# Which Decisions Should AI Never Make in Your School? Canonical URL: https://blog.theaieducator.io/posts/which-decisions-should-ai-never-make Publication: Dan Fitzpatrick Insights Author: Dan Fitzpatrick Topic: AI Governance and Responsible Leadership Published: 2026-09-08T08:02:56.000Z Modified: 2026-09-08T08:03:28.109Z Washington bars AI from deciding IEP eligibility but allows it to draft IEP language. England says the same thing in different words. Dan Fitzpatrick reads the rule underneath both, and offers a three-question test for drawing the line in your own organization. ## In brief AI may draft a decision, but it may not make one. The line that DC's September 2026 model policy and England's updated DfE guidance both draw falls between producing the words of a decision and producing the decision itself. Dan Fitzpatrick's Answerability Test asks three questions before AI goes near any decision: who does this decision land on, could a named person explain it to them in their own words without mentioning the tool, and if it were wrong, who puts it right and by when? Decisions that fail any of the three belong to a named human being. ## Key takeaways - The defensible line for AI in decision-making runs between producing the words of a decision and producing the decision itself. - The District of Columbia's model AI policy, published September 1, 2026, prohibits AI from determining IEP or Section 504 eligibility while permitting it to draft IEP language with oversight. - England's Department for Education guidance, updated May 28, 2026, applies the same rule: AI may draft support plans, but may not decide whether a student has a special educational need. - Model policies list tasks, which age out as tools change; leaders need the rule underneath so they can classify new tools themselves. - Dan Fitzpatrick's Answerability Test asks who the decision lands on, whether a named person could explain it without mentioning the tool, and who puts it right if it is wrong. - Every AI-touched decision should be sorted into draft, inform or decide, and anything landing in decide should either be moved or given a named owner. - RAND reported in September 2025 that 45 percent of principals had school or district AI policies or guidance, while 53 percent of core-subject teachers were already using AI for school. On September 1, 2026, the District of Columbia's Office of the State Superintendent of Education published a [model AI policy](https://osse.dc.gov/release/osse-releases-ai-model-policy-guide-responsible-staff-use-schools) that sorts staff use of AI into colors. Red, prohibited: student discipline decisions, evaluations of teacher performance, and determining eligibility for individualized education programs or Section 504 accommodations. Yellow, permitted with enhanced oversight: drafting IEP language, and reviewing and grading student work. Read those two lists again. Drafting IEP language is yellow. Determining who gets an IEP is red. The subject is identical. The verb is not. Four thousand miles away, England's Department for Education updated its guidance for schools on May 28, 2026 and drew the line in the same place. Staff may use AI to draft initial versions of support plans and template letters home. Staff may not use it to decide whether a student has a special educational need, and statutory documents such as EHCP contributions need significant professional review before they go anywhere. Neither document announces the rule it is applying. Both apply the same one. That matters this term, because a lot of leaders are about to write a policy against a deadline. Ohio districts had to adopt one by July 1, 2026 under House Bill 96. Maryland districts are working to a fall deadline set by the Artificial Intelligence Ready Schools Act. California published its model policy in June. Copy the lists and you inherit this year's answer to a question that changes every time a vendor ships a feature. Take the rule and you can write next year's answer yourself. ## The short answer: AI may draft a decision, it may not make one The defensible line runs between producing the words of a decision and producing the decision itself. A machine can write the first version of a support plan, a report comment, a governors' paper or a letter to a family, because a person still reads it, changes it and stands behind it. A machine may not determine eligibility, settle a grade or decide a suspension, because at that moment nobody is left holding the reasoning. This has little to do with how good the model is, and everything to do with what happens when the person on the receiving end asks why. A parent whose child has been refused an assessment is owed an explanation from someone who can give one. So is a teacher who has been marked down, and so is a student who has been sent home. If the only available answer is that the system flagged it, the decision was never the school's to begin with. Nobody is harmed by a machine writing a sentence. People are harmed by a decision nobody can explain. ## Why the model policies look like they disagree, and do not The apparent contradiction between Washington and Westminster dissolves the moment you read for verbs instead of tasks. Washington's red list is a list of determinations: who qualifies, what grade, what consequence, how a teacher performed. Its yellow list is a list of drafts and reviews, each of which lands in front of a human being who has to accept or reject it. England's guidance does