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Transferring Domain Knowledge with (X)AI-Based Learning Systems

Title data

Spitzer, Philipp ; Kühl, Niklas ; Goutier, Marc ; Kaschura, Manuel ; Satzger, Gerhard:
Transferring Domain Knowledge with (X)AI-Based Learning Systems.
2024
Event: 32nd European Conference on Information Systems (ECIS) , 13-19 June 2024 , Paphos, Cyprus.
(Conference item: Conference , Paper )

Abstract in another language

In numerous high-stakes domains, training novices via conventional learning systems does not suffice. To impart tacit knowledge, experts' hands-on guidance is imperative. However, training novices by experts is costly and time-consuming, increasing the need for alternatives. Explainable artificial intelligence (XAI) has conventionally been used to make black-box artificial intelligence systems interpretable. In this work, we utilize XAI as an alternative: An (X)AI system is trained on experts‘ past decisions and is then employed to teach novices by providing examples coupled with explanations. In a study with 249 participants, we measure the effectiveness of such an approach for a classification task. We show that (X)AI-based learning systems are able to induce learning in novices and that their cognitive styles moderate learning. Thus, we take the first steps to reveal the impact of XAI on human learning and point AI developers to future options to tailor the design of (X)AI-based learning systems.

Further data

Item Type: Conference item (Paper)
Refereed: Yes
Keywords: Artificial Intelligence; Explainable AI; Human-Computer Interaction; Cognitive Style; Learning Systems; Mammography
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Information Systems Management > Chair Information Systems Management - Univ.-Prof. Dr.-Ing. Niklas Kühl
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
600 Technology, medicine, applied sciences > 610 Medicine and health
Date Deposited: 27 Mar 2024 06:47
Last Modified: 27 Mar 2024 06:47
URI: https://eref.uni-bayreuth.de/id/eprint/89081