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

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dc.contributor.authorSpitzer, Philipp
dc.contributor.authorGoutier, Marc
dc.contributor.authorKühl, Niklas
dc.contributor.authorSatzger, Gerhard
dc.date.accessioned2024-10-08T15:13:01Z
dc.date.available2024-10-08T15:13:01Z
dc.date.issued2024
dc.description.abstractDue 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.en
dc.identifier.doi10.1145/3670653.3677504
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/44879
dc.language.isoen
dc.pubPlaceNew York, NY, USA
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofProceedings of Mensch und Computer 2024
dc.subjectAI-based learning
dc.subjectArtificial Intelligence
dc.subjectComputer Vision
dc.subjectExplainable AI
dc.subjectHuman-Computer Interaction
dc.title(X)AI as a Teacher: Learning with Explainable Artificial Intelligenceen
dc.typeText/Conference Paper
gi.citation.startPage571–576
gi.conference.locationKarlsruhe, Germany

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Tagungsband MuC 2024