AI Literacy: An annotated OER bibliography

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.