by Yenda Prado, Pati Ruiz, Judi Fusco, Denise DuVall, Danielle Hinkel, Margaret Perkoff, and Orchid Rocha

A sixth-grade student listens to music and writes about the feelings the song evokes for her during a lesson in English class. — Photo by Allison Shelley for EDUimages

Resource Brief 1: AI Literacy Basics: What Educators Need to Know
Resource Brief 2: Creating Responsible AI Use Guidance: A Practical Guide for Educators
Resource Brief 3: Effective Classroom Practices with AI
Resource Brief 4: Misconceptions About AI: What Educators Should Know

Introduction

This set of four resource briefs, developed in collaboration with the U-GAIN Reading National Leadership Educator Cohort, is designed to provide quick, essential knowledge for navigating the emerging AI landscape.

These briefs offer essential AI literacy knowledge for supporting the ethical adoption of AI in schools, as outlined by the U-GAIN Reading R&D Center. They also serve as a foundational basis for the eventual development of guidance on effective uses of AI-enabled tools to support the reading development of all students in K-12 schools.

Each resource focuses on a critical aspect of AI implementation:

AI Literacy Basics: What Educators Need to Know Defines core knowledge, skills, and dispositions, such as understanding how AI systems generate outputs and the importance of critical evaluation.
Creating Responsible AI Use Guidance: A Practical Guide for Educators Provides a practical guide for developing human-centered policies and clear expectations for educators, students, and families regarding transparency and ethical use.
Effective Classroom Practices with AI Illustrates practical, instructional applications where Generative AI can amplify teaching, support the student writing process, and promote critical thinking and questioning skills.
Misconceptions About AI: What Educators Should Know Addresses common myths to move educators toward informed, confident action in the classroom.

Responsible AI integration begins with a strong, shared foundation of AI literacy and ethical governance. These resources seek to support educators in maintaining instructional integrity and guiding students to become informed, critical, and responsible users of AI in their learning environments.

AI Literacy Basics: What Educators Need to Know

What is AI Literacy?

AI literacy is the knowledge, skills, and dispositions needed to critically understand, evaluate, and responsibly use AI systems in learning environments. For educators, AI literacy is not about becoming a computer scientist, it’s about knowing what AI can do, what it can’t do, how to use AI-enabled tools thoughtfully, and how to guide students to use AI responsibly.

AI literacy includes:

Understanding how to identify when AI is being used,
Identifying when AI should be used and when it should not be used
Understanding how AI systems generate outputs
Writing effective prompts (sometimes called prompt engineering)
Evaluating AI responses critically
Recognizing that AI is not human-like but rather a probabilistic automation system with many limitations
Recognizing the limitations of AI systems including bias and errors
Making informed instructional and ethical decisions about AI implementation to not harm students or the learning process

How AI Works

Much of the discussion today about AI tools is around language models that generate text and are called Generative AI (GenAI). They answer questions, but they do not think and they do not know. These are human abilities. Instead, these systems:

Process massive amounts of text, audio, and visual data
Determine patterns (that may or may not be meaningful)
Predict the most likely response to your question
Generate responses based on probability/statistics, not understanding

GenAI doesn’t reason or verify truth, it answers questions by predicting what is likely to be the next word in a sequence.

Understanding Bias

AI systems are not neutral. Biases can be embedded in the data AI systems are trained on. Bias is a limitation of AI that can lead to skewed or partial information, sometimes resulting in inaccurate assumptions. To address bias, educators should:

Help students critique AI responses to identify bias or missing perspectives.
Evaluate AI outputs to ensure the content is fair and representative of the community.
Explore the access implications of AI implementation to avoid widening opportunity gaps for students.

Writing Effective Prompts

Writing effective prompts requires learning how to ask specific and detailed questions. Teachers already do this in their daily classroom practice. Using AI just makes it more explicit.

