Situate critical AI literacy curricula in AI as it appears in students' social worlds rather than abstract bias examples¶

Strategy · All strategies
Evidence · no claims cited

Description¶

For curriculum designers and policymakers, the paper recommends shifting critical AI literacy "from generalized awareness of sociopolitical issues of AI toward situated engagement with AI consequences for a specific community." Curricula can examine AI as it appears in students' social worlds, including surveillance systems, recommendation platforms, predictive tools, and cultural production technologies, and policy initiatives can incorporate contextual interpretation of AI's role within local educational ecologies.

Design Implications¶

Context¶

Requirements¶

• Curricula must attend to how AI infrastructures are embedded in local infrastructures, labor arrangements, and surveillance regimes

Constraints¶

• In communities disproportionately affected by data extraction or algorithmic harm, critical engagement may involve limitation, refusal, or strategic non-use rather than adoption

Target Learners¶

• Youth in communities with varied exposures to AI infrastructures

Target Learning Goals¶

• Situated critical AI literacy grounded in local consequences

Affordances¶

• Three Commitments Community Based Ai Learning

Related Strategies¶

• Structure opportunities for students to evaluate AI outputs using locally grounded criteria of relevance, harm, and usefulness

Examples¶

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Key Sources¶

• Santiago Ojeda-Ramirez, Symone Gyles and Kylie Peppler. 2026. Community-Based AI Learning: Redistributing Artificial Intelligence's Epistemic Authority in Education. In Proceedings of 2026 Conference for Research on Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT 2026). https://doi.org/10.1145/3796496.3811760