Lehr Assist
GDPR compliant AI correction
Current Assumption
"German teachers will adopt AI-assisted correction if it is GDPR-compliant."
Target User
German teachers (DaF/DaZ) with high privacy sensitivity and limited time.
Phase
Signal Detection
Primary Signal
Product Page Conversion (iOS): 12.8% (↑ 198%)
Decision Framework
○ Intent
Define the specific behavior change expected (e.g., teachers replacing red pens with the app).
○ Assumption
German teachers will adopt AI-assisted correction if it is GDPR-compliant.
○ Experiment Design
Release v1.1 with automated PII detection and 'bottom drawer' UI to a cohort of 50 teachers and measure retention vs v1.0.
○ Decision Gates
- If D7 Retention > 25% -> Scale
- If Session Duration < 2m -> Pivot
- If Costs > €5/user -> Stop
○ Current Decision
Refine (Focus on Friction Removal)
○ Confidence LevelHigh
Current Position
- Phase: Signal Detection / Interpretation.
- Testing: Does the automated PII detection and a 'bottom drawer' UI reduce the cognitive load?
- Decision Gates: If D7 Retention > 25% → Scale. If Session Duration < 2m → Pivot.
Decision History
2026-01-24
Refine (Friction Removal)
Acquisition signals are strong (12.8% conv.), but retention is poor (3 MAU). Must resolve onboarding friction and privacy concerns.
2026-01-18
Signal Creation
Initial release of v1.1 PRD features including EU-hosted Gemini OCR and spaCy PII detection.