Measuring AI Literacy of Future Knowledge Workers : A Mediated Model of AI Experience and AI Knowledge

Title data

Hönigsberg, Sarah ; Mallek, Sabrine ; Watkowski, Laura Marie-Luise ; Weritz, Pauline:
Measuring AI Literacy of Future Knowledge Workers : A Mediated Model of AI Experience and AI Knowledge.
In: Proceedings of the 20th International Conference on Wirtschaftsinformatik (WI). - Münster, Germany , 2025

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Abstract in another language

AI Literacy is crucial for knowledge workers, yet existing models fail to capture theoretical and practical evolvement and its predictors. This study extends the AI Literacy framework by Pinski and Benlian (2023), conceptualizing AI Knowledge as a second-order construct and using this as a mediator linking AI Experience and AI Literacy. Using Experiential Learning and Information Processing Theory, we examine AI Experience (Usage and Design) and its impact on AI Literacy via PLS-SEM. A survey of 352 master's students at a French business school reveals that AI Knowledge mediates the relationship between AI Experience and AI Literacy, challenging prior models. We contribute to IS by developing a new mediation model, extending the existing AI Literacy framework, and testing this in a new context. These results highlight the practical importance of hands-on AI Experience and AI Knowledge to enhance AI Literacy.

Further data

Item Type:	Article in a book
Refereed:	Yes
Keywords:	knowledge worker; AI literacy; digital intelligence; digital literacy; AI knowledge
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 Business Administration VII - Information Systems Management and Digital Society Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration VII - Information Systems Management and Digital Society > Chair Business Administration VII - Information Systems Management and Digital Society - Univ.-Prof. Dr. Torsten Eymann 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:	10 Oct 2025 08:04
Last Modified:	10 Oct 2025 08:04
URI:	https://eref.uni-bayreuth.de/id/eprint/94842