An AI Workflow for Differentiating Instruction
Differentiation researcher Carol Ann Tomlinson's long-standing framework holds that a lesson can be adjusted along four levers — content, process, product, or learning environment — without changing what students are actually expected to learn. That framework, more than any single prompt trick, is what should drive an AI-assisted differentiation workflow.
Quick Answer: Build the on-level version of a lesson first, then generate tiered variants by adjusting exactly one of Tomlinson's four levers — content, process, product, or grouping — while holding the learning objective fixed. Verify each tier for rigor and reading level, keep answer keys aligned so whole-class review still works, and layer in any IEP or 504 accommodations separately, since those are legally distinct from general ability tiering.
A one-shot "make this easier" prompt tends to produce a version that's shorter, not more accessible — fewer questions rather than better-scaffolded ones. A workflow that names the specific lever being adjusted avoids that trap, because it forces a decision about what's actually changing instead of leaving the model to guess.
Why Differentiation Needs a Workflow, Not a Single Prompt
A vague request like "differentiate this for struggling readers" hands the model an undefined problem, and it typically responds by simplifying everything at once — vocabulary, question count, and rigor together — rather than adjusting the one variable a student actually needs. A workflow fixes this by making the lever an explicit, separate decision from the content itself.
What "Differentiation" Actually Means
CAST, the nonprofit behind Universal Design for Learning, frames differentiation and UDL as related but distinct: differentiation adjusts a lesson for specific identified needs after the fact, while UDL tries to build flexibility into the original design. An AI workflow can support either approach, but the prompt itself needs to be honest about which one it's doing.
The Risk of Over-Simplifying
A struggling reader in a science class often needs the reading level adjusted, not the science made less rigorous — collapsing both into one vague prompt risks lowering the conceptual bar along with the vocabulary, which is a different (and usually unintended) outcome.
How This Differs From Reteaching the Whole Class
Differentiation and reteaching solve different problems, and an AI workflow should treat them differently too. Reteaching means the whole class missed something and needs the same content presented a second way; differentiation means different students need different paths to the same objective during the same lesson.
A prompt written for reteaching ("explain this concept a second, simpler way for the whole class") and a prompt written for differentiation ("create a support-tier version of this specific activity for a subset of students") aren't interchangeable, even though both involve simplifying something. Naming which one is actually happening keeps the workflow's later steps — especially tier tracking — pointed at the right problem.
The Four Levers of AI-Assisted Differentiation
Every tiered version of a lesson should be traceable to one of four levers, and naming that lever directly in the prompt is what keeps a tier faithful to the original objective. Mixing levers in one request tends to produce a version that's hard to compare against the original.
| Lever | What Changes | What Stays the Same | Example Adjustment |
|---|---|---|---|
| Content | What students engage with | The learning objective | Simpler text at the same topic and objective |
| Process | How students work through it | The end goal | Added scaffolding, worked examples, partner work |
| Product | How students show understanding | What they needed to learn | A diagram instead of a paragraph response |
| Environment/Grouping | Who works with whom, and how | The task itself | Small-group support vs. independent work |
This four-lever structure is the same underlying discipline covered in AI Prompting & Content Workflows for Teachers (2026 Guide) — differentiation just applies it to variations of one lesson rather than to a single new piece of content.
Why Naming the Lever Beats Naming a "Level"
Asking for a "lower-level version" leaves the model to decide which lever to touch, and it often touches several. Asking specifically for "the same content, but with a sentence-starter scaffold added to the process" produces a tier that's actually comparable to the original, item for item.
Using the Grouping Lever Without Extra Content Prep
Grouping is the one lever that doesn't require generating a new version of the material at all — the content, process, and product can all stay identical, and only who works with whom changes. A prompt here isn't for the activity itself but for a facilitation plan: "Suggest a partner-grouping strategy for this fractions activity, pairing a student who has already shown the skill with one still building it."
- Content, process, and product levers each need a distinct generated variant.
- The grouping lever only needs a facilitation suggestion layered onto the existing on-level materials — often the fastest lever to apply on a day with little prep time.
A Six-Step Workflow for Differentiating One Lesson With AI
The workflow runs from a single on-level anchor, through one deliberate lever adjustment per tier, to a verification pass — and skipping the anchor step is the most common reason tiered versions drift apart in rigor. Each step closes a specific gap a rushed differentiation request tends to leave open.
Step 1: Build the On-Level Version First
Generate and finalize the standard version of the lesson or activity before requesting any tiered variant. Every later tier will be compared against this one, so it needs to be settled first.
- Write and review the on-level activity completely, treating it as the anchor every other tier gets measured against.
- Confirm the learning objective is explicit, not just implied by the content, so later prompts can hold it constant.
Step 2: Pick One Lever Per Tier
Decide, before prompting, which single lever a given tier needs — content, process, product, or grouping — based on what a specific group of students actually requires.
Adjusting two levers in one request usually means neither adjustment lands precisely. A prompt that changes reading level and response format at once makes it hard to tell which change did what.
