Developing AI Policy in K–12 Schools, Step 2: Learning

Explore strategies for learning about artificial intelligence and AI policy in K–12 schools.

Grades K-12 22 min Resource by:
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In the first article of this series, we explored how to begin the process of writing an AI policy for your school or district. Step 1 involved gaining a basic understanding of the current state of AI in schools and society, framing the conversation with some common beliefs, and setting up an inclusive committee.

Step 2 is essentially a continuation of the learning process undertaken in Step 1. You can’t write effective policy unless you understand what you’re writing about, and since you can only write about a topic as well as you comprehend it, it’s critical to form a sound foundation of understanding. This will center the work on fact and reality, and it will help your committee get past the sensationalized headlines that so often dominate the news.

Focus on Generative AI

Most of the new learning that you’ll likely need will focus on the newest and most disruptive type of AI: generative AI.

Generative AI is artificial intelligence that generates, or creates, new content that didn’t exist before. It accomplishes this by referencing patterns embedded in billions of human examples, mostly scraped from the internet. When a user enters a prompt or submits a question, the AI creates its response by predicting the most likely sequence of words and sentences based on its learned examples.

While it can seem quite magical to see original content appearing in seconds, the process isn’t magic at all. It is simply the result of computer algorithms that have been trained to respond in a human-like fashion, and these responses are dependent upon the quality of material used to train the AI. By connecting ideas from an immense database of content, generative AI tools can return insightful ideas as well as an efficient synthesis of known ideas. However, AI responses can be filled with biases, inaccuracies, and hallucinations (or made-up answers). Again, it’s important to remember that generative AI responses are reliant upon the quality of the AI algorithm written into the program, the effectiveness of the platform’s training, and the quality of the original content.

Use It

Perhaps the best first way to begin learning more about generative AI is by simply using it.

Start by choosing one of the leading generative AI chatbots. The four leading companies all offer versions that you can try for free:

Premium versions of these chatbots do offer improved functionality and capabilities over the free versions, and it’s helpful if at least a few people on your committee can experience an upgraded account. That said, the free version can definitely get you started and meet the needs of most committee members.

Once you’ve set up your account, try it out. Ideally, you should use it to assist with authentic tasks in your life. For example, you might ask AI to help you draft an email or brainstorm lesson plan ideas. If you have a draft of something you’ve written, paste it into an AI chatbot and ask for ways to improve aspects of the writing, like grammar, fluency, or organization. With any task, be aware that the more specific your request, the better the response.

And don’t stop with one experience. Use it often. Use it for a variety of purposes. Experiment. Try different ways to word your request. Ask follow-up questions. Do a little research on prompt engineering and try out some of the strategies that you learn. Try different forms of generative AI, including both text and image generators. The more you work with any tool, the more you will learn, and those writing policy should deeply understand the tool about which they are writing that policy.

Take a Course

There are new AI courses being released all the time, and many are free. Once you’ve played around with one of the generative AI tools for a bit of time, these courses can help you dive deeper and discover some of the nuances about AI that you might have overlooked. Courses are also helpful if you like a more sequential, guided learning experience.

Here are some online courses, listed in alphabetical order, for you to consider. Some are structured as formal learning experiences, while others are a collection of informative resources that you can review. Some are extensive, and others are simply meant to provide a quick overview. For broader exposure, you might consider dividing the following options up among your group members and then sharing back what you learn:


Hands-on experience and formal courses can provide practical insights into how generative AI works, but that learning will not necessarily provide you with an education context. To gain additional understanding and perspective regarding how AI applies to education, it’s helpful to explore education-specific resources. These materials will often provide valuable context and considerations that educators should take into account when bringing generative AI to schools and classrooms, and this applies when writing policy. The following is an annotated list of relevant resources to consider reviewing, with content organized in alphabetical order.

AI Guidance for Schools Toolkit (TeachAI)

This collection of resources has been specifically created to help school leaders enter into the age of AI. The toolkit includes a Google Drive folder of resources and a Foundational Policy Ideas for AI in Education presentation.

AI Toolkit (InnovateOhio)

This toolkit includes seven parts, beginning with Part 1: Policy Development for AI. The executive summary of this multistep toolkit states, “This toolkit will equip stakeholders in Ohio’s schools (district superintendents, school principals, educators, parents, and more) with the resources to advance AI literacy among their students.”

Artificial Intelligence (AI) in K-12 (CoSN in partnership with Microsoft)

This report is intended to help guide educators by providing context, potentially promising applications, and questions and considerations about artificial intelligence in education. Specifically, they write, “Before adopting new AI systems, school and district leaders need to not only consider some of the social and moral components but also three more immediate concerns: privacy, bias, and literacy.”

Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations (U.S. Department of Education’s Office of Educational Technology)

This report is based on research and input from listening sessions. The report has sections for the following topics: Building Ethical, Equitable Policies Together; What is AI?; Learning; Teaching; Formative Assessment; Research and Development; and Recommendations.

Blueprint for an AI Bill of Rights (The White House)

This document from the Biden administration is aimed at preserving American civil rights in the age of artificial intelligence. The document offers five sections: Safe and Effective Systems, Algorithmic Discrimination Protections, Data Privacy, Notice and Explanation, and Human Alternatives, Consideration, and Fallback.

Bringing AI to School: Tips for School Leaders (ISTE)

This document provides a quick and accessible summary of AI for school leaders. An overview of artificial intelligence is provided as well as frequently asked questions that a school leader might ask about AI. A section around strategies for success offers five action steps: Encourage Exploration, Provide Training, Spotlight Success, Host Conversations, and Set the Right Conditions.

