Few topics in educational technology generate as much heated debate as the use of AI in grading. The conversation is everywhere right now and the opinions run strong in every direction. Some teachers say AI is the only realistic way to keep up with grading loads that have become genuinely unsustainable. Others feel just as strongly that assessment is too personal, too contextual, and too central to the teacher-student relationship to hand off to a machine, even partially. And administrators are often caught somewhere in the middle, trying to figure out what to encourage, what to allow, and what to restrict.
I have spent a lot of time reading through teacher discussions on Reddit, Quora, LinkedIn, and X , studying the available research, and testing several of these tools myself. And yes, I find myself sympathizing with both camps, depending on the context.
Here is where I come down on it. AI can be a genuinely useful grading assistant, but only when you treat it as a first draft, never a final word. The moment you hand off your professional judgment to an algorithm entirely, you lose something critical. You lose the ability to recognize that a struggling student just turned in their most coherent essay yet. You lose the context of knowing that a strong writer had a rough week. You lose the relational weight of feedback that a student knows came from someone who actually read their work and cared enough to respond. No AI tool, however advanced, can read a student’s writing the way their teacher can.
But here is the other side. A Gallup survey found that teachers who use AI weekly save an average of 5.9 hours per week, which adds up to roughly six extra weeks over a school year. Three in ten teachers now use AI tools weekly. And 57% of teachers report that AI actually improves their grading and feedback. Schools with formal AI policies see 26% larger time savings compared to schools without them.
So the real question is about boundaries. Where does helpful assistance end and problematic automation begin? And who gets to draw that line? I believe teachers, not administrators or tech companies, should be the ones setting those boundaries for their own classrooms.
In this post, I share some of my thinking on the topic, built on ideas from many educators and researchers out there. I walk through the AI grading tools available right now, starting with how you can use chatbots like ChatGPT, Gemini, and Claude for grading, then covering dedicated grading platforms. I also share practical tips for getting the most out of these tools, and I close with an honest look at the limitations every teacher should know about.
Please note that I am not affiliated with any tool or platform mentioned here and its inclusion in this guide does not necessarily mean endorsement!
Related: Formative Assessment Simply Explained
Using AI Chatbots for Grading
Before looking at specialized grading platforms, it is worth starting with the AI chatbots most teachers already have access to. ChatGPT, Google Gemini, and Claude can all function as grading assistants, and a growing number of teachers are already using them exactly that way. The key is knowing how to prompt them effectively.
ChatGPT
ChatGPT is arguably the most widely used AI chatbot in education right now. Many teachers typically use it for grading in this way: you paste a student’s essay into the chat along with your rubric and grading criteria, and ChatGPT returns a score breakdown with specific feedback for each rubric category. The quality of the output depends almost entirely on the quality of your prompt. Teachers who get the best results include the grade level and course context, the full rubric with point values, the specific assignment prompt the student was responding to, and examples of the kind of feedback they normally give.
One approach worth trying: create a custom GPT that already knows your rubric, your grade level, and your feedback style. You set it up once, and then you can drop in student essays without repeating your instructions every time. Some teachers have taken it even further, building Google Sheets integrations through the ChatGPT API that batch-process student responses and return scores and comments in a spreadsheet.
The ChatGPT for Education program also deserves a mention. OpenAI launched a version specifically for K-12 schools with built-in privacy protections, and student data does not get used to train the models. Schools can set up accounts where teachers get access to the full feature set at no cost.
Google Gemini
Google Gemini is especially practical if your school already runs on Google Workspace. Gemini is built into the Google ecosystem, so you can use it directly inside Google Docs, Sheets, and Classroom without switching between platforms.
The grading workflow follows a similar pattern: you provide your rubric and a student submission, and Gemini generates feedback and suggested scores. Where Gemini has a real advantage for many teachers is the integration. You can highlight a section of student writing in Google Docs, ask Gemini for feedback on just that section, and paste the response as a comment. No copying and pasting between browser tabs.
Gemini for Education comes with data protection policies aligned with Google Workspace for Education standards, which is important for schools with strict data governance requirements. If your district already approved Google Workspace, Gemini typically falls under the same umbrella agreement.
Claude
Claude by Anthropic handles longer documents better than most chatbots, which makes it particularly useful for grading extended essays, research papers, and longer writing assignments. The free tier gives you access to Claude Sonnet, and the Pro plan ($20/month) adds higher usage limits and access to more powerful models.
You can ask Claude to evaluate an essay against your rubric, suggest areas for improvement with specific references to the student’s text, and generate differentiated feedback based on student proficiency level.
You can also ask Claude to flag patterns across multiple submissions. Paste in several student essays from the same assignment, and ask it to identify common misconceptions, recurring grammar issues, or gaps in understanding across the class set. Insights like these help you adjust your teaching, not just your grading.
Getting the Best Results from AI Chatbots
Across all three chatbots, a few practices consistently make the difference between useful output and generic filler.
Give the AI your full rubric with point values for each criterion. Include the grade level and course context, because a 9th-grade personal narrative and a 12th-grade AP argument essay need completely different feedback, and the AI has no way of knowing the difference unless you spell it out. Provide the specific assignment prompt the student was responding to. Share examples of your own feedback style, so the AI can match your voice. And set clear instructions about tone, something like “Be encouraging but specific” or “Focus on two strengths and two areas for growth.”
