Lehr Assist

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GDPR compliant AI correction & High-Accuracy OCR

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Consequence

Round2

ConfidenceHigh

Updated2026-01-25

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

2026-01-25

OCR Benchmark: Best Model for German Handwriting (Gemini vs Mistral)

We benchmarked Gemini 2.5 Flash Lite vs Mistral for German handwriting recognition. See why Flash Lite wins on speed, accuracy, and error preservation.

v3: Exercises Generation

v3 introduced a new capability: AI-generated exercises for class preparation. The trigger was direct feedback from a teacher who said she struggled to prepare lessons — a clear gap that sat right next to the correction workflow Lehr Assist already covered.

With v3, Lehr Assist now spans the full teaching loop: from preparing class exercises to scanning and correcting handwritten essays. One teacher, one app, zero context-switching.