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

Abstract

Due to changing demographics, limited availability of experts, and frequent job transitions, retaining and sharing knowledge within organizations 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 organizations. 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 learning 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 exploratory study. These results shed light on how XAI can facilitate learning, taking first steps towards understanding the potential of XAI in learning systems.

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