The Four Modes of AI Assignments
How much AI on this assignment?
· Updated August 14, 2026
How much AI should students use on this assignment?
Setting the policy per assignment, not per school
A schoolwide line like “AI may be used responsibly” gives a ninth-grader nothing to act on, and it gives you nothing to point at when a submitted essay reads like nobody wrote it. Responsibility is not a behavior. The student who pasted the prompt in and the student who argued with the chatbot for an hour can both say they were responsible, and both are telling the truth as they understood the rule.
The unit of an AI policy is the assignment, and inside the assignment it is the phase. What a student may do while reading a source is a different question from what they may do while drafting a thesis. A policy that cannot tell those apart leaves the student to decide, which is not their job. Four modes cover the range.
The Four Modes of AI Assignments answer how much. The Five Tests ask the prior question of whether AI belongs in the task at all, starting from UNESCO’s guidance on generative AI in education. Attempt → Coach → Verify → Transfer sets the order of events inside the lesson once you have said yes. We built all three out of the research; none of them is a validated model, and they sit together on the AI and K-12 hub.
What each mode permits, and when to reach for it
The four differ on who may touch AI and at which phase of the work.
Mode 0 — AI-free
Students use nothing, at any phase.
Reach for it when you are measuring independent proficiency, when students are building the foundational knowledge that makes later judgment possible, when the performance of the skill is itself the objective, or when the material is personal enough that a machine has no business in it.
Mode 0 needs one more line than most teachers expect. A bare “no AI” never says whether spellcheck, text-to-speech, translation, and approved accommodations are still allowed, so say which of them are. A student with a read-aloud tool in their IEP should not have to guess whether the rule took it away.
Mode 1 — Teacher-directed
Students use nothing on their own. You use AI to prepare material, or to put something in front of the class.
Reach for it when students are too young for open interaction, or when you want to model catching an error where everyone can watch it happen. A confident wrong answer, corrected out loud, does more than a warning about hallucination.
Mode 2 — AI-assisted after an attempt
Students use defined feedback or questioning functions, and only after finishing specified independent work.
Reach for it when the reasoning has to be theirs and a challenge to it would sharpen the revision. The condition on the front is the whole difference between this and unrestricted use: a student who has already committed to a thesis reads a counterargument as something to answer rather than something to adopt.
Mode 3 — AI-integrated
Students use AI, and are graded on how well they verify, critique, and account for it.
Reach for it when the system is the object of study — auditing an AI narrative of a historical event, comparing how two models frame a public issue, analyzing synthetic media. Mode 3 is not the permissive end of the scale. It asks more of a student than Mode 0 does, because using the tool well and showing your reasoning about it is a harder thing to demonstrate than working without it.
One unit, several modes
Modes attach to phases, so one unit can carry several. A research project might run Mode 1 while you build the source packet the night before, then Mode 0 for the background-knowledge quiz, then Mode 2 through drafting. Label each phase on the assignment sheet. A course-level rule cannot tell a student which phase they are in.
Writing the mode into the assignment itself
Students meet a mode as a paragraph at the top of the handout, which means that paragraph carries the whole policy. A Mode-2 policy for a document-based argument runs about like this:
The role limits name what the tool may and may not produce, so a dispute gets settled by looking at the assignment sheet. The revision memo is the cheapest look you get at what a student did with the suggestions. The in-class response tells you whether any of it stuck.
The memo gets easier to write when students keep a verification record as they draft — four columns, filled in as each suggestion arrives.
| The AI suggested | Checked against | Accepted, changed, rejected | Why |
|---|---|---|---|
| A supporting quote | The assigned source packet | Rejected | Not in any assigned text |
| A citation | The library database | Rejected | Source could not be found |
| A counterargument | Document 4 | Accepted | Document 4 supports it |
A student who goes to the database, cannot find the citation the chatbot offered, and writes it off is practicing epistemic vigilance: checking a claim instead of complying with it.
Modes govern what students may do while working. How you gather evidence of learning once students have access to AI is assessment design.
Redesigning an assignment you already teach
Teachers rarely design an assignment from nothing. The mode gets retrofitted onto a unit you have taught for six years.
1. Name the non-negotiable learning (the protected core)
What students in this course must be able to do with nobody helping. In social studies that list usually holds corroborating accounts, telling evidence from assertion, explaining causation, and constructing an argument in writing.
2. Audit the assignment against a chatbot
Could a general chatbot produce a satisfactory product? Could a student finish without reading the assigned sources? Does the rubric reward polish more than reasoning, and what would become invisible to you?
3. Classify the mode, then rebuild the evidence
Label every phase with one of the four, then add the evidence that mode leaves you short of: an initial attempt, source annotations, a revision memo, an unseen transfer task.
4. Define the AI role in verbs
What it may do — ask questions, generate a counterargument, apply the rubric without assigning a grade — and what it may not: supply prose, evidence, or citations. Then put points on the verification.
5. Protect the human parts, then check locally
Specify where students defend a position aloud, teach a peer the causal chain, or work with no devices on the table. Judge the redesign on independent no-AI transfer, delayed retention, and source-verification accuracy.
An assignment a chatbot can complete easily is not necessarily obsolete; it may need a different sequence and a different evidence structure instead. Two or three added pieces of evidence are plenty. And students enjoying the redesign, finishing more work, or handing in cleaner prose is not grounds to approve it.
Building the materials a Mode-2 assignment needs
A Mode-2 sequence asks you to make three things before class: the attempt task students complete first, a source packet they can actually read, and a no-AI transfer task for the end. That is three things to build before the first bell, on top of the lesson itself.
Kindred K-12 is built for that part of the work. Bring a source in as a link, a PDF, or a photograph of a page. Cut it to the passage you want, adjust the reading level, or translate it for the students who need that, then build the worksheet around it. Kindred K-12 has no student accounts, keeps no personal information about students, and its AI never chats with a student. If you are looking at this for a department, what Kindred K-12 offers instructional leaders is the place to start.
Labeling the mode before the work starts
Next time an assignment comes back suspiciously polished, ask which mode it was in, and whether it ever said so on the page students read.
Most schools already have an AI policy. Actually getting it into classrooms is the hard part. We help with that. Talk to us.