the same thing in prose rather than color: draft the plan, do not decide the need. Once you see that, the disagreement between jurisdictions turns out to be smaller than it looks. What varies is where each system places a particular task, following local law, local risk appetite and local capacity. What does not vary is the principle underneath. Maryland's guidance puts it as plainly as any: the teacher will always be the subject matter expert. Leaders keep asking me which state or which national guidance to follow. It is usually the wrong question. The lists differ. The rule does not. ## The Answerability Test The Answerability Test is three questions I ask before letting AI near any decision: Who does this decision land on? Could a named person explain it to them, in their own words, without mentioning the tool? And if it turned out to be wrong, who puts it right, and by when? A decision that survives all three can be drafted by a machine. A decision that fails any of them has to be made by a person, and that person has to be named. This is my suggested way of thinking about the line, not a validated instrument, and it is deliberately blunt so that it survives contact with a busy leadership team. The second question does the real work. "Without mentioning the tool" is the part people find uncomfortable, and it should be. If a deputy head can justify a decision on its merits, the AI that helped produce it was a drafting aid. If the justification collapses into a description of the software, the school has quietly transferred a judgment it is still legally and morally holding. The third question is the one most policies forget. Every automated process eventually produces a wrong answer about a real child, and the organizations that come out of it well are the ones that already knew who fixes it. ## Draft, inform, decide Sort every AI-touched decision in your organization into one of three verbs, and treat the third as a refusal. **Draft.** AI produces the words. A person owns the judgment and could defend it unaided. A support plan drafted by a model and rewritten by the teacher who knows the child sits here, along with most report writing and nearly all correspondence. **Inform.** AI supplies evidence into a decision a person makes, and that person knows how the evidence was produced and where it fails. An attendance model that flags a student as at risk belongs here, but only if the pastoral lead can say what the flag is counting and which students it systematically misses. **Decide.** The output becomes the outcome, and nobody is in a position to answer for it. Automatic grade release, automated eligibility screening and any rule that fires without a human in the path belong here. This is the category to refuse, in writing, in your policy, by name. The first two are work. The third is an abdication wearing the clothes of efficiency. ## What I Tell Leadership Teams I tell them the list in their policy will be out of date before the year is out, and to write the rule down next to it. Across the leadership teams I work with, the pattern is consistent: they arrive with a document that names tools and leave with one that names decisions. That change is small on paper and large in practice, because a policy built around tools has to be rewritten every time procurement signs something new, and a policy built around decisions only has to be rewritten when the school changes its mind about who is accountable. The second thing I tell them is to run the test on decisions they already make with software, not just the ones involving generative AI. Timetabling, behavior points, reading-age scores and safeguarding filters have been shaping outcomes for years with far less scrutiny than a chatbot now attracts. Some of those, examined honestly, are already sitting in the decide column. Having worked with government bodies on AI guidance, including the Department for Education, KHDA in Dubai and the Ministry of Education in Kazakhstan, I would add one caution about model policies generally. They are written to be adoptable by every institution in a jurisdiction, which means they describe a floor. A floor is a useful thing to stand on. It is not a strategy, and it is not the same as knowing where your own line falls. ## What good looks like A school where this is settled can produce, on request, a short list of decisions AI may not make and a named person against each one. That is the observable test, and it is a low bar that very few clear. The [RAND American Educator Panels](https://www.rand.org/pubs/research_reports/RRA4180-1.html), reporting in September 2025, found 45 percent of principals reported having school or district policies or guidance on AI use, against 53 percent of English language arts, math and science teachers who said they used AI for school. Guidance has been running behind practice for a while, and the deadlines arriving this fall are the system's attempt to close the gap. Good also looks like a decision that has been reversed. When a leadership team can tell you about the time an AI-assisted judgment was wrong, who noticed, and what happened next, the accountability is real rather than declared. Until then, you have a document. ## Where to start this term Take the ten decisions your organization makes most often about individual people, and sort them by verb before you touch the policy. Do it with the people who make those decisions in the room, because the person doing the drafting knows exactly where