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

Strategy · All strategies
Evidence · no claims cited

Description¶

For computing educators, the paper recommends creating "structured opportunities for students to evaluate AI outputs using locally grounded criteria of relevance, harm, and usefulness, positioning AI as one interpretive resource within collective sensemaking." This introduces practices of epistemic calibration in which learners compare AI-generated claims with community knowledge, lived experience, and shared values, and can coexist with technical instruction modularly.

Design Implications¶

Context¶

Requirements¶

• Educators must connect AI evaluation to community members, community sites, and practitioners whose expertise is grounded in lived local experience

Constraints¶

• The approach does not require abandoning technical rigor or dismantling standards-aligned curricula; it can function modularly

Target Learners¶

• K-12 and postsecondary computing students

Target Learning Goals¶

• Epistemic calibration of AI claims against community knowledge and shared values

Related Strategies¶

• Design PjBL projects so students must evaluate, critique, and improve AI-generated outputs rather than prohibiting AI use
• Districts should build community-shared visions, evaluation infrastructure, and policies for procuring and using AI edtech
• Situate critical AI literacy curricula in AI as it appears in students' social worlds rather than abstract bias examples

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