critical ai handbook

The Critical AI Handbook is Human Restoration Project's guide to using AI in the classroom without losing what makes teaching human. Co-authored with Trevor Aleo, it treats AI as a tool rather than a replacement for teachers, and asks students to look closely at how these systems work, who they leave out, and what they cost. Grab the printable PDF on the left, or work through the prompts and activities on the right.

We do our best to keep these ideas current, and every interactable carries a review date marking when we last checked it for accuracy.

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Using AI Without Losing Ourselves: A Critical Media Literacy for the 21st Century

We must be proactive in teaching students how and when to use AI while taking a critical lens to how it works and its potential pitfalls. This is the essay the whole handbook grows from, rooted in Paulo Freire's critical pedagogy.

read the essay
1

Foundations

What these tools actually are, what they can't do, and why a critical eye comes first.

2

Multimodal Thinking

Move one idea across modes, genres, languages, and reading levels, and bring more voices to a topic than any single text could.

3

Transformation

Tasks that weren't really possible in a classroom before. These open up creativity and inquiry instead of shortcutting them.

Remix for a new audience

Remix a text for a completely different reader and watch where the model's fluency runs out, since it can fake a fandom's surface but not the references only a real fan would know.

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Going on an adventure

Turn an AI into a storyteller and play a branching adventure that shows where it shines as a creative partner and where its endless agreeableness runs out.

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Read across texts like a researcher

Trace a theme across several books or compare how two eras handle the same idea, the kind of pattern-finding that used to take weeks.

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Research with Elicit

Elicit summarizes research findings like a Scholar-style assistant, and it's the same tool we use to build HRP's research database.

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Talk to a PDF

Upload a dense academic PDF and ask it questions, with each answer tied back to the page it came from.

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Pair writing with sound

Ask for songs that match a poem or essay, then weigh whether the suggestions actually fit, and where the model's ear falls short.

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Visuals that match your writing

Adobe Firefly trains on licensed, compensated work, which makes it a more ethical place to generate images that pair with student writing.

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Discussion: AI and academic research

Where does this information come from, how was the model trained, and why does that matter when you cite it?

4

Ethical Dilemmas

The largest part of the handbook, and the reason we don't just ignore these tools: bias, deepfakes, labor, privacy, and the environment are conversations students need to have out loud.

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Whose great works?

Ask for the ten best books of all time, then examine whose canon the answer reflects, and who's missing from it.

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Whose wars matter?

Ask which U.S. wars matter most, then compare the model's textbook answer against the Philippine-American War it tends to skip.

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Make it hallucinate

Find the riddles, slang, and niche facts that make a confident model invent things, then talk about what that means for medicine and research.

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Spot the deepfake

Set real AI-generated images and face-swaps beside the genuine article and see whether you and your students can tell which is which.

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Deepfakes, consent, and harm

AI-generated explicit imagery is already turning up in schools, which makes a hard conversation about consent worth having before it happens rather than after.

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The environmental cost

A calculator that weighs a class's AI use against a school-bus ride or a hot shower, so the energy question stays concrete instead of abstract.

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When AI detection fails

Detection tools flag real student work as fake, so why do we second-guess students' AI use but take the detectors at face value?

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Cite the machine

If a student used AI, how should they say so in a remix culture that rarely stops to cite anything?

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The AI Incident Database

A searchable, user-submitted wiki of AI gone wrong, with thousands of documented cases to fuel a class discussion.

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What counts as knowing?

A model retrieves a fact in seconds, but the handbook's five E's of sense-making (embedded, embodied, enactive, emotive, extended) argue that isn't the same as learning.