An Experiential Approach to AI Literacy

Why Read This from an HCI Perspective

This article is significant from an HCI perspective because it aims to turn AI literacy into lived experience within real work contexts, rather than simply delivering knowledge. In particular, it focuses less on “what you know” and more on “when and how to decide to use—or not use—AI,” revealing the gap between instructional design and product adoption. For practitioners, it provides a starting point for participatory design; for researchers, it offers a strong example of asking what conditions enable AI understanding to translate into behavior change.

CIT's Commentary

One interesting aspect is that the design doesn’t stop AI literacy inside the workshop. Instead, it uses participants’ everyday work as raw material and lets it “mature” over several days. This approach connects abstract AI concepts to real workflow decision-making. From a product perspective, however, it also highlights that “understanding” does not automatically translate into “deployability.” Some tasks may increase intervention costs and responsibility burdens rather than providing help. Therefore, you should design failure modes and human intervention pathways alongside (and before) identifying use cases. Moreover, this approach can be extended beyond training people to use AI well, into research that formulates questions for evaluating AI.

Questions to Consider While Reading

• Q.In real work contexts, how can the AI use cases that participants come up with be categorized as ‘appropriate’ versus ‘overly optimistic’—based on what criteria?
• Q.How can we measure whether storytelling-based experiential learning leads not to one-off shifts in awareness, but to actual adoption decisions and behavior change?
• Q.To make this approach effective even for participants whose roles and industries differ, how much of a shared framework should be maintained, and from where should context-specific customization begin?