Step 3: Generate Tiers From the Same Base Content
Prompt from the finished on-level version, not from a fresh description of the topic, so the tiered version stays anchored to identical content and objective.
- Paste the on-level version directly into the tiering prompt, rather than re-describing the lesson from memory.
- State explicitly which lever to adjust and how — "simplify sentence length and vocabulary only; keep every question and the objective identical."
Step 4: Keep the Core Objective Identical Across Tiers
Every tier should be assessable against the same underlying goal, even if the path to demonstrating it looks different.
- Check that a tier's product still lets you assess the same objective — a diagram-based response and a paragraph response can both show understanding of the same concept, if built deliberately.
Step 5: Verify Each Tier for Rigor and Reading Level
A generated tier needs a human check before it reaches students, since a prompt can request a specific reading level without reliably hitting it.
- Spot-check vocabulary and sentence length in a "simplified" tier against your actual target reading level, not just the prompt's stated one.
A stated reading-level request in a prompt is a target, not a guarantee — a request for "third-grade vocabulary" can still return the occasional multi-syllable word a model considers common. That gap is exactly why this step stays a required part of the workflow rather than an optional extra, no matter how precisely Step 3's prompt was written.
Step 6: Track Which Students Get Which Tier — Without Labeling It
How tiers are distributed and referenced in class matters as much as how they're built.
- Avoid naming tiers "easy/medium/hard" on the materials themselves — a neutral label (Group A/B/C, or no label at all) keeps the differentiation from becoming visible status.
- Rotate which students receive which tier over time where appropriate, rather than treating tiering as a fixed, permanent assignment based on one early assessment.
Worked Example: One Lesson, Three Tiers
Seeing the same lesson split three ways makes the lever-based approach concrete instead of abstract. Say a Grade 4 class is working through a lesson on equivalent fractions, and the on-level version asks students to shade fraction models and explain their reasoning in writing.
| Tier | Lever Adjusted | What Changes |
|---|---|---|
| On-level | — | Shade fraction models, write a 2–3 sentence explanation |
| Support tier | Process | Same models and question, plus a sentence-starter scaffold and one worked example first |
| Extension tier | Product | Same models, but students also generate one equivalent-fraction pair of their own and justify it |
Each tier still targets the identical objective — recognizing and explaining equivalent fractions — and a teacher could review all three side by side during the same lesson without the underlying skill being assessed shifting between them.
Why This Beats Three Separate Prompts Written From Scratch
Generating each tier from the finished on-level version, rather than describing the topic three separate times, is what keeps the fraction models, the numbers used, and the phrasing consistent across all three — which matters if the whole class discusses the activity together afterward.
Differentiating for IEP or 504 Accommodations vs. General Ability Tiers
General ability tiering and a legally mandated IEP or 504 accommodation are not the same thing, and treating them identically in an AI workflow risks understating what the law actually requires. An IEP accommodation is individualized, team-decided, and binding; a general tier is an instructional judgment call a teacher can adjust freely.
What AI Can Reasonably Help With
- Drafting a version of an activity that reflects an already-decided accommodation — extended time built into a shorter item count, or a format change already specified in the plan.
- Generating scaffolds that align with a stated accommodation type, like a graphic organizer for a student whose IEP specifies visual supports.
What Stays With the IEP Team
The Individuals with Disabilities Education Act (IDEA) requires that accommodations come from an individualized, team-based decision grounded in a specific student's documented needs — not from a generic "make this accessible" prompt. A generated worksheet variant can implement an accommodation the team already decided on; it should not be the source of that decision.
The Council for Exceptional Children (CEC) and the U.S. Department of Education's Office of Special Education Programs both frame accommodations as tied to an individual student's profile, not a general reading-level tier — which is exactly why a same lesson, same-objective structure used for general differentiation still needs the specific accommodation named explicitly when a tier is meant to serve an IEP or 504 plan.
For goal language itself, The Best AI Prompts for Writing IEP Goals covers drafting that separate, case-manager-owned document.
A Quick Litmus Test
One question separates the two cases cleanly: is this adjustment written down in a legal document tied to one specific student, or is it a teaching judgment call that could reasonably change week to week? The first case is an accommodation, and any generated material needs to implement what's already specified. The second is general tiering, and the six-step workflow above applies directly.
Tools for AI-Assisted Differentiation
Different tools handle the six-step workflow with different amounts of manual re-entry along the way.
| Tool Type | Strength | Trade-Off |
|---|---|---|
| General AI chatbot | Flexible across any lever or subject | Full lesson context needs re-pasting for every tier |
| Classroom content platform (e.g., EduGenius) | Holds a class profile with stated ability ranges | Best suited to structured content formats |
| District-provided accommodation templates | Pre-approved, compliance-vetted | Not built for day-to-day general tiering |
EduGenius's class profiles let a teacher set an ability range once and generate an on-level activity plus tiered variants from that same saved profile, which is designed to remove the need to redescribe reading level and rigor in every single tiering prompt. New accounts start with 25 welcome credits, and the Professional plan runs $15.99 a month for 1,000 credits for classrooms tiering activities on a regular basis across a full course load.
