Pre-university AI Instruction
Stephen K. Reed · 2025
The earlier emphasis on teaching programming and computational thinking at the pre-university level was followed by a second wave as artificial intelligence became increasingly integrated into technology. Its growing presence in people’s lives created a need for an earlier introduction to AI. Many countries recognized the importance of instruction on artificial intelligence at the pre-university level by formulating national policies to guide its development. Desired competencies include the ability to distinguish between technologies that use and do not use AI; identify a variety of technologies that do use AI; and critically analyze differences in intelligence among humans, animals, and machines. An example curriculum consists of an introduction to AI, traditional approaches to AI, face detection, speech recognition, machine translation, image classification, text classification, and self-driving cars. Courses on machine learning focus on how algorithms learn, the role of training data, applications, and its impact on society. Instruction should combine the ideas of computational thinking, data science, and multi-disciplinary knowledge to encourage AI literacy. Success in implementing these courses requires effective professional development.