segmentation in an ML-based Learning Experience Platform</title>
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</export-example>
