(X)AI as a Teacher : Learning with Explainable Artificial Intelligence

Titelangaben

Spitzer, Philipp ; Goutier, Marc ; Kühl, Niklas ; Satzger, Gerhard:
(X)AI as a Teacher : Learning with Explainable Artificial Intelligence.
In: Maedche, Alexander ; Beigl, Michael ; Gerling, Kathrin ; Mayer, Sven (Hrsg.): Proceedings of Mensch und Computer 2024. - Karlsruhe, Germany : Association for Computing Machinery , 2024 . - S. 571-576
ISBN 979-8-4007-0998-2
DOI: https://doi.org/10.1145/3670653.3677504

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Abstract

Due to changing demographics, limited availability of experts, and frequent job transitions, retaining and sharing knowledge within or ganizations is crucial. While many learning systems already address this issue, they typically lack automation and scalability in teaching novices and, thus, hinder the learning processes within organi zations. Recent research emphasizes the capability of explainable artificial intelligence (XAI) to make black-box artificial intelligence systems interpretable for decision-makers. This work explores the potential of using (X)AI-based learning systems for providing learn ing examples and explanations to novices. In an exploratory study, we evaluate novices’ learning performance in a learning setting taking into account their cognitive abilities. Our results show that novices increase their learning performance throughout the ex ploratory study. These results shed light on how XAI can facilitate learning, taking first steps towards understanding the potential of XAI in learning systems.