# AI-Based Learning Recommendations: Use in Higher Education +
## 1 Introduction  
The last decade has seen various systems deploy Learning Management Systems (LMSs) in educational institutions [1, 2]. Such systems are used to support online learning by including educational resource presentation and communication tools such as forums, tests, exercise tools, and administrative functions. An LMS provides the same service to all learners in an institution, rather than personalized learning. Personalized learning environments can be perceived by users as more relevant and motivational [3]. Typically, they have a content recommendation feature, which supports learners in various ways. For example, learners can choose their individual learning path that helps them continuously gain knowledge. Similarly, by recommended learning resources from a large number of resources, learners can avoid an information overload problem. Furthermore, recommendation systems can integrate context information such as prior knowledge, learning goals, progress, etc., to help recommend precise resources and improve the learners' performance [4]. To facilitate personalized learning, various Artificial Intelligence (AI)-based Learning eXperience Platforms (LXPs) have been developed. These platforms are designed to provide personalized learning resources by curating content from various sources and recommending them to learners individually based on their level of learning and personalpreferences [5]. Curated learning resources such as quizzes, digital books, online resources, and Open Educational Resources (OERs) are highly beneficial to learners. However, finding these resources can be quite challenging, especially when there is an overwhelming number of resources [6]. The LXP market is predominantly occupied by proprietary vendors such as Degreed, Valamis, and Learning Pool, which mainly focus on workspace learning [7]. In higher education, LMSs have been in use for several years. However, transitioning to a different platform involves significant costs and effort. Especially in Germany, Moodle and other open source LMSs have more than 50% of the market share [8], prompting questions about whether or not their capabilities can be expanded to include LXP features. Enhancing Moodle with such functions offers advantages such as smooth transitions to new features and provides teachers with flexibility to utilize both the classical LMS tools and the new comprehensive learning support for their students. The literature covers extensively the proposal, documentation, and evaluation of recommendation systems for e-learning content [9; 10; 11; 12]. However, their integration into typical e-learning environments or LMSs is rare. An exception is MoodleRec, a recommendation system integrated as a Moodle plug-in [13]. The plug-in recommends external content, the so-called learning objects, to lecturers. In contrast, the approach described in this paper recommends both internal and external resources for learners. However, AI-driven recommendations in LXPs offer a solution by identifying relevant resources based on the learner's competencies and preferences. These recommendations can be classified into the following types: Content-based recommendations that utilize textual information such as tags, keywords, or short descriptions to suggest learning materials. Collaborative recommendations, on the other hand, are based on the assessment of the past behavior of learners, which can be ratings, reviews, and interactions with learning resources [14]. Hybrid recommendations combine both approaches, incorporating additional knowledge about individual preferences and interests for personalized experience [15]. In our project, the recommended learning resources have a special didactic purpose: As the central goal of the project is to improve the knowledge related to AI, the recommended resources are used as a vehicle to learn about AI. It is called 'learning with AI' [16]. While students learn in their Moodle learning environment, they receive individual recommendations on what they could learn next.