There is growing recognition of the need to teach artificial intelligence (AI) and machine learning (ML) at the school level. This push acknowledges the meteoric growth in the range and diversity of applications 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 education to school learners are being propelled by substantial industry interest, efforts such as AI4K12, 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 cur- rent 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 on how to purposefully address the pertinent, topical question of how to teach AI in schools and tackle what to many feels like “the next new thing”.

Shuchi Grover (@shuchig) is a senior research scientist at Looking Glass Ventures and a visiting scholar at Stanford University. Her research is focused on teaching and learning of computer science, computational thinking, and programming in schools. She has been working with children and programming since 2001, first in informal afterschool settings, and recently, in classrooms. Her current research encompasses the design of curricula and assessments for all levels of preK-12 CS education, as well as the integration of computing and coding in STEM and other subjects. She has led, and continues to lead, several large research projects (often in collaboration with universities and research organizations) with grants from the US National Science Foundation and other federal agencies. She also consults globally on projects related to K-12 CS, programming, and computational thinking education.

Over the past decade, she has served on the National K-12 Computer Science Framework team, taskforces of the Computer Science Teachers’ Association, the ACM Education Advisory Committee (2018-present), and the editorial board of the ACM Transactions on Computing Education (2015-present). Shuchi’s educational journey includes undergraduate and graduate degrees in computer science, an Ed.M. in Technology in Education from Harvard University, and a Ph.D. in Learning Sciences and Technology Design from Stanford University.

Shuchi edited and co-authored ‘Computer Science in K-12: An A-Z Handbook on Teaching Programming’ that was published in June 2020.