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Description

"Scalable, discipline-agnostic AI literacy instruction can be implemented incrementally without requiring full course redesign in higher education, supporting both technical understanding and critical engagement with the social and ethical dimensions of AI: The curated list of open educational resources (OER) on this poster enable a flexible, modular approach to teaching foundational AI literacy. The annotated list includes self-paced, hands-on projects with complementary educator-guided activities and discussion to support conceptual understanding of machine learning, training data, and algorithmic bias, drawing on OER such as Code.org’s AI curriculum, MIT RAISE’s Day of AI, and the multi-lingual Elements of AI course. Many resources listed here also support metacognitive development (Flavell, 1979; Tanner, 2012), and self-regulated learning (Zimmerman, 2002; Theobald, 2021), by prompting students to monitor their understanding, evaluate AI outputs, recognize bias, and make informed decisions about when and how to trust AI systems. Students engage with interactive tools and reflect on AI system behavior, bias, capabilities and limitations through low-stakes assignments individually, in pairs, or whole class. "

Publisher Location

Las Vegas (Nev.)

Publication Date

4-30-2026

Publisher

University of Nevada, Las Vegas

Language

English

Keywords

AI literacy; ML literacy; metacognition; self-regulated learning; small teaching approach; higher education; algorithmic bias; social impacts of AI

Disciplines

Artificial Intelligence and Robotics | Computer Sciences | Other Computer Sciences

File Format

PDF

File Size

300 KB

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

AI Literacy: An annotated OER bibliography


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