AI Risk Engine
An explainable recommendation system that analyzes fictional early-warning indicators — attendance, assessment trends, assignment completion, engagement, behavior, course performance, and intervention history — to produce a risk level, the contributing factors, a confidence level, recommended supports, and a monitoring schedule. It always explains why a student is flagged and never makes a final decision — educators do. All data shown is realistic fictional sample data.
How it works
- 📥 Analyzes indicators — attendance, assessment trends, completion, engagement, behavior, course performance, and intervention history.
- 📈 Weights recent trends — a downward turn this month counts more than a stable long-term pattern, so help arrives in time.
- 🔍 Explains the factors — every flag lists the contributing factors and a confidence level in plain language.
- 🪜 Suggests supports — recommends the least-intensive effective tier plus a monitoring schedule for review.
Responsible AI
- 🔎 Transparent — no black box; the reasoning is shown every time.
- 🧠 Explainable — factors and confidence are always stated.
- 👤 Educator decides — the engine recommends; people make every final decision.
- 🔒 Privacy-first — fictional data; nothing leaves your browser.
- ⚖️ Guards against over-reliance — a starting point for professional judgment, not a verdict.
This engine never decides
The AI Risk Engine produces decision-support only. Risk levels and recommendations are reviewed by educators before any action is taken on behalf of a student.
All data shown is realistic fictional sample data created for demonstration. AI analysis is simulated client-side and is decision-support only — educators make every decision.