How Do Teachers Check for AI-Generated Code?

If you are a CS student, it helps to know what actually happens to your code after you submit it. Most of what instructors use is not a single "AI detector" but a set of ordinary course tools, each answering a narrower question, plus their own judgement. This guide goes through those methods one at a time, using what each tool's official documentation says. We read those pages in October 2026. Your course may use only some of these, or others not listed here, so treat this as a map rather than a description of your class.

Step zero: the course policy

Before any tool, there is the policy. Some courses ban AI help entirely, some allow it for specific parts of an assignment, and some allow it with disclosure. The same code can be fine in one course and a violation in another. Instructors check your work against that policy, so reading it, and asking when it is unclear, is the single most useful thing you can do.

Method 1: similarity reports

The most common automated check in programming courses compares submissions with each other.

• MOSS. The official MOSS page describes a system for detecting software similarity. An instructor submits the whole class's files and gets back pairs of programs that share code, highlighted side by side.
• Gradescope Code Similarity. Gradescope's guide says the report shows how similar students' code is to each other within a single assignment and does not automatically detect plagiarism. Instructors can upload starter code so that matches against it are ignored.

Neither tool looks for AI. They show resemblance. If several students paste similar generated answers, that resemblance can appear; a unique solution will not. See does MOSS detect AI code for more on why.

Method 2: AI writing and code detectors

Some schools have AI detection built into tools they already license, and some instructors paste code into a standalone detector. It is worth knowing the limits. Turnitin's AI writing FAQ says its model removes text that appears to be code before scoring, does not reliably detect AI-generated code, and that Turnitin is not pursuing ChatGPT code detection at this time. Your code itself is therefore not what Turnitin's AI score is built for, though a written report or README in prose can be. Code-specific detectors, like the one on this page, estimate from style patterns and can be wrong in either direction.

Method 3: your repository and its history

Many courses hand out work through GitHub Classroom. According to GitHub's documentation on individual assignments, when a student accepts an assignment, GitHub Classroom creates a new repository for that student, and each assignment repository belongs to the teacher's organization account. Teachers can optionally turn on feedback pull requests and autograding tests that run each time you push.

The assignment overview page lets teachers see which students have accepted and submitted, whether submissions were on time or late, autograding results, and a link to each student's repository. Because the repository sits in the course's organization, staff can open it and read the commits. A history of small, sensible steps tells a very different story from one commit containing a finished program, although neither is proof on its own.

Method 4: reading the code

Graders read a lot of code from the same assignment, and they know what is taught in the course. Things that stand out to a reader include:

• a big jump in style or quality compared with your earlier work;
• libraries, language features or patterns the course has not covered;
• code that solves a slightly different problem from the one set, or ignores a stated constraint;
• generic extras such as a comment on every line, a demo block or input validation nobody asked for.

These are reasons to look closer, not conclusions. Our guide on how to tell if code was written by ChatGPT covers each signal and how often it misfires.

Method 5: asking you

The step that settles most questions is a conversation. An instructor might ask you to walk through your solution, explain why you chose one approach, or make a small change while they watch. Some courses build this into grading for everyone through code interviews or in-class follow-up questions. OpenAI's educator FAQ, written for teachers, says that in OpenAI's experience AI detectors are not reliable enough to base decisions on, and suggests approaches such as asking students to share their ChatGPT conversations to show their work. In practice that pushes teachers toward exactly this kind of conversation and process evidence.

Method 6: assessments where AI is not available

Some courses avoid the detection question altogether by moving part of the grade into settings where the rules are easy to enforce: in-lab programming exams, quizzes on paper, or short coding tasks done in class. If your take-home work is excellent but you struggle with very similar problems under exam conditions, that gap is itself something an instructor may notice. It is also the clearest reason to make sure any help you get on homework, from AI or from people, leaves you able to do the work yourself.

What this means for you

• Know your policy. If AI help is allowed, disclose it the way the course asks.
• Commit as you work. Your history is your best record of how the solution came together.
• Understand everything you submit. If you cannot explain a line, that is the line you will be asked about.
• Keep drafts until grades are final.
• If a tool flags you, stay calm and bring your evidence. Our guide for when your code was flagged as AI walks through the steps.

Seeing your code the way a grader might

You cannot run a class-wide similarity report on your own, but you can check how your code reads. Paste it into the detector at the top of this page and look at the reasons it gives. If it points at patterns you do not normally write, think about whether that part reflects your own understanding, and be ready to talk about it. Teachers who want the instructor-side view can read our AI code detection guide for teachers.

Frequently asked questions

Try the detector

Have a snippet you are unsure about? Paste it into the free AI Code Detector to get a verdict, an AI probability and the reasons behind it. No sign-up, and your code is not stored.