Designing Adaptive Argumentation Learning Systems Based on Artificial Intelligence

Argumentation skills are an omnipresent foundation of our daily communication and thinking. However, the learning of argumentation skills is limited due to the lack of individual learning conditions for students. Within this dissertation, I aim to explore the potential of adaptive argumentation skill learning based on Artificial Intelligence (AI) by designing, implementing, and evaluating new technology-enhanced pedagogical concepts to actively support students in developing the ability to argue in a structured, logical, and reflective way. I develop new student-centered pedagogical scenarios with empirically evaluated design principles, linguistic corpora, ML algorithms, and innovative learning tools based on an adaptive writing support system and a pedagogical conversational agent. My results indicate that adaptive learning tools based on ML algorithms and user-centered design patterns help students to develop better argumentation writing skills. Thereby, I contribute to research by bridging the boundaries of argumentation learning and argumentation mining and by examining pedagogical scenarios for adaptive argumentation learning from a user-centered perspective.