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

Title data

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 (ed.): Proceedings of Mensch und Computer 2024. - Karlsruhe, Germany : Association for Computing Machinery , 2024 . - pp. 571-576
ISBN 979-8-4007-0998-2
DOI: https://doi.org/10.1145/3670653.3677504

Official URL:

Abstract in another language

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.

Further data

Item Type:	Article in a book
Refereed:	Yes
Keywords:	Computer Vision; Artificial Intelligence; Explainable AI; Human-Computer Interaction; AI-based learning
Institutions of the University:	Faculties > Faculty of Law, Business and Economics > Department of Business Administration Research Institutions Research Institutions > Affiliated Institutes Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT Research Institutions > Affiliated Institutes > FIM Research Center for Information Management
Result of work at the UBT:	Yes
DDC Subjects:	000 Computer Science, information, general works > 004 Computer science 300 Social sciences > 330 Economics
Date Deposited:	06 Sep 2024 08:13
Last Modified:	06 Sep 2024 08:13
URI:	https://eref.uni-bayreuth.de/id/eprint/90354