A strong prompt usually includes:

Role – “You are a middle school science tutor…”
Task – “Explain photosynthesis…”
Audience – “For a 6th-grade student…”
Constraints – “Use simple language and one example.”
Justification – “Explain why you made this decision…”

Example Prompt: “Explain the causes of the American Revolution for a 5th-grade student using a short paragraph and a bulleted list.” This mirrors good teaching practice and helps learners strengthen clarity, writing, and thinking skills.

A simple classroom framework:

Learners ask a question and develop a detailed prompt based on the question
AI generates a response
Students evaluate the output

Teachers can guide students to:

Check accuracy
Identify missing perspectives
Compare multiple sources, conduct lateral reading checks
Revise or improve the response

This evaluation step supports learners’ critical thinking.

Understanding Errors

AI sometimes produces information that sounds confident but is incorrect. These are called errors (we don’t use the “industry” term, “hallucinations,” because it anthropomorphizes the technology and also makes light of mental illness [Nanna, et al., 2026]).

Important reminders to share with learners:

AI is often wrong.
AI does not fact-check.
Sources must be verified elsewhere and even requesting sources from an AI tool requires verification since sources are often made up and don’t exist.

Teaching students to question AI outputs builds digital literacy.

Takeaway

When educators understand how AI works, they can help students become informed, critical, and responsible users of AI.

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Creating Responsible AI Use Guidance: A Practical Guide for Educators

Why AI Guidance Matters

As AI tools enter classrooms, schools need clear, shared expectations to protect students, support teachers, and ensure responsible use. AI policy shared by state or district leaders can be shared in practical, values-driven, and flexible ways with the school community.

Good AI guidance should answer the question: “How should AI be used to support teaching and learning, without replacing human judgment?”

Developing AI Guidance

Strong AI policies and guidance are built on several key principles:

Human Oversight: AI is used to support decisions, not to make them
Transparency: Students and families understand when and how AI is being used
Access: AI tools are not used in ways that widen opportunity gaps
Privacy & Safety: Student data is protected and secured
Instructional Purpose: AI use is aligned to students’ learning goals
Evidence-Based Procurement: AI tools are approved according to evidence-based district guidelines

Examples of Responsible AI Use Policies:

Common Themes in Effective AI Policies and Guidance

Across districts and schools, effective AI policies and guidance tend to:

Involve the community: learners, families, and various levels of educators
Start small (pilot before full rollout)
Focus on use cases, and understanding data used in tools, not tool lists
Identifies who is responsible for approving AI tools
Emphasize learning over compliance
Include regular review and updates

Guidance and policy documents should be revisited as technology and classroom needs change.

Guidelines by Audience

Effective AI policy is not one size fits all and should clearly describe specific expectations for educators and students.

Sample Educator Guidelines

Share district and state policies on acceptable tools to use
Use AI as a planning, feedback, or differentiation tool, not as a grading replacement
Identify what kind of assignments can easily be fed to an AI
Review all AI-generated content before sharing with students
Model ethical use, attribution, and critical evaluation

Sample Student Guidelines

AI may be used as a support tool, not a shortcut
AI outputs must be checked for accuracy
Students remain responsible for their work and learning

Sample Family Guidelines

Understand appropriateness of AI tools by age group
Explain what tools are used and why
Clarify how student data is protected
Invite questions and ongoing dialogue

Addressing Academic Integrity

Effective AI policies for students shift the conversation to responsible tool use, rather than just cheating, and clarify:

When AI assistance is allowed
What requires student-generated thinking and why
How AI use must be disclosed by both educators and learners

Takeaway

AI guidance works best when it is clear, human-centered, and grounded in instructional values. When educators and students understand the reasoning behind policy, they are better able to engage with AI responsibly and effectively.

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Effective Classroom Practices with AI

What is Generative AI?

Generative AI (GenAI) is a type of artificial intelligence that produces content by learning and predicting patterns from large amounts of data. It can be used to produce text, images, or code. It can also be used to answer questions. GenAI is a type of machine learning that arrives at responses and creates content by predicting sequences and patterns, rather than by reasoning or verifying truth.

