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1. AI Learning Priorities for All K-12 Students

   Twarek, Bryan; Koressel, Jacob; Gardner-McCune, Christina; Touretzky, David (May 2025, Computer Science Teachers Association)

   Rapid changes in artificial intelligence (AI) require changes in how and what is taught about AI to K-12 students. These changes will ensure that students are prepared to be smart consumers and competent creators of AI, as well as informed citizens. To meet this need, CSTA, in partnership with AI4K12, spearheaded the Identifying AI Priorities for All K-12 Students project. The project gathered experts – including teachers, researchers, administrators, and curriculum developers – to articulate priorities for AI education. This report summarizes the result of that effort. The project had four goals: 1. Identify priorities for AI learning across each K-12 grade band. As a result of a collaborative, iterative process, the project articulated five categories for AI learning: Humans and AI, Representation and Reasoning, Machine Learning, Ethical AI System Design and Programming, and Societal Impacts of AI. 2. Suggest updates to the AI4K12 Guidelines. Advances in generative AI necessitate updates to the AI4K12 Guidelines. This is especially true for Big Idea #4: Natural Interaction, since generative AI represents a substantial advance in the ability of AI to interact with humans. Similarly, generative AI raises many ethical questions relevant to Big Idea #5: Societal Impacts. 3. Advance the research agenda for K-12 AI education. Priorities for research in AI education include the importance of supporting teachers, promoting inclusive and student-centered pedagogies, developing appropriate tools, gaining a better understanding of AI’s impact on learning, and ensuring equity in AI education. 4. Share promising practices across the AI and CS education communities. Participants shared their work in AI education. While the practices described varied, there were some common themes. Concerns about ethics and responsible AI were foregrounded, and hands-on learning activities were featured prominently. Meeting the needs of all children was a key concern, with approaches and tools that are widely accessible as well as engaging for all students. As a result of these common themes, we offer related recommendations for AI curriculum and instruction. The report also includes an exploration of the tensions and challenges that emerged from the project, such as the difficulty of categorizing, organizing, and prioritizing learning content. Preparing students to succeed personally and professionally in a world powered by computing will require rigorous, high-quality, and equitable learning opportunities in AI education. This project sought to determine priorities for AI education for all students to learn as part of a robust foundation in computer science, as well as options for more comprehensive study of AI. Within and across these priorities, two themes stand out. First, all students need to explore the personal, societal, and environmental impacts – both positive and negative – of AI. Second, students need to develop a broad conceptual understanding of how AI works: a frequent refrain from the project’s participants was that students need to understand that “AI isn’t magic.” While implementing high quality AI education, at scale, for all students will be challenging, the work already undertaken by convening participants demonstrates that there are elements of a foundation in place, one that can be built upon to ensure that all students are prepared to flourish in a world powered by computing. 

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2. Reimagining Essential Computing Content for High School Students

   Smith, Julie M; McGill, Monica M; Koressel, Jacob; Twarek, Bryan (June 2024, ASEE Global Colloquium on Engineering Education)

   There are several changes anticipated in computer science (CS) education over the next decade, including updated student standards, rapidly changing impacts of artificial intelligence (AI), and an increasing number of school systems requiring a CS class for graduation. In order to prepare for these changes – as well as to address the equity issues that have plagued CS since its inception – we engaged in a project designed to reimagine content and pathways for high school CS education. As a collaborative project, we hosted multiple events for relevant parties (including K-12 educators and administrators, higher education faculty, industry professionals, state and district CS supervisors, and CS education researchers). These events were designed to collaboratively seek input for the creation of a series of reports recommending what a CS course that satisfies a high school graduation requirement should include, how that course should align with Advanced Placement (AP) and post-secondary CS instruction, and what pathways should exist for students after that introductory high school course. The portion of the project highlighted in this article contains an analysis of data collected from focus groups (n=21), interviews (n=10), and an in-person convening of participants from K-12, post-secondary, industry, and administrative roles (n=35). The data is centered on determining what CS content is essential for all high school students. Participants considered knowledge, skills, and dispositions across a range of CS and CS-adjacent topics and, through a variety of activities, described what new content should be taught when viewing through the lens of teaching CS to high school students in the year 2030 and what content should be prioritized. Our analysis sought to delineate and synthesize their sentiments. Six major priorities emerged from our analysis: societal impacts and ethical issues, algorithmic thinking, data and analysis, inclusive computing culture, AI, and career knowledge. The significance of our findings is that they present a broad overview of what a variety of relevant parties consider to be the most important CS content for high school students; this information is important for educators, administrators, and those who develop curriculum, standards, and/or teaching tools. 

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3. AI in Computer Science Education: Tool, Subdomain, and Wildcard

   Smith, Julie M (May 2024, Proceedings of the International Convention MIPRO)

   The future of AI will be determined in part by how its developers are educated. Thus, how computer science (CS) education incorporates instruction in various aspects of AI will have a substantial impact on AI's evolution. Understanding how and what CS educators think about AI education is, therefore, an important piece of the landscape in anticipating -- and shaping -- the future of AI. However, little is known about how educators perceive the role of AI education in CS education, and there is no consensus yet regarding what AI topics should be taught to all students. This paper helps to fill that gap by presenting a qualitative analysis of data collected from high school CS instructors, higher education CS faculty, and those working in the tech industry as they reflected on their priorities for high school CS instruction and on anticipated changes in high school, college, and workplace CS. We conclude with recommendations for the CS education research community around AI in K-12, particularly at the high school level. 

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4. Artificial Intelligence Workshop: Introducing AI Concepts Using Neural Style Transfer

   https://doi.org/10.32473/FLAIRS.38.1.138950  

   Tang, Anh; Doloc-Mihu, Anca (May 2025, The International FLAIRS Conference Proceedings)

   With the exponential growth of Artificial Intelligence (AI) technologies in recent years, it is more important than ever to increase AI literacy for our students at multiple grade levels, including college students and K-12 students. AI is a broad and complex topic that requires a steep learning curve for most students. This research project focuses on determining the best way to introduceAI to students with little to no prior experience in AI within the scope of a one-hour workshop setting, while still making sure they are having fun exploring this new technology. 

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5. Envisioning AI for K-12: What Should Every Child Know about AI?

   https://doi.org/10.1609/aaai.v33i01.33019795  

   Touretzky, David; Gardner-McCune, Christina; Martin, Fred; Seehorn, Deborah (July 2019, Proceedings of the AAAI Conference on Artificial Intelligence)

   The ubiquity of AI in society means the time is ripe to consider what educated 21st century digital citizens should know about this subject. In May 2018, the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) formed a joint working group to develop national guidelines for teaching AI to K-12 students. Inspired by CSTA's national standards for K-12 computing education, the AI for K-12 guidelines will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The AI for K-12 working group is also creating an online resource directory where teachers can find AI- related videos, demos, software, and activity descriptions they can incorporate into their lesson plans. This blue sky talk invites the AI research community to reflect on the big ideas in AI that every K-12 student should know, and how we should communicate with the public about advances in AI and their future impact on society. It is a call to action for more AI researchers to become AI educators, creating resources that help teachers and students understand our work. 

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