Integrate AI literacy across grades and subject-area learning rather than confining it to electives¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The report recommends that educators 'integrate AI literacy within the topics they already teach, such as math and ELA.' Because library media and computer science courses are typically electives not mandated for all students, and computer science courses 'disproportionately enroll white males,' integrating AI literacy into core subjects promotes AI literacy for all students, particularly females and students of color. Cross-disciplinary integration also lets AI literacy become 'a value-add to disciplinary learning and not an add-on to an already overscheduled school day.'

Design Implications¶

Context¶

Requirements¶

• Identify complementary integration points within existing disciplinary topics

Constraints¶

• Library media and computer science courses are typically elective courses not mandated for all students

Target Learners¶

• K–12 students across subject areas
• core subject teachers

Target Learning Goals¶

• cross-disciplinary application of AI literacy practices
• equitable access to AI literacy for all students

Affordances¶

• Six Ai Literacy Practices

Related Strategies¶

• Integrate computational thinking into existing disciplinary teaching across grade bands rather than adding it on
• Integrate data and analysis into existing K-2 teaching practices through classroom graphs such as birthdays and word walls.
• Integrate metacognitive strategy instruction across content areas and across before, during, and after phases of teaching
• Integrate AI assistants with existing curricula and teaching practices rather than replacing instruction
• Integrate computational thinking across K-12 subject areas rather than treating it as computer-science-only
• Select familiar contextual topics and age-appropriate materials when integrating AI into subject courses
• Use a modular lesson structure to keep integrated AI curricula scalable under time constraints
• Integrate AI literacy into disciplinary core subjects rather than adding a new AI subject
• Embed computational thinking across existing K-12 subject areas using everyday and data-based activities

Examples¶

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Key Sources¶

• Mills, K., Ruiz, P., Lee, K., Coenraad, M., Fusco, J., Roschelle, J., & Weisgrau, J. (2024). AI Literacy: A Framework to Understand, Evaluate, and Use Emerging Technology. Digital Promise. https://doi.org/10.51388/20.500.12265/218