I’m confused about why instructors are struggling so much with students using artificial intelligence for assignments. Wouldn’t it be simple to just require all work to be done in platforms like Google Docs that automatically track changes?
With version history enabled, teachers could easily review how a document was created step by step. They would be able to see if a student worked on their paper gradually over time, making edits and revisions along the way. If the entire assignment appeared suddenly without any editing process, that would be a clear red flag that it was generated by AI rather than written by the student.
This seems like such an obvious solution that I’m wondering if there’s something I’m missing about why more schools aren’t implementing this approach.
You’re focused on the wrong problem. Don’t try to detect cheating - automate the whole process instead.
I’ve been through this at scale. Manual review always fails because humans can’t handle the volume. Version control dumps massive amounts of data that no one actually analyzes.
You need smart automation doing the work. Build workflows that automatically scan submission patterns across entire classes. Let the system spot anomalies, compare writing styles to past work, and run multiple detection methods at once.
Combine version tracking with behavioral analysis, plagiarism detection, and AI screening - all running together. Students can beat one system, but beating multiple automated checks simultaneously? Nearly impossible.
Most schools don’t have the tech resources to build this stuff. But you can set up sophisticated automation without coding using the right platform. Create triggers, connect detection tools, let the system handle pattern recognition while teachers actually teach.
This scales way better than professors manually digging through edit histories for hundreds of students.
yeah, exactly! students often bypass tracking by writing in other apps, so gdocs version history doesn’t help much. plus, with so many students, profs might not want to dig into every single edit, making it a hassle to catch AI misuse.
Version control sounds good in theory, but it’s easily gamed in practice.
Students can paste AI content in chunks over several days, make fake edits, or copy text between docs to fake writing progress. Takes 10 minutes to create convincing edit history.
The real problem is scale. I’ve watched teams try manual log reviews - it’s a nightmare. Picture a professor with 150 students analyzing every document’s revision history. That’s hours per assignment.
This screams for automation instead of teachers doing manual detective work. You could set up automated workflows analyzing writing patterns, timing anomalies, and revision behaviors across all submissions at once.
Build triggers that flag suspicious docs, cross-reference writing styles, and integrate AI detection tools for comprehensive screening. All background work while teachers actually teach.
The key is having a platform handling complex workflows without coding skills. Most educators won’t build custom solutions, but they’d configure automated processes if the tool’s intuitive enough.
Version control as AI detection is fundamentally flawed because it assumes AI usage follows predictable patterns. After teaching composition for years, I’ve seen how wildly different legitimate student writing processes can be. Some students brainstorm offline, then type out a complete draft in one sitting - which looks exactly like AI generation under version control. Smart students have already figured out workarounds. They’ll ask AI to generate content with deliberate errors, then do real revisions over time. Others use AI for research and outlining instead of direct text generation, making everything look organic. There’s also a massive privacy issue that administrators ignore. Detailed keystroke and revision monitoring is basically surveilling every part of how students think and write. This changes the entire nature of academic work and probably violates institutional privacy policies. Version control just treats symptoms instead of asking why students use AI in the first place. Better assignment design and clearer academic integrity expectations work way better than technological surveillance.
Document tracking sounds good in theory, but it’s a nightmare in practice. The whole system falls apart if students don’t do 100% of their writing on the monitored platform. Most students I know draft stuff in different apps, write on their phones, or work offline and paste everything in at the end.
Then there’s the accessibility mess. You’re basically screwing over students who can’t afford the latest tech, have spotty internet, or just prefer different software. Plus, many schools have policies blocking third-party platforms from storing student data anyway.
Here’s what nobody’s talking about - legitimate writing doesn’t always look “normal.” I’ve seen students research for weeks then bang out entire papers in one sitting. Under version control analysis, that’d get flagged as AI even though it’s just how some people work.
The workload is insane too. Even with automated flags, someone has to manually review every suspicious case and decide if weird revision patterns mean AI cheating or just different writing habits. Good luck scaling that across thousands of students.
this misses the real issue - ai detection isn’t just about catching generated text anymore. students use ai for brainstorming, research, and editing their own work. version control won’t catch any of that since they’re still writing naturally over time.