Rashid, Sheikh FaisalReichow, InsaBlanc, BeritKiesler, NatalieSchulz, Sandra2024-10-212024-10-212024https://dl.gi.de/handle/20.500.12116/45050The integration of recommender systems (RS) into digital vocational education and training (VET) programs holds significant potential for personalized learning and skill development. While most scientific studies have traditionally focused on the application of RS in higher education, this paper shifts the focus to the VET sector. It provides an overview of the development and application of RS within the context of the German funding program “INVITE”. INVITE supports 35 multi-stakeholder projects fostering innovation in digital learning platforms for VET. Out of the 35 projects, 22 develop RS tailored to different target groups and domains. The RS primarily aim to enhance learner support by recommending adaptive learning paths, personalized learning content, and further training opportunities. Based on the available documentation, this paper provides a structured analysis of the developed RS across diverse VET application domains within the INVITE program.enRecommender SystemsAdaptive learning pathsPersonalized learningVocational Education and TrainingInnovationswettbewerb INVITEText/Conference Paper10.18420/delfi2024-ws-34