A writing analysis platform that evaluates student submissions for structure, argumentation quality, citation usage, and AI-generated content patterns, giving faculty specific feedback beyond simple plagiarism detection.
Plagiarism detection tools were the go-to for academic integrity, but they only answered one question: was this text copied from somewhere? They said nothing about whether the writing was actually good. Did the arguments hold together? Were citations backing up claims? Was the student doing real critical thinking? Faculty were left doing all of that evaluation manually, submission by submission.
Meanwhile, AI-generated content was becoming harder to spot. Students could prompt a language model and get polished prose that passed plagiarism checks entirely. The writing looked clean on the surface but lacked the hallmarks of authentic student work: uneven depth, a developing voice, real engagement with sources. Existing tools had no way to flag these patterns.
Faculty at the institution were spending hours giving the same structural feedback across dozens of submissions: fix your thesis placement, strengthen your argument transitions, cite more than one source per claim. They needed a way to automate the mechanical parts of writing evaluation so they could focus where it mattered: on the ideas themselves.
Existing tools caught copying but said nothing about argumentation quality, logical structure, or whether evidence actually supported claims.
Faculty had no automated way to assess whether student arguments were well-structured, logically sound, or properly supported by evidence across an entire cohort.
Faculty spent hours writing the same comments (fix your thesis, improve transitions, add citations) on submission after submission, every term.
AI-generated submissions passed plagiarism scans cleanly. Polished but hollow prose was getting harder for faculty to distinguish from genuine student work.
Built a PHP and Python backend pipeline that accepts PDF, DOCX, and TXT uploads, extracts clean text while preserving structural markers (headings, paragraphs, citations), and normalizes content for downstream analysis.
Implemented natural language processing pipelines that evaluate argumentation structure, thesis clarity, paragraph coherence, transition quality, and evidence usage. Each dimension receives a scored assessment with specific feedback.
Layered pattern analysis that goes beyond surface-level plagiarism. It evaluates stylistic consistency, vocabulary distribution, sentence complexity variance, and other markers that distinguish authentic student writing from AI-generated text.
A web frontend that surfaces per-submission scores, cross-cohort comparisons, and trend analysis. Faculty can drill into individual submissions or zoom out to see writing quality patterns across an entire section or term.
Let's talk about how automated writing analysis could help faculty at your institution provide better feedback, faster.
Start a ConversationAccept PDF, DOCX, and TXT submissions with automatic text extraction that preserves structural markers like headings, paragraphs, and citation blocks.
Quality scores covering clarity, coherence, vocabulary usage, sentence variety, and readability, broken down into specific dimensions faculty can act on.
Evaluates thesis placement, claim-evidence pairing, logical flow between paragraphs, and whether conclusions follow from the arguments presented.
Assesses citation density, source diversity, integration quality, and whether references actually support the claims they accompany.
Pattern analysis that identifies stylistic uniformity, vocabulary distribution anomalies, and complexity variance typical of machine-generated text.
Cross-submission comparative analysis, cohort-level trends, readability metrics, and per-student progress tracking. All in one interface.
Per-submission breakdown showing quality scores, argument structure maps, citation analysis, and AI detection results in a unified view.
Visual representation of thesis-to-evidence flow, showing how claims connect to supporting evidence and whether logical transitions are present.
Cross-submission analysis showing writing quality distribution, common structural weaknesses, and AI detection flags across an entire class section.
Instead of writing the same structural comments on every paper, I can see at a glance which students need help with argument flow and which ones need citation guidance. The AI detection catches things plagiarism checkers never would.
Early prototypes produced one overall score per submission. Faculty found it useless. They needed to know specifically what was weak: was it the argument logic, the citation usage, the transitions, or all three? Breaking analysis into distinct dimensions with individual scores and feedback made the tool actually useful.
Rather than only looking for AI patterns, comparing submissions against a student's own previous work revealed more. When a student who typically writes in short, direct sentences suddenly produces flowing academic prose, that contrast is a stronger signal than any vocabulary distribution analysis alone.
When most students in a section scored poorly on citation integration, that wasn't a student problem. It was a curriculum gap. Faculty started using cohort-level reports to adjust their teaching before the next assignment cycle. A grading tool became a teaching improvement tool.
Tell us about how your institution evaluates student writing and let's explore what an automated analysis platform could look like.
No pitch. No pressure. Just a conversation about what might work.