the judgment currently sits. Put each decision in draft, inform or decide. For anything that lands in decide, either move it or name the person who answers for it. Then take the decide column to your board or governors, because that column is the one they will be asked about, and [the questions a leadership team should be able to answer about AI](https://dan-fitzpatrick-blog.replit.app/posts/questions-every-leadership-team-should-ask-about-ai) start with who owns the answer. This work sits alongside two decisions many schools have already made this year: [whether and how to restrict students' own use of AI](https://dan-fitzpatrick-blog.replit.app/posts/should-schools-ban-ai), which is a separate question about learning rather than accountability, and [what responsible AI adoption looks like in practice](https://dan-fitzpatrick-blog.replit.app/posts/what-responsible-ai-adoption-looks-like-in-practice), which supplies the surrounding behaviors. If you are unsure whether the rest of your organization could hold a decision like this, [the seven dimensions of AI readiness](https://dan-fitzpatrick-blog.replit.app/posts/seven-dimensions-of-ai-readiness) will tell you where the weakness is. The deadline in your calendar asks for a policy. The question underneath it asks who you are willing to name. If your leadership team or your board is working through that question this term, it is the kind of work I support through AI governance and strategy sessions. ## Sources and further reading - [OSSE Releases AI Model Policy to Guide Responsible Staff Use in Schools](https://osse.dc.gov/release/osse-releases-ai-model-policy-guide-responsible-staff-use-schools), Office of the State Superintendent of Education, District of Columbia, September 1, 2026. - [DfE guidance suggests schools use AI to draft SEND support plans](https://schoolsweek.co.uk/dfe-guidance-suggests-schools-use-ai-to-draft-send-support-plans/), Schools Week, May 28, 2026, reporting the Department for Education's updated guidance for schools and colleges. - [AI Model Policy for Ohio Districts and Schools](https://education.ohio.gov/Topics/AI-in-Ohio-s-Education/Model-Policy), Ohio Department of Education and Workforce, adoption required by July 1, 2026 under House Bill 96. - [Maryland school districts face fall deadline to set AI policies](https://marylandmatters.org/2026/07/06/maryland-school-districts-face-fall-deadline-to-set-ai-policies/), Maryland Matters, July 6, 2026, on the Artificial Intelligence Ready Schools Act. - [Release of Model Policy: Artificial Intelligence in Education](https://www.cde.ca.gov/nr/el/le/yr26ltr0706.asp), California Department of Education, July 6, 2026. - [AI Use in Schools Is Quickly Increasing but Guidance Lags Behind](https://www.rand.org/pubs/research_reports/RRA4180-1.html), Doss and colleagues, RAND, September 30, 2025. *Dan Fitzpatrick is [The AI Educator](https://www.theaieducator.io/about): a Forbes contributor, keynote speaker and author who helps leaders and organizations understand, plan for and lead the changes caused by AI.* ## Frequently asked questions ### Which decisions should AI never make in a school? Any decision that lands on a named person and that nobody could explain to them without describing the software. In practice that means eligibility determinations, grades released without review, discipline outcomes and staff performance judgments. DC's model policy prohibits exactly these four, and England's guidance reaches the same conclusion. ### Can teachers use AI to draft IEPs or SEND support plans? Yes, in both jurisdictions, with review. DC's policy places drafting IEP language in its restricted category, permitted with enhanced oversight. England's Department for Education guidance, updated in May 2026, allows AI to draft initial versions of support plans while requiring significant professional review of statutory documents. ### Can AI grade student work? Reviewing and grading student work sits in DC's restricted category rather than its prohibited one, meaning it is permitted with enhanced oversight. The distinction that matters is whether a teacher reads the judgment, can change it, and could defend the grade on its merits if a student challenged it. ### Does our school or district need an AI policy this year? In several places it is now a legal requirement. Ohio districts had to adopt one by July 1, 2026 under House Bill 96, and Maryland districts are working to a fall 2026 deadline under the Artificial Intelligence Ready Schools Act. California and DC have published model policies to adopt or adapt. ### Who is accountable when an AI-assisted decision about a student is wrong? Whoever your policy names, which is why the naming matters more than the technology. If no person is named against a decision, accountability defaults to the person who happened to click, usually the least senior individual in the chain. Name the owner before the mistake, not after it. ### What is the difference between AI drafting a decision and making one? Drafting means AI produces the words and a person owns the judgment and could defend it unaided. Deciding means the output becomes the outcome and nobody is positioned to answer for it. The same task can fall either side of that line depending on whether a human review is real. --- Source: [Which Decisions Should AI Never Make in Your School?](https://blog.theaieducator.io/posts/which-decisions-should-ai-never-make) Publisher: [The AI Educator](https://theaieducator.io)