You should also tell the chatbot if the student was allowed to use AI in creating their work, because that changes the evaluation criteria significantly.
| Ideal workflow: Run student work through the chatbot, read the AI-generated feedback carefully, adjust the score and rewrite comments where the AI missed nuance, and then share the revised feedback with the student. The AI handles the time-consuming first draft; you add the professional judgment that only a teacher can provide. |
AI Grading Tools for Teachers
Beyond chatbots, a growing number of platforms are built from the ground up for grading. These tools plug directly into your LMS, support batch processing of full class sets, and offer specialized features that general-purpose chatbots cannot match.
1. CoGrader
CoGrader is probably the most talked-about dedicated grading tool in teacher communities right now. It connects directly to Google Classroom and Canvas, and you can grade a full class set of essays in a fraction of the normal time.
The workflow is simple: connect your LMS, select an assignment, paste in your rubric, and CoGrader generates feedback and scores for every submission. You review each one before releasing grades. The free tier handles up to 100 essays per month, which is enough for most teachers to give it a serious try.
2. Gradescope
Gradescope (now owned by Turnitin) is the heavyweight, particularly in higher education and advanced secondary courses. It handles essays, math problems, bubble sheets, code assignments, and even handwritten work.
The feature teachers talk about most is auto-grouping: the system clusters similar student answers together, so you grade one response and apply that score to every similar submission. Pricing runs $1 to $3 per student per course depending on the plan. A growing number of universities are now using Gradescope at institutional scale.
3. Brisk Teaching
Brisk Teaching is a free Chrome extension, and the zero price tag makes it one of the easiest tools to try. It works as an overlay on Google Docs, Word Online, and most LMS platforms, so there is no new interface to learn.
You can pick from several feedback modes: “Glow & Grow” (strengths and growth areas), rubric-based scoring, targeted comments on specific passages, and next-steps guidance. Brisk supports 20+ languages, which is valuable for multilingual classrooms. The tool also includes features for adjusting text complexity and detecting AI-generated writing.
4. EssayGrader.ai
EssayGrader.ai has built a user base of over 100,000 teachers. The free tier gives you 25 essays per month, and it integrates natively with Google Classroom, Canvas, and Schoology. You upload your rubric, and the tool returns a score breakdown with comments for each criterion.
The platform also includes an AI detection feature and a comment bank you can build and reuse across assignments. Paid plans start around $19.99/month for teachers with heavier grading loads.
5. GPTZero
GPTZero is best known as an AI detection tool, but it has expanded into a full grading assistant. The combination is genuinely practical: you can grade student essays and check for AI-generated content in the same workflow. Over 380,000 educators are on the platform.
The grading side works with custom rubrics and provides detailed feedback by criterion. If your school is already using GPTZero for AI detection, adding the grading features is a natural extension of what you already have.
6. VibeGrade
VibeGrade is a newer tool gaining traction quickly. It works inside Google Docs, Canvas, and Google Classroom, and it claims a 90% reduction in grading time.
The feature worth knowing about is “Replay.” It lets you watch the student’s writing process unfold step by step, so you can see how the essay was actually composed. For teachers concerned about academic integrity, that kind of process visibility is far more revealing than any AI detection algorithm.
7. Formative (with Luna)
Formative focuses on real-time assessment. Luna, the platform’s AI assistant, auto-grades multiple-choice, true/false, matching, and fill-in-the-blank questions as students submit them. For open-ended responses, Luna uses “Smart Grouping” to cluster similar answers so you can grade them in batches.
The real-time monitoring feature is where Formative really earns its name: you can watch student progress during class, identify who is struggling, and intervene before the lesson is over. It functions more as a formative assessment companion than an end-of-unit grading platform.
8. Turnitin Feedback Studio
Turnitin Feedback Studio is the tool most schools already know. The updated version includes QuickMarks (a drag-and-drop comment library), video and voice comments, AI-generated summary feedback, and the integrated similarity and AI detection reports Turnitin is known for.
If your institution already has a Turnitin license, Feedback Studio is likely included. The familiarity factor is real: there is no new system to learn and no IT approval to chase.
Tips for Using AI in Grading
The tools above can save you significant time, but the results depend on how thoughtfully you use them. Here are some practical tips drawn from teacher discussions, education research, and my own experience.