Consultancy Protocol for Building AI Capacity in Your School District (EDSAFE AI)

This one-pager provides guidance for school leadership. It outlines a process for assessment and action planning.

EDSAFE Policy Library and Resources (EDSAFE AI)

EDSAFE has collected relevant documents from leading agencies regarding AI implementation and integration in K–12 school systems. Among others, documents include the Kennedy HQ AI Literacy Map, Responsible AI and Tech Justice: A Guide for K-12 Education, How School Districts Are Integrating Generative AI Into Their Policies, the NIST Cybersecurity Framework, and more.

Ethical Guidelines on the Use of Artificial Intelligence and Data in Teaching and Learning for Educators (European Commission)

This report outlines ethical guidelines for educators on the use of AI and data. The European Commission states, “These ethical guidelines on AI and data usage in teaching and learning are designed to help educators understand the potential that the application of AI and data usage can have in education and to raise awareness of the possible risks so that they are able to engage positively, critically and ethically with AI systems and exploit their full potential.”

Ethics of Artificial Intelligence (UNESCO)

Among their list of objectives for this document, UNESCO writes that these recommendations are intended “to provide a universal framework of values, principles and actions to guide States in the formulation of their legislation, policies or other instruments regarding AI, consistent with international law.” This information can be helpful to school leaders as well. They lay out 10 core principles regarding a human rights-centered approach to addressing the ethics of AI.

How to Create a Responsible Use Policy for AI (TCEA)

This article suggests nine steps for integrating AI into a Responsible Use Policy (RUP). Steps range from forming a committee of multiple stakeholders to continuing to review and revise policy as the AI landscape evolves.

K-12 Generative AI Readiness Checklist (Council of the Great City Schools and CoSN)

This helpful and detailed checklist helps guide school leaders through the process of implementing AI in their schools. The checklist covers: Executive Leadership Readiness, Operational Readiness, Data Readiness, Technical Readiness, Security Readiness, and Legal/Risk Management. One of the key strengths of this checklist is that it is designed to be adaptable. Districts can, and should, customize it according to their unique needs and challenges.

Principles for AI in Education (SIIA)

This resource outlines seven principles for the Future of AI in Education. These principles can be used to help frame future policy discussion.

Revealing an AI Literacy Framework for Learners and Educators (Digital Promise)

This framework “emphasizes that understanding and evaluating AI are critical to making informed decisions about if and how to use AI in learning environments.” The framework is broken into three sections: Understanding, Using, and Evaluating AI.

The AI Index Report: Measuring Trends in AI (Stanford University)

This downloadable report aims to provide data and trend information regarding artificial intelligence. They state, “Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI.”

The National Educational Technology Plan (U.S. Department of Education’s Office of Educational Technology)

This document offers national guidance regarding technology integration in U.S. K–12 schools. The insights and frameworks included in this document can help inform and guide local conversations about artificial intelligence policy development.

What Is the EDSAFE AI SAFE Framework? (EDSAFE AI)

This document summarizes the EDSAFE AI Framework intended to help guide schools to the safe adoption of AI technology: Safety, Accountability, Fairness, and Efficacy. A detailed description can be found on the EDSAFE website.

Review Current Policies

When writing policy, it can be very insightful to review existing policy on the same subject. While many states and districts are still in the process of crafting AI policy, there are policies available online that can be reviewed. Districts can also reach out to neighboring districts to inquire about policies created in their geographical areas. The following are a few AI policies that are available online for review:

Share and Network

Since there is a large amount of content available for review, it can be helpful to divide and conquer this research as a team. Once individuals have had a chance to investigate on their own, they can come back together to share with others. Here are a few ways that sharing can be structured:

  • 4–2–1: Begin in groups of four people, with each member taking a turn sharing something they’ve learned. Those four members then choose one key idea from the group to pass along. Two groups combine together into one, with each of those groups sharing their one key takeaway. This new combined group then chooses one of the two ideas to share with the larger group. Through this process, everyone gets to share, and the best ideas reach the larger group.
  • AI Playground: Participants can gather in a common area to experiment with AI tools, with a focus on playing, having fun while learning, and sharing out key takeaways.
  • Demo Slam: Participants take turns presenting a quick AI discovery or tip to the whole group. Oftentimes, a 1- or 2-minute time limit is set to keep things moving. The intention is to share lots of ideas rather than to go into depth on any one topic.
  • Jigsaw: Members are split into groups. Each member in the small group chooses one or more resources to review and become an expert on. After everyone has had time to review their assigned materials, members of the different groups meet in “expert groups.” These members have all chosen the same materials to review and work together to become experts on the common topic. Once these groups have become experts, they generate key talking points that are then shared back with their original groups. In this way, everyone is actively involved, and everyone gets the same information.
  • SWOT: In groups, participants can use the knowledge they’ve gathered to generate a group SWOT analysis. SWOT stands for Strength, Weakness, Opportunity, and Threat. Members brainstorm a list of ideas for each of the four categories. Once completed, groups share with the larger group, possibly compiling ideas into an overarching SWOT. This process facilitates both sharing and a deeper processing of what has been learned.
  • Round-Robin: In this simple strategy, members take turns sharing something they’ve learned with the larger group. Proceed around the room until everyone has shared. You may want to assign a recorder or have each person add their own idea to a shared document.

The next article in this series will focus on Step 3: Writing, where you will find strategies to help you write your AI policy.

AVID Connections

This resource connects with the following components of the AVID College and Career Readiness Framework:

  • Systems
  • Leadership
  • Culture
  • Break Down Barriers
  • Align the Work
  • Advocate for Students
  • Collective Educator Agency

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