A general chatbot works fine for occasional tiering on a single lesson, but the repeated context — grade level, ability range, subject — adds up across a full week of lesson planning. A platform that holds that context in a saved profile mainly pays off once tiering becomes a routine part of weekly planning rather than an occasional adjustment for one difficult lesson.
- Occasional tiering (one tricky lesson a week): a general chatbot works fine.
- Routine tiering (most lessons, every week): a saved class profile removes repeated re-typing of context.
- Compliance-bound accommodations: start from the district template, then use AI only to fill in content within it.
Once tiers are built, The Best AI Prompts for Giving Feedback covers adjusting feedback language to match the tier a student worked from, and How to Write AI Prompts for Financial Literacy is one example of a content area where the same lever-based tiering approach applies directly. The same lever discipline carries over into How to Write AI Prompts for Spanish, and once tiers are built, How to Generate 50 Quiz Questions in 5 Minutes With AI helps build a leveled practice bank for each one quickly.
Expert Advice for a Cleaner Differentiation Workflow
- Anchor every tiering prompt to the finished on-level version, pasted in directly, rather than a fresh topic description.
- Name the lever explicitly — content, process, product, or grouping — every single time, even when it feels repetitive to state.
- Keep the objective sentence identical across every tier's prompt, so a reviewer can confirm at a glance that nothing besides the named lever actually shifted.
- Build in a rotation plan for tier assignment, so tiering reflects a current need rather than a fixed label attached to a student all year.
- Save a working lever-adjustment prompt per subject, since a "simplify vocabulary, keep everything else identical" instruction reuses well across many different lessons.
- Try the grouping lever first on a busy week — it needs no new content generation at all, just a facilitation decision layered onto materials that already exist.
- Ask for a one-line rationale with each tier, like "scaffold added: sentence starters," so a substitute teacher or co-teacher can follow the tiering logic without a separate explanation.
What to Avoid When Differentiating With AI
- Adjusting more than one lever in a single tiering request. A prompt that changes reading level and response format together makes it hard to isolate which change actually helped a given student.
- Treating a general ability tier as equivalent to an IEP or 504 accommodation. A generated variant can implement an already-decided accommodation; it should never substitute for the individualized team decision behind one.
- Skipping the verification pass on reading level. A prompt can request a specific grade-level vocabulary and still miss it — a quick human check catches what the request alone can't guarantee.
- Labeling tiers by difficulty on the materials themselves. Visible "easy/medium/hard" labels can turn an instructional decision into a status marker students notice and remember.
- Confusing reteaching with differentiation. A whole-class reteach and a same-lesson tiered variant solve different problems, and prompting for one when you mean the other wastes the workflow's structure.
Key Takeaways
- AI-assisted differentiation works best organized around Tomlinson's four levers — content, process, product, and environment/grouping — adjusted one at a time.
- Every tiered version should be generated from the finished on-level version, not a fresh description of the topic, to keep the underlying objective identical.
- A verification pass on reading level and rigor is required even after a well-built prompt, since a stated request doesn't guarantee the result lands where intended.
- General ability tiering and IEP/504 accommodations are legally distinct — AI can help draft an already-decided accommodation, but the decision itself stays with the IEP team.
- Neutral tier labeling and periodic rotation keep differentiation from becoming a visible, fixed status for a given student.
- A saved, lever-specific prompt reuses across subjects and lessons far more easily than one written fresh for each new topic.
Frequently Asked Questions
What's the best AI workflow for differentiating a lesson?
Build the on-level version first, then generate each tiered variant by adjusting exactly one lever — content, process, product, or grouping — from Carol Ann Tomlinson's differentiation framework, while keeping the learning objective identical across every tier. Verify reading level and rigor before handing tiers out.
Can AI replace the IEP team's decision about student accommodations?
No. AI can help draft a version of an activity that implements an accommodation the IEP team has already decided on, but the individualized decision itself is required by law to come from that team, grounded in a specific student's documented needs — not from a general differentiation prompt.
How many tiers should a differentiated lesson have?
There's no fixed number; two or three tiers (support, on-level, extension) cover most classrooms without becoming unmanageable to prep and track. More tiers than that tend to add prep time without a proportional instructional benefit for most lessons.
Why does a "simplify this" prompt sometimes make a lesson too easy?
Because a vague simplification request often changes multiple things at once — vocabulary, question count, and conceptual rigor together — rather than adjusting just the specific lever a student needs. Naming a single lever explicitly, like content or process alone, keeps the rest of the lesson's rigor intact.
References
- Carol Ann Tomlinson — differentiation framework (content, process, product, environment), widely referenced through ASCD.
- CAST — Universal Design for Learning guidance distinguishing UDL from after-the-fact differentiation.
- Individuals with Disabilities Education Act (IDEA) — legal basis for individualized IEP accommodation decisions.
- Council for Exceptional Children (CEC) — guidance on accommodations as individualized, not general-tier, decisions.