How BridgeLab AI works
A small, transparent pipeline. Designed so a teacher can explain it in one slide.
Pipeline
Photo → Hint
- 1. Capture
- 2. Detect components
- 3. Rule-guided diagnosis
- 4. Bilingual explanation
- 5. Hint + follow-up
- 6. Aggregate to teacher view
Pitch summary
BridgeLab AI turns lab mistakes into teachable moments by combining visual setup analysis, bilingual GenAI explanations, hint-first learning, and teacher analytics.
1. Student uploads
A photo or short video of the build is captured on a tablet or phone, locally.
2. Component detection
A vision model identifies the components on the bench: board, LED, resistor, sensor, driver, jumpers.
3. Rule-guided inference
Detected components and their relationships are checked against a library of common lab mistakes.
4. GenAI explanation
A language model writes a short, age-appropriate explanation in Arabic or English from the structured diagnosis.
5. Hint-first pedagogy
The response is shaped as a hint plus a follow-up question instead of a full solution.
6. Teacher analytics
Anonymous diagnoses are aggregated into class-level errors, safety alerts, and intervention suggestions.
Responsible AI
Responsible AI by design
Five commitments that shape how BridgeLab AI behaves in a real classroom.
Hint-first, not answer dumping. Students still have to think.
Teacher remains in control. Decisions stay with the educator.
Minimal student data. No accounts, no PII, history stays local.
Designed for safer lab sessions by surfacing safety-relevant issues first.
Class-level insights, not student surveillance.