GenAI as a Teaching Tool, not a Teaching Replacement

Effective classroom use of GenAI should focus on supporting learning goals, not automating instruction or automating the process of producing a product (for example, an essay or document). When integrated thoughtfully into classroom instruction, GenAI can be used to support writing, inquiry, feedback, and critical thinking.

Teaching Effective Prompting as a Skill

Prompting helps students:

Clarify their thinking
Ask better questions
Revise and refine ideas

Break prompting into steps that students can practice to mirror the research, writing, and inquiry skills students need:

What am I asking?
What details matter?
How can I improve my question?

Supporting the Student Writing Process

GenAI, when used responsibly, could be used to support students’ writing processes across the following stages:

Pre-writing and planning: generating initial ideas, outlining essay structures
- Example: A student uses the prompt like: Generate three potential thesis statements for an essay on climate change’s impact on coastal cities.
Drafting language support: assisting with syntax and sentence structure
- Example: A student pastes a complicated sentence and prompts: Revise this sentence to make the flow clearer and the tone more formal.
Revision and feedback: Offering feedback on clarity, tone, and style
- Example: A student uses the prompt: Identify three areas in this draft where I can strengthen my argument with more specific evidence.

Classroom Uses That Support Learning

Uses of GenAI that support learning can be integrated into classroom practice. Educators can focus their use of GenAI to support instructional purposes, such as generating classroom discussion questions and differentiating explanations. Effective practices include using GenAI to support:

Generating discussion questions
- Example: A teacher uses GenAI to create three open-ended questions about the theme of isolation in The Wild Robot lesson plan.
Differentiated explanations
- Example: A student who is struggling with a history concept uses the prompt: Explain how a bill becomes a law using a simple analogy that a 5th grader can understand.
Practice problems with scaffolding
- Example: An educator asks GenAI: Create a set of five practice problems for calculating the area of a trapezoid, and provide one hint for each problem.

After GenAI outputs are generated, students need to learn how to:

Analyze for accuracy and mistakes
Evaluate bias or missing voices
Create improved versions, with and without technology
Reflect on what they learned

Responsible Classroom Norms

Establish norms such as:

Human judgment always comes first
GenAI outputs are starting points, not final answers
Attribution matters, clearly cite use of GenAI

Takeaway

GenAI is most effective when it is used to amplify good teaching practice as well as student questioning, feedback, reflection, and critical thinking. If you’re not comfortable with AI, and what it can give you and your students, it is best to learn more before using it with your students.

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Misconceptions About AI: What Educators Should Know

Myth 1: AI Thinks Like Humans AI does not understand, reason, or create meaning. It predicts language based on patterns. It has no intent, beliefs, or awareness.

Myth 2: AI Creates Original Ideas AI does not invent new knowledge. It recombines existing information. Creativity still comes from humans; AI can support human creativity but not replace it.

Myth 3: AI Will Replace Teachers AI will not replace teachers, but teachers who understand AI will be better equipped for future classrooms than those who don’t. Human relationships, judgment, and care are irreplaceable.

Myth 4: AI Is Always Accurate AI sometimes produces information that sounds confident but is incorrect. This makes critical evaluation of AI outputs essential.

Myth 5: Using AI Always Lowers Rigor Poorly used AI lowers rigor. Well-designed AI use can support rigor by requiring students to analyze, revise, justify, and reflect.

Takeaway

Understanding what AI is, and isn’t, helps educators move towards informed action. Concerns about automation, creativity, and relevance are valid. AI literacy empowers educators to maintain agency, protect instructional values, and guide students responsibly.

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Recommended Citation

Prado, Y.; Ruiz, P.; Fusco, J.; DuVall, D.; Hinkel, D.; Perkoff, M.; Rocha, O. (2026). AI in Education: A Set of Four Essential Resource Briefs for Educators. Digital Promise. https://doi.org/10.51388/20.500.12265/291

Funding/Acknowledgments:

This project is supported by the Institute of Education Sciences, U.S. Department of Education through Grant R305C240040 to Digital Promise. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.

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