A Knowledge-Model for AI-Driven Tutoring Systems Andreas Baumgart a,1 and Amir Madany Mamlouk b a Engineering and Computer Science, HAW Hamburg, Germany b Neuro- and Bioinformatics, University of Lübeck, Germany Abstract. A powerful new complement to traditional synchronous teaching is emerging: intelligent tutoring systems. The narrative: A learner interacts with a digital agent. The agent reviews, selects and proposes individually tailored educational resources and processes – i.e. a meaningful succession of instructions, tests or groupwork. The aim is to make personal tutored learning the new norm in higher education – especially in groups with heterogeneous educational backgrounds. The challenge: Today, there are no suitable data that allow computer- agents to learn how to take reasonable decisions. Available educational resources cannot be addressed by a computer logic because up to now they have not been tagged with machine-readable information at all or these have not been provided uniformly. And what’s worse: there are no agreed conceptual and structured models of what we understand by „learning“, how this model-to-be could be implemented in a computer algorithm and what those explicit decisions are that a tutoring system could take. So, a prerequisite for any future digital agent is to have a structured, computer-accessible model of “knowledge”. This model is required to qualify and quantify individual learning, to allow the association of resources as learning objects and to provide a base to operationalize learning for AI-based agents. We will suggest a conceptual model of “knowledge” based on a variant of Bloom’s taxonomy, transfer this concept of cognitive learning objectives into an ontology and describe an implementation into a web-based database application. The approach has been employed to model the basics of abstract knowledge in engineering mechanics at university-level. This paper addresses interdisciplinary aspects ranging from a teaching methodology, the taxonomy of knowledge in cognitive science, over a database-application for ontologies to an implementation of this model in a Grails service. We aim to deliver this web-based ontology, its user-interfaces and APIs into a research network that qualifies AI-based agents for competence-based tutoring. Keywords. Competence-Based Learning, Knowledge Concept, Ontology, Web- Service, Taxonomy of Knowledge, Web-Application 1. Introduction Digital learning is seen as a new and promising approach in creating a more effective and efficient teaching environment, in delivering higher returns on education. One line of development has been to provide structural elements like educational resources and courses online – many of them being freely accessible or openly licensed as in HOOU [17] – or to provide environments for testing domain-specific knowledge – as in MINTfit [22]. 1 Corresponding Author . Information Modelling and Knowledge Bases XXXIII M. Tropmann-Frick et al. (Eds.) © 2021 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/FAIA210474 1