Designing Adaptive Argumentation Learning Systems Based on Artificial Intelligence

Item Type Journal paper

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.

Authors Wambsganss, Thiemo
Language English
Keywords adaptive_learning, argumentation_learning, argumentation_mining, dialog-based_learning_systems, pedagogical_conversational_agents
Subjects computer science
information management
HSG Classification contribution to scientific community
Refereed Yes
Date April 2021
Publisher ACM CHI Conference on Human Factors in Computing Systems Extended Abstracts
Place of Publication Yokohama, Japan
Publisher DOI
Depositing User Thiemo Wambsganss
Date Deposited 31 Jan 2021 21:12
Last Modified 25 Feb 2021 08:05


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Wambsganss, Thiemo (2021) Designing Adaptive Argumentation Learning Systems Based on Artificial Intelligence.

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