Select familiar contextual topics and age-appropriate materials when integrating AI into subject courses¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article's practice implications hold that contextual knowledge matters in integrated AI curricula: "it appears advantageous to select contextual topics in which students are competent and confident (e.g., human brain knowledge) rather than unfamiliar and challenging ones (e.g., colorectal cancer detection)". The instructor activated prior biology knowledge before introducing AI concepts, used hands-on tools such as Teachable Machine, and compared AI and biology concepts side by side. Academic research papers should be converted into accessible formats such as news articles or interactive simulations.

Design Implications¶

Context¶

Requirements¶

• Instructor time to revisit existing subject knowledge before introducing AI concepts; access to age-appropriate AI tools and materials

Constraints¶

• Academic research papers "often pose significant comprehension challenges for high-school students"; the study was conducted in one advanced-level biology course and may not represent other courses

Target Learners¶

• secondary school students in subject-area courses

Target Learning Goals¶

• AI literacy concepts embedded in disciplinary contexts

Related Strategies¶

• Integrate AI literacy across grades and subject-area learning rather than confining it to electives
• Integrate computational thinking into existing disciplinary teaching across grade bands rather than adding it on
• Use a modular lesson structure to keep integrated AI curricula scalable under time constraints

Examples¶

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Key Sources¶

• Shenghua Zha, Marcy Maulucci Bragdon, Na Gong, Jinhui Wang, Silas Leavesley, Rachel Eaton, Erin Bosarge. (2025). A Case Study of Integrating AI Literacy Education in a Biology Class. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-025-00476-8