We bring transparency to written work.
AI detectors are unreliable. Professors are skeptical. Honest work gets questioned. And students have no way to show how they struggled.
OKhuman changes that. One stamp. Shows what went into the work.
For Students
You did the work.
Now you can show it.
AI detectors can flag honest writing. And you've had no way to prove otherwise.
OKhuman captures your writing activity as you write. Not your words, just the work behind them. Use the desktop app with any writing tool, or write directly on a platform that integrates OKhuman. One click produces a stamp.
Stop defending your work.
Start proving it.
Stop surveilling.
Stop guessing.
Give students agency.
For Faculty
You didn't sign up to police AI use.
AI detectors can flag human writing erroneously. AI detectors can also be fooled easily. And confronting students is never fun. You're left guessing and you're spending your precious time on this.
OKhuman changes the situation. Students write and stamp their work — through the OKhuman app or directly on platforms that integrate it. You see what went into the work. No guessing, no awkward conversations. Sentence-by-sentence documentation of the writing process.
Same writing and submission workflow. One additional click.
For Institutions
When students skip the work, your credential pays the price.
AI made it easy for students to look accomplished. When these students join the workforce, their skills may fall short. The credential that you conferred will mean less. That's not a future problem. It's happening now.
Honor codes can't stop it. Detection tools can't catch it. But students can show what they put in and learned, if you give them a way.
OKhuman is that way. Written submissions get stamped. Writing continues to be a learning tool, not just an output. Save faculty time.
Students own their learning.
Faculty see the work.
Protect the weight of your credential.
Growing up in my grandmother's kitchen, I learned that the best meals require patience. Her arroz con pollo took three hours — the sofrito alone was forty minutes of stirring. She never measured anything. She'd taste, adjust, taste again. When I asked how she knew it was ready, she said you stop when it tells you something true. I think about that every time I sat down to work on this essay. The first draft is never the one that matters.
Throughout my academic journey, I have consistently demonstrated a passion for learning and a commitment to excellence. Junior year I enrolled in AP Government and it changed what I wanted to study. My experiences have shaped me into a well-rounded individual who is prepared to contribute meaningfully to your institution's vibrant academic community.
Admissions
The essay should reveal the person. Now they all read alike.
College admissions essays, law school personal statements, and medical school narratives are high-stakes and deeply personal. They are also now trivially easy to generate. Admissions officers reviewing thousands of applications have no reliable way to distinguish an essay a student spent weeks on from one produced in minutes.
OKhuman gives applicants a way to clarify their involvement in their essay. The stamp documents the effort behind the essay and travels with the submission.
Research & Peer Review
Peer review depends on peers actually reviewing.
Research conferences and peer review systems depend on genuine scholarly engagement. When reviews or contributions are AI-generated, the integrity of the scientific process is compromised. A 2025 analysis found roughly 21% of peer reviews at ICLR were fully AI-generated.
Major publishers have tightened policies, but enforcement depends on detection tools that submissions are designed to evade.
Research platforms and peer review systems can integrate OKhuman so that reviews and scholarly contributions carry evidence of genuine intellectual engagement.
The framing in Section 2 conflates two distinct failure modes. The cascade analysis assumes uniform propagation, but Figure 3 shows the opposite — failures cluster at boundary nodes. I'd rerun with stratified sampling.
Interesting approach. The ablation in 4.3 is convincing but I'm not sure the baseline comparison is fair — the authors should include the 2024 variant from Chen et al. which uses a similar attention mask. Minor revision.
This paper presents a comprehensive and well-structured analysis of the proposed methodology. The theoretical framework is sound and the experimental design is rigorous. I recommend acceptance with minor revisions.