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Enabling Individualized and Adaptive Learning – The Value of an AI-Based Recommender System for Users of Adult and Continuing Education Platforms

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Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky (AIED 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1831))

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Abstract

The extent to which individualized and adaptive learning can be supported by recommender systems is increasingly being discussed in the field of adult and continuing education (ACE). Aspects of accessibility and customization of learning platforms play just as much a role as the added value of AI from a pedagogical perspective. This paper addresses the question of how recommender systems can be used to support self-directed learning of adult learners with heterogenous prerequisites and learning needs. Building on the initial situation of the target group as well as assumptions of learning opportunity-use models, an idea of AI and humans acting in partnership is designed and at the same time made to the object of investigation.

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Acknowledgments

This contribution originates from the research project KUPPEL (KI-unterstützte plattformübergreifende Professionalisierung erwachsenen-pädagogischer Lehr-kräfte), funded by the German Federal Ministry of Education and Research (BMBF), ref. no. 21INVI0802, 21INVI0803, 21INVI0805.

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Authors and Affiliations

  1. Universität Tübingen, Münzgasse 11, 72070, Tübingen, Germany

    Sabine Digel

  2. German Research Center for Artificial Intelligence, Parkstr. 40, 49080, Osnabrück, Germany

    Thorsten Krause

  3. German Institute for Adult Education, Heinemannstr. 12–14, 53175, Bonn, Germany

    Carmen Biel

Authors
  1. Sabine Digel
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  2. Thorsten Krause
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  3. Carmen Biel
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Corresponding author

Correspondence to Sabine Digel .

Editor information

Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Ning Wang

  2. University of British Columbia, Vancouver, BC, Canada

    Genaro Rebolledo-Mendez

  3. University of Leeds, Leeds, UK

    Vania Dimitrova

  4. North Carolina State University, Raleigh, NC, USA

    Noboru Matsuda

  5. UNED, Madrid, Spain

    Olga C. Santos

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Digel, S., Krause, T., Biel, C. (2023). Enabling Individualized and Adaptive Learning – The Value of an AI-Based Recommender System for Users of Adult and Continuing Education Platforms. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_121

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  • DOI: https://doi.org/10.1007/978-3-031-36336-8_121

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36335-1

  • Online ISBN: 978-3-031-36336-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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