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The ongoing digitalization of the education sector yields great potential through the use of Artificial Intelligence but is decelerated by a necessity for privacy and security. This paper investigates the potential of Federated Recommender Systems in school education as a solution to this problem within a two-cycle design science research approach. Meta-requirements for Federated Recommender Systems are extracted from the literature and evaluated through an educational prototype. To balance the technical evaluation, practical design guidelines are articulated and evaluated by a focus group of experts resulting in tangible guidelines for practitioners and educational stakeholders.

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I Don’t Know Who You Are, but I Know What You Need: Guidelines for Federated Learning in Educational Recommender Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Enrico Kochon,
  • Daniel Stattkus,
  • Simon Binz,
  • Marian Eleks,
  • Nils Lauinger,
  • Philipp Fukas,
  • Ann-Kristin Claudia Müller,
  • Julia Knopf,
  • Oliver Thomas

摘要

The ongoing digitalization of the education sector yields great potential through the use of Artificial Intelligence but is decelerated by a necessity for privacy and security. This paper investigates the potential of Federated Recommender Systems in school education as a solution to this problem within a two-cycle design science research approach. Meta-requirements for Federated Recommender Systems are extracted from the literature and evaluated through an educational prototype. To balance the technical evaluation, practical design guidelines are articulated and evaluated by a focus group of experts resulting in tangible guidelines for practitioners and educational stakeholders.