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".

The integration of computational modelling in science classrooms provides a unique opportunity to promote key 21st century skills including computational thinking (CT) and collaboration. The open-ended, problem-solving nature of the task requires groups to grapple with the combination of two domains (science and computing) as they collaboratively construct computational models. While this approach has produced significant learning gains for students in both science and CT in K–12 settings, the collaborative learning processes students use, including learner regulation, are not well understood. In this paper, we present a systematic analysis framework that combines natural language processing (NLP) of collaborative dialogue, log file analyses of students’ model-building actions, and final model scores. This analysis is used to better understand students’ regulation of collaborative problem solving (CPS) processes over a series of computational modelling tasks of varying complexity. The results suggest that the computational modelling challenges afford opportunities for students to a) explore resource-intensive processes, such as trial and error, to more systematic processes, such as debugging model errors by leveraging data tools, and b) learn from each other using socially shared regulation (SSR) and productive collaboration. The use of such SSR processes correlated positively with their model-building scores. Our paper aims to advance our understanding of collaborative, computational modelling in K–12 science to better inform classroom applications.

Background and Context: Learners struggle with conceptual understanding of introductory programming concepts such as variables, expressions, and loops. Objective: We examine whether and how designed activities for conceptual exploration support preliminary engagement with and learning of foundational and often hard-to-grasp programming concepts for students in grades 6–8. Method: Drawing on principles from dynamic mathematics, we developed a suite of non-programming digital and unplugged activities embedded in a curriculum before students engage in Scratch block-based programming. We conducted empirical research in three middle school classrooms in diverse urban US schools and examined student performance through mixed qualitative and quantitative methods. Findings: Learning gains were significant and not predicted by grade, gender or prior academic preparation. Free-choice projects of students showed statistically greater (correct) use of key concepts compared to those not in the study. Implications: Our work demonstrates the promise of novel approaches such as interactive non-programming activities for deeper understanding of programming concepts.

Artificial Intelligence (AI) and cybersecurity are becoming increasingly intertwined, with AI and Machine Learning (AI/ML) being leveraged for cybersecurity, and cybersecurity helping address issues caused by AI. The goal in our exploratory curricular initiative is to dovetail the need to teach these two critical, emerging topics in highschool, and create a suite of novel activities, 'AI & Cybersecurity for Teens' (ACT) that introduces AI/ML in the context of cybersecurity and prepares high school teachers to integrate them in their cybersecurity curricula. Additionally, ACT activities are designed such that teachers (and students) build a deeper understanding of how ML works and how the machine actually "learns". Such understanding will aid more meaningful interrogation of critical issues such as AI ethics and bias. ACT introduces core ML topics contextualized in cybersecurity topics through a range of programming activities and pre-programmed games in NetsBlox, an easy-to-use block-based programming environment. We conducted 2 pilot workshops with 12 high school cybersecurity teachers focused on ACT activities. Teachers' feedback was positive and encouraging but also highlighted potential challenges in implementing ACT in the classroom. This paper reports on our approach and activities design, and teachers' experiences and feedback on integrating AI into high school cybersecurity curricula.

Existing approaches to teaching artificial intelligence and machine learning (ML) often focus on the use of pre-trained models or fine-tuning an existing black-box architecture. We believe ML techniques and core ML topics, such as optimization and adversarial examples, can be designed for high school age students given appropriate support. Our curricular approach focuses on teaching ML ideas by enabling students to develop deep intuition about these complex concepts by first making them accessible to novices through interactive tools, pre-programmed games, and carefully designed programming activities. Then, students are able to engage with the concepts via meaningful, hands-on experiences that span the entire ML process from data collection to model optimization and inspection. This paper describes our 'AI & Cybersecurity for Teens' suite of curricular activities aimed at high school students and teachers.