Abstract
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.
Recommended Citation
Kochon, Enrico; Stattkus, Daniel; Binz, Simon; Eleks, Marian; Lauinger, Nils; Fukas, Philipp; Müller, Ann-Kristin Claudia; Knopf, Julia; and Thomas, Oliver, "I don't know who you are, but I know what you need: Guidelines for Federated Learning in Educational Recommender Systems" (2024). Wirtschaftsinformatik 2024 Proceedings. 23.
https://aisel.aisnet.org/wi2024/23