Understanding students · Empowering teachers
AI that reads psycholinguistic signals in student writing to flag mental health distress early — so the teacher can respond first.
90.5%
Accuracy on real student text
40
Psycholinguistic features
24.7K
Student essays audited for fairness
0 data
Stored from your students
The Problem
Mental health distress often surfaces in how students write — word choice, sentence patterns, emotional tone — long before they seek help. Teachers see these words every day and miss the signal buried inside.
A student's essay about summer might contain death-word density 4× above baseline. Without computational analysis, no teacher can catch this pattern across 30 students.
A typical BC teacher reads hundreds of pieces of student writing each week. Systematic early detection is humanly impossible without a tool.
Without early signals, teachers and counselors only respond after a crisis. UEdu aims to shift the window — from reaction to prevention.
How It Works
UEdu uses a deterministic, auditable pipeline. Every score is explainable — no opaque AI guesswork.
40 psycholinguistic features extracted deterministically — death-word density, self-focus pronoun ratio, cognitive distortion patterns, temporal urgency, and more.
XGBoost classifier trained on 232,000 Reddit mental health posts scores each feature vector. The model is static, auditable, and never updates on student data.
Claude (Claude AI) translates the signal into actionable, empathetic teacher guidance — constrained by MHFA and BC ERASE Bullying knowledge bases. The teacher decides.
Design principle: "Psychology discovers. Code measures. AI translates. The teacher decides." — UEdu is a first-response companion, not a diagnostic tool. All flags require human review.
Research Results
Validated on 232,000 Reddit posts, 24,700 ASAP student essays, and 200 real-world samples.
232,000 Reddit posts · 5-fold CV
256 synthetic student texts
Top signals driving detection
Social pronoun patterns are 2× more diagnostic than emotional vocabulary alone.
200 real Kaggle samples
Hybrid v3 recommended for schools: misses almost no real distress (5% FNR) while keeping false alarms manageable.
Exp 3 · Fairness Audit
We audited UEdu against 24,728 real ASAP student essays across five demographic dimensions. Our hypothesis was that ESL students might get higher false-positive rates due to language differences.
The result surprised us. ESL students had a lower false positive rate than native English speakers (0.72×, p = 0.022). The model detects psychological distress signals, not writing ability.
False Positive Rate by Group (M5)
Lower FPR = fewer false alarms. All rates < 3%, well within acceptable clinical thresholds.
Ongoing commitment: Fairness audits will be re-run with each model update. UEdu will never ship a model with statistically significant demographic bias above predefined thresholds.
Interactive Demo
Paste a writing sample and see UEdu analyze psycholinguistic features in real time.
Paste any student writing sample and see UEdu's Hybrid model and GPT-4o-mini analyze it side by side, with psycholinguistic feature breakdowns and teacher guidance.
Open Interactive DemoNo text is stored or used for training. All analysis is ephemeral.
About the Project
Built by a high school student in BC, UEdu is offered free to all educators. No venture funding, no data harvesting — just a genuine effort to help schools support student wellbeing.
All 40 features are documented and auditable. The training dataset (232K Reddit posts) is publicly available on Kaggle. No black-box AI.
Student text is analyzed locally — never stored, never sent to cloud services for training. PIPEDA-compliant by design.
UEdu is built for BC teachers. Join our early access list to be the first to pilot the tool in your classroom.
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