An Experiential Approach to AI Literacy
arXiv:2603.29238v1 Announce Type: new Abstract: Despite AI tools becoming more prevalent and applicable to a variety of workplaces, workers consistently report uncertainty about where AI applies, what problems it can help solve, and how it fits into real workflows. In other words, there is a gap between `knowing' and `doing' when it comes to AI literacy. We propose an experiential form of AI literacy which integrates participant's daily experiences into the learning experience by brainstorming grounded AI use cases through storytelling. We introduce a novel pedagogical approach that helps individuals move away from abstract notions of AI towards practical knowledge of how AI would (or would not) work in different workflows, contexts, and situations. Through this approach, we anticipate two
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Abstract:Despite AI tools becoming more prevalent and applicable to a variety of workplaces, workers consistently report uncertainty about where AI applies, what problems it can help solve, and how it fits into real workflows. In other words, there is a gap between
knowing' anddoing' when it comes to AI literacy. We propose an experiential form of AI literacy which integrates participant's daily experiences into the learning experience by brainstorming grounded AI use cases through storytelling. We introduce a novel pedagogical approach that helps individuals move away from abstract notions of AI towards practical knowledge of how AI would (or would not) work in different workflows, contexts, and situations. Through this approach, we anticipate two major outcomes: (1) enhanced AI literacy for stakeholders within a variety of work sectors and (2) concrete AI use cases developed through participatory design that are grounded in AI literacy and participant's expertise.
Comments: Paper accepted at CHI 2026 Workshop on Data Literacy. For more details, see this https URL
Subjects:
Human-Computer Interaction (cs.HC)
Cite as: arXiv:2603.29238 [cs.HC]
(or arXiv:2603.29238v1 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2603.29238
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Aakanksha Khandwaha [view email] [v1] Tue, 31 Mar 2026 04:08:18 UTC (583 KB)
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