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

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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.