To cater to the increasingly diverse student bodies, higher education has to personalize education. In times of stagnant educational budgets and staffing problems, this can only be achieved via adaptive, interactive learning support services. In this paper we show how these can be generated by modeling the domain, the learner competencies, and the rhetoric and didactic relations among learning objects, re-using existing technologies and systems of mathematical knowledge management.

Machine learning based image classification algorithms, such as deep neural network approaches, will be increasingly employed in critical settings such as quality control in industry, where transparency and comprehensibility of decisions are crucial. Therefore, we aim to extend the defect detection task towards an interactive human-in-the-loop approach that allows us to integrate rich background knowledge and the inference of complex relationships going beyond traditional purely data-driven approaches. We propose an approach for an interactive support system for classifications in an industrial quality control setting that combines the advantages of both (explainable) knowledge-driven and data-driven machine learning methods, in particular inductive logic programming and convolutional neural networks, with human expertise and control. The resulting system can assist domain experts with decisions, provide transparent explanations for results, and integrate feedback from users; thus reducing workload for humans while both respecting their expertise and without removing their agency or accountability.