Teaching AI to K-12 Learners: Lessons, Issues, and Guidance

The authors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected VoR was published on May 29, 2024. For reference purposes the VoR may still be accessed via the Supplemental Material section on this page.

There is growing recognition of the need to teach artificial intelli- gence (AI) and machine learning (ML) at the school level. This push acknowledges the meteoric growth in the range and diversity of ap- plications of ML in all industries and everyday consumer products, with Large Language Models (LLMs) being only the latest and most compelling example yet. Efforts to bring AI, especially ML educa- tion to school learners are being propelled by substantial industry interest, research efforts, as well as technological developments that make sophisticated ML tools readily available to learners of all ages. These early efforts span a variety of learning goals captured by the AI4K12 "big ideas" framework and employ a plurality of pedagogies.This paper provides a sense for the current state of the field, shares lessons learned from early K-12 AI education as well as CS education efforts that can be leveraged, highlights issues that must be addressed in designing for teaching AI in K-12, and provides guidance for future K-12 AI education efforts and tackle what to many feels like "the next new thing".