--- title: "Using AI to Grade Student Work: Policy and Practice 2026" description: "Can teachers use AI to grade student work? Yes, with the teacher as grader of record. What official guidance says, what to disclose, and what to write into a department policy." canonical: "https://fiveable.me/using-ai-to-grade-student-work" type: "seo-landing" --- # Using AI to Grade Student Work: Policy and Practice 2026 ## Page Content ## Using AI to Grade Student Work: What's Allowed Can you use AI to grade student work? In most places, yes, and the official guidance that exists converges on one rule: the teacher stays the grader of record, reviewing AI output before it counts. The details worth knowing are what the federal and state guidance says, what disclosure looks like in practice, and what belongs in a department policy before someone asks for one. We built an FRQ grader, so we're not neutral here. The guidance quoted below is linked to its primary sources, and your district's own AI policy, if one exists, outranks all of it for your classroom. ## What the Official Guidance Says **The U.S. Department of Education** set the frame in its May 2023 report, [Artificial Intelligence and the Future of Teaching and Learning](https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf). Its first recommendation is titled "Emphasize Humans in the Loop," and it treats grading as a legitimate AI-assist use case: an AI assistant "may be able to reduce the load for teachers related to grading simpler aspects of student responses," freeing teacher judgment for the parts that need it. The same report is blunt that consequential decisions must allow teacher monitoring and overrides. **State guidance** has gotten specific. Georgia's Department of Education [K-12 AI guidance](https://gadoe.org/learning/artificial-intelligence/) (January 2025) draws the cleanest line in print: AI grading multiple-choice sits in the permitted column, AI grading subjective work sits in the high-stakes column, and its footnote requires that "final decision-making should always involve human judgment." Oklahoma's [K-12 AI guidance, version 2.0](https://oklahoma.gov/content/dam/ok/en/osde/ai-and-digital-learning/Guidance%20and%20Considerations%20for%20Using%20Artificial%20Intelligence%20in%20Oklahoma%20K-12%20Schools%202.0.pdf) (July 2025) endorses AI-assisted grading as an efficiency gain while holding that AI "must supplement, not replace, human instruction and decision-making." Massachusetts' [DESE guidance](https://www.doe.mass.edu/edtech/ai/ai-guidance.pdf) adds the transparency half: "educators, students, and families deserve to know when AI is involved in learning, grading, decision-making, or access to services." **The teachers' unions** land in the same place. The [NEA's 2024 policy statement](https://www.nea.org/resource-library/artificial-intelligence-education/vii-full-statement-text), adopted by its Representative Assembly, says AI-informed analyses "alone should never be used for high-stakes or determinative decisions," naming student assessment among them. And the [TeachAI policy toolkit](https://www.teachai.org/toolkit-guidance), the template many district policies borrow from, states it plainly: "AI will not be solely responsible for grading." Read together, the guidance adds up to a workflow requirement rather than a prohibition: AI produces a first pass, and a teacher makes the call. ## What to Disclose, and to Whom Disclosure is the emerging norm across the guidance, and it costs you a few plain sentences. Three groups need to hear it: - **Students**: it takes one line in the syllabus. "I use an AI tool to produce a first-pass score on free-response work; I review and finalize every grade myself." Students who know the workflow don't discover it secondhand. - **Families**: Massachusetts recommends folding AI-tool disclosure into existing technology consent communication, and that approach works anywhere. A line in the syllabus or the back-to-school letter answers the question before a parent asks it at conferences. - **Administrators**: tell your department chair which tool you use and under what data terms before the first graded assignment goes out. If your district has an approved-tools list, get on it. ## The Student Data Question The FERPA question is real, and it turns on which tool and what you send it. The safe pattern is a tool that operates under your school's data agreements, minimizes student personal information, and doesn't train models on student submissions. Pasting student essays into a personal consumer-chatbot account is the version districts prohibit, and the federal student privacy office now runs [dedicated breach-scenario training for AI grading systems](https://studentprivacy.ed.gov/resources/ai-grading-compromise-handouts), which tells you how seriously regulators take AI grading. For what it's worth on our side: Fiveable strips student identifiers before anything reaches an AI provider, doesn't use student work to train models, and documents its practices for districts on the [schools privacy page](/schools/privacy). ## A Department Policy in Four Lines If your department writes nothing else down, write these: 1. **Approved tools**: which AI grading tools may be used, under which data agreements. 2. **Teacher of record**: a human reviews every AI-suggested score before it counts, no exceptions. 3. **Disclosure**: the syllabus line, standardized across sections. 4. **Feedback routing**: whether AI-generated feedback may reach students directly or only after teacher review. Revisit annually; the guidance is moving fast enough that this year's policy will need a paragraph next year. ## What This Looks Like in a Real Workflow The guidance describes a workflow, and it's buildable today. In Fiveable's [FRQ grading](/grading), the AI scores a class set point by point and shows its reasoning on every point; nothing reaches a student until you've reviewed and approved it, which builds the teacher-of-record rule into the workflow instead of leaving it as a promise. The scoring is benchmarked against publicly released AP® samples with [per-subject results published](/frq/scoring-benchmarks), which is the evidence to bring when an administrator asks why this tool. That's what the guidance asks for: the AI drafts a score you can check, and you make the final call. ## FAQs ### Can teachers use AI to grade student work? Generally yes, and the consistent thread across official guidance is the human in the loop: the teacher reviews AI output and stays the grader of record. The U.S. Department of Education's 2023 AI report calls for humans in the loop for consequential decisions, and state guidance that addresses grading says the same. Check your district's own AI policy first; a growing number now have one. ### Do I have to tell students I use AI for grading? Disclosure is the emerging norm in official guidance, and it is also the practical move: one plain syllabus line, saying an AI tool produces a first pass that you review and finalize, answers the question before a parent asks it. Transparency recommendations appear across state guidance and the TeachAI policy toolkit. ### Is it a FERPA problem to put student work into an AI tool? It depends on the tool and what you send it. The safe pattern is a tool that operates under your school's data agreements and minimizes student personal information; sending student work through a consumer chatbot account is where districts draw the line. Fiveable strips student identifiers from what is sent to AI providers and documents its student data practices for schools. ### Should AI ever set the final grade? No. Treat the AI score as a first pass and make the final call yourself. In Fiveable's grading that review is built in: the AI suggests a point-by-point score with its reasoning, and nothing reaches a student until you approve it. You stay the grader of record. ### What should a department AI grading policy cover? Four things cover most of it: which tools are approved and under what data agreements, the teacher-of-record rule that a human reviews every score before it counts, a disclosure line for syllabi, and whether AI feedback may reach students directly or only after your review. Write it once and revisit each year. ## About This Document Canonical Fiveable pages are available as Markdown at the same path plus `.md`. - [llms.txt](https://fiveable.me/llms.txt): index of Fiveable's sections and URL patterns - [llms-full.txt](https://fiveable.me/llms-full.txt): complete subject and unit listing - [MCP server](https://fiveable.me/mcp): call Fiveable as tools instead of fetching pages (`https://fiveable.me/api/mcp`)