| Start with low-stakes assignments. Use AI feedback on first drafts, practice essays, and exit tickets before trying it on summative assessments. The risk is lower, you get a feel for how accurate the tool is with your specific assignments and student population, and you can adjust your approach before the stakes go up. |
| Build a detailed rubric first. AI grading accuracy correlates directly with rubric specificity. A rubric that says “clear thesis statement” gives the AI much less to work with than one that says “thesis statement appears in the first paragraph, takes a clear position on the topic, and previews the main supporting arguments.” Research from FutureEd at Georgetown University shows that AI accuracy with rubrics hovers around 50-55%, and drops to 33% without them. The specificity of your rubric directly shapes the quality of AI output. |
| Always review before releasing. Every experienced teacher I have read or spoken with says the same thing: read the AI-generated feedback before students see it. Adjust scores where the AI missed context, rewrite comments that sound generic, and add personal observations the AI could never make. The AI saves you time on the first pass; your expertise makes the final version worth reading. |
| Use AI for feedback on drafts, not just final grading. One of the most effective approaches involves running early drafts through AI tools, returning the feedback to students for revision, and then doing the final grading yourself. AI becomes a formative tool in the writing process, and students get an additional round of feedback they would not have received otherwise. You can even have students respond to the AI feedback as part of their revision work, which builds critical thinking about feedback itself. |
| Tell the AI about your students. Include grade level, course context, and the specific assignment prompt in your instructions. A 9th-grade personal narrative and a 12th-grade AP argument essay call for completely different feedback, and the AI has no way of knowing the difference unless you provide that context. |
| Keep a comment bank. As you review AI-generated feedback and edit it into your own voice, save the best comments. You end up building a personal library of feedback that deploys faster each time and always sounds like you. |
| Watch for repetitive feedback. One of the most common complaints from teachers is that AI gives the same suggestions to every student: “add more examples,” “use more statistics,” “strengthen your conclusion.” When you notice the AI defaulting to these patterns, it usually means your rubric needs added specificity or your prompt needs additional detail. |
| Be transparent with students. Let students know you are using AI as part of your grading workflow. Explain what the AI does and what you do. Honesty here actually strengthens trust, and it addresses the reasonable concern students raise when they are told not to use AI for writing but then receive AI-generated feedback on their work. |
Limitations and Concerns
AI grading tools are useful, but they come with real limitations that deserve honest attention.
Accuracy on subjective writing is moderate at best. Research from FutureEd at Georgetown University and The Hechinger Report shows that AI essay grading accuracy hovers around 50-55% when a rubric is provided. Without a rubric, accuracy drops to about 33%. AI performs well on objective assessments like math problems with clear answers, multiple choice, and fill-in-the-blank questions. But it struggles with the nuance of creative writing, argument quality, and critical thinking.
Bias against English language learners is well-documented. A 2025 study on ESL bias in automated grading found 15-20% score discrepancies for English language learners compared to native speakers producing equivalent-quality work. High-proficiency ESL essays received scores 10.3% lower than native-speaker essays rated the same by human graders. Stanford research has shown that AI detection tools are similarly biased against non-native English writers, which compounds the problem when grading and detection happen in the same workflow.
Racial bias is present too. A study covered by The 74 found that AI systems underperformed by up to 25% on work from speakers of African American Vernacular English. Separate research covered by Chalkbeat found racial bias in AI teacher assistant tools, including grading features. The root cause is straightforward: training data reflects dominant language patterns, so the AI treats linguistic variation as error.
AI cannot read context the way a teacher can. Your grading tool does not know that a particular student has been struggling all semester and just produced their most organized essay yet. It cannot recognize that a strong student’s weak submission might signal something going on outside of school. Contextual judgments like these are at the core of good teaching, and no algorithm can replicate them.
Generic feedback can weaken the teacher-student relationship. Multiple teachers have raised what one educator called the “feedback from me” problem. Students value feedback partly because it comes from someone who knows them, who has read their previous work, and who cares about their growth. When AI generates the comments, some of that relational value is lost. Students are also perceptive: they notice when feedback feels automated.
AI tends to reward formulaic writing. Teachers have observed that AI feedback consistently pushes students toward traditional five-paragraph structure, even when the assignment calls for something different. If you are teaching creative nonfiction, unconventional argument forms, or any format that breaks the standard mold, you will need to calibrate your prompts carefully or risk the AI rewarding predictable writing over original thinking.
Privacy and data concerns vary by tool. When you paste student work into ChatGPT or another chatbot, that text is processed by external servers. Some tools explicitly state that student data is not used for model training (OpenAI’s education program, for example), and dedicated platforms like Brisk and CoGrader tend to have more specific data protection policies. But you should always check what happens to the student work you upload, especially if your district has FERPA, COPPA, or SOPIPA requirements.
AI in Grading is also available in PDF format!
Conclusion
AI grading tools are here, and the conversation has moved past “should we use them at all” to “how do we use them responsibly.” The time savings data is hard to argue with: 5.9 hours per week, six extra weeks per school year, and a majority of teachers reporting better feedback quality. When you are carrying 150 students and trying to give each one the attention they deserve, numbers like these are hard to ignore.
But the limitations are just as real. Moderate accuracy on subjective writing, documented bias against English language learners and speakers of non-standard English, and the inevitable loss of personal connection when feedback comes from a machine. Every one of those concerns deserves honest attention.
My take: use AI for the mechanical part of grading. The first pass, the initial feedback, the time-consuming read-through that keeps you up until midnight on a school night. Then bring your own judgment to the final version. Review every score. Rewrite every comment that does not sound like you. Add the observations that only someone who knows that student could make.
The best version of AI in grading is one where students get more feedback, more often, with greater detail, and the teacher’s voice and judgment remain at the center. I think the effort to find that balance is absolutely worth it.
