Giving Credit Where It’s Due – Teaching AI Attribution & Copyright
💡 Want more insights on AI & copyright? Paid subscribers get exclusive access to bonus content, including a deep dive into real-world AI copyright cases, classroom discussion prompts, and hands-on activities for educators and librarians.
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NotebookLM Deep Dive:
👋 Welcome
Transparency is more important than ever in our ever-changing world of generative artificial intelligence. As AI tools continue to support students and educators in everything from brainstorming to publishing, it’s critical we help learners clearly communicate how AI was used—and whether their work is original, collaborative, or for commercial use.
This week, we’re spotlighting Attribution 4 AI, a fantastic educator-developed framework that helps students label AI involvement in their projects with clarity and integrity. Whether AI generated content, enhanced a human draft, or supported an idea for profit, this model brings transparency to the forefront.
We’ll also explore why copyright and AI are increasingly linked, and how we as educators can address the ethical and legal tensions emerging from this rapidly evolving space.
🛠️ AI Tool of the Week: Attribution 4 AI
Attribution 4 AI offers a clear, flexible system for labeling how AI has been used in creative and academic work. It’s built around six easy-to-understand categories that distinguish human vs. AI effort—and whether the work is for personal or commercial use.
Here are the key categories:
Made by AI – Entirely generated by AI, with or without a prompt.
Made by AI and Modified by Humans – Created by AI and refined by a person.
Made by AI and Modified by Humans – For Profit – Same as above, but intended to be sold.
Made by Humans and Modified by AI – Human-created content is later enhanced or edited using AI tools.
Made by Humans and Modified by AI – For Profit – Human-created and AI-modified, then sold.
Made by Humans – No AI assistance; entirely human-produced.
Each category includes downloadable icons and suggested uses. This framework helps students practice honesty and self-reflection, while also giving educators a tool for assessing how AI supports learning—without making assumptions about “cheating.”
📚 Lesson Plan: “How Did AI Help?”
Grade Levels: 7–12
Time: One class period + reflection
Objective:
Students will identify the role of AI in their recent work and apply the appropriate Attribution 4 AI label, accompanied by a brief explanation of their process.
Steps:
Discussion – What does it mean to “use AI” in your work? Is that the same as doing the work?
Explore – Visit Attribution 4 AI and review the six labels.
Apply – Students revisit a past assignment and choose a label to describe how AI helped (or didn’t).
Reflect – Students write a short attribution statement and discuss what they learned.
💬 Extension idea: Create an “AI Use Statement” section on research rubrics to help normalize responsible use.
⚖️ AI Ethics Corner
📖 The Atlantic – “AI Is Training on Your Favorite Books—Without Permission”
This powerful piece reveals how AI developers trained models on a massive dataset—Books3—that includes tens of thousands of copyrighted books, scraped without permission from authors or publishers. Many bestselling writers, from Colleen Hoover to Margaret Atwood, were unknowingly included.
🧩 Why it matters in schools:
Students may use AI tools built on stolen content without even realizing it.
This introduces serious questions about plagiarism, intellectual property, and ethical AI use.
It challenges us to rethink how we define authorship in the age of machine learning.
👩🏫 In the classroom:
Use this article to spark debate:
Should students disclose the origins of the tools they use?
What rights do authors have over the use of their writing in AI training?
How do we balance accessibility of AI tools with fair use and copyright law?
📚 AI Reading List – Copyright & AI
These articles offer a deeper look into the legal and ethical issues of generative AI:
AI and Copyright: 3 Key Issues – A primer on how AI intersects with data rights, outputs, and ownership.
Every AI Copyright Lawsuit in the US, Visualized – See who’s suing whom—and why—in this real-time overview of ongoing AI copyright battles.
AI and the Law: What Educators Need to Know – Understand the copyright, privacy, and policy implications of AI in schools.
Copyrighting with Artificial Intelligence – A look into the gray areas of AI authorship and why current laws struggle to keep up.
Navigating AI and Copyright in American Schools – A practical guide for educators navigating the complex intersection of law, learning, and AI.
🧁 Final Thought
Attribution is more than just giving credit—it’s about cultivating ethical habits, encouraging honest reflection, and supporting intellectual integrity. As AI continues to change how we learn and create, it’s up to us to lead students in understanding when and how to use it—and when to say, “This was made by me.”
📬 I’d love to hear how you’re integrating attribution conversations into your classroom or library. Let’s keep the dialogue going
.