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Presentation . 2025
License: CC BY NC
Data sources: Datacite
ZENODO
Presentation . 2025
License: CC BY NC
Data sources: Datacite
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A Transdisciplinary Course on AI Literacy: From Concept to Reality

Authors: Véron, Anna C.; Weber, Marco E.;

A Transdisciplinary Course on AI Literacy: From Concept to Reality

Abstract

Slides accompanying the conference presentation at the 9th European Conference on Information Literacy (ECIL 2025); Bamberg A Transdisciplinary Course on AI Literacy: From Concept to Reality Anna C. Véron, Marco E. Weber and Gary SeitzUniversity of Zurich, Switzerland Abstract:The emergence of generative artificial intelligence (AI) technologies has rapidly transformed our landscape ofknowledge creation and communication, raising great opportunities, but also significant challenges, particularly forstudents at higher education institutions (Furze, 2024). AI literacy—essentially an extension of information literacy—has come to the fore as a central skill to ensure the effective and ethical engagement with these new tools. In additionto providing a foundational understanding of the functional possibilities and limitations, AI literacy aims at fosteringstudent’s abilities to critically evaluate the provenance of individual tools and models, as well as to consider the broadersocietal, environmental, and ethical implications of their use (UNESCO, 2021). To achieve this comprehensiveunderstanding, perspectives from diverse disciplines must converge, making interdisciplinary collaboration essentialfor successful AI literacy training—not only among lecturers but also among students, who bring their own disciplinarybackgrounds and perspectives into the learning process.This talk introduces the design and implementation of the course “ChatGPT and Beyond: Interdisciplinary Approachesto AI Literacy” developed by the University Library Zurich and the School of Transdisciplinary Studies at theUniversity of Zurich, Switzerland. The course aligns with the academic library’s mission to promote informationliteracy and aims to equip Bachelor’s and Master’s students from various disciplines with the skills needed toresponsibly navigate and innovate within the rapidly evolving landscape of generative AI.The seminar-style course features eleven lecturers from a range of disciplines including computational linguistics,information science, health science, art, and law. We combined flipped-classroom elements and practical workshopson generative AI tools, alongside discussions addressing critical issues such as societal implications, environmentalconcerns, stereotyped or biased content, and the power dynamics embedded in AI systems.As a preliminary outcome of the course, the student’s assessment portfolios demonstrate that participants develop anuanced understanding of AI, becoming equipped to critically assess and apply generative AI tools in ethical andeffective ways.Finally, we highlight the vital role of academic libraries in promoting both information and AI literacy. Libraries actas a melting pot for interdisciplinary collaboration, providing spaces where diverse perspectives can come together totackle complex challenges. As key facilitators of knowledge and literacy, academic libraries are uniquely positionedto lead the way in fostering responsible engagement with generative AI technologies.ReferencesFurze, L. (2024). Practical AI Strategies: Engaging with Generative AI in Education. Melbourne, Australia: Amba Press.UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. Paris. Retrieved 17 August 2025 fromhttps://unesdoc.unesco.org/ark:/48223/pf0000381137

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Keywords

higher education, AI literacy, transdisciplinary education, Information Literacy, information literacy, critical thinking

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green