AI analysis of handwritten answer sheets
60%less evaluation time
Indian EdTech company (name withheld)
- Client
- Indian EdTech company (name withheld)
- Industry
- Education
- Region
- India
- Engagement
- AI engineering
- Platforms
- Web
The challenge
Assessments gave aggregate scores without diagnostics. The client, an Indian EdTech company, wanted subjective answer sheets analyzed for topic-level gaps and personalized remediation for every student.
What the system had to handle
- Handwritten, subjective answers rather than multiple choice
- Scoring that follows each question's rubric
- Gaps identified by concept and topic, not just a total mark
- Results that students, parents and schools can act on
Our role
Full-cycle technology and AI partner: DeepSeek-based language-model architecture, data pipelines, model training and inference optimization.
How it works
- 1Answer sheetsHandwritten subjective answers
- 2Computer visionOpenCV and TensorFlow
- 3LLM scoringDeepSeek, trained on contextual scoring rubrics
- 4Error classificationConcept-level errors mapped to topics
- 5Learning pathDynamic remediation for each student
- 6DashboardsParent heatmaps and school-wide insights
What we built
AI scoring engine
Trained on contextual scoring rubrics for subjective answers.
Concept-level diagnostics
Error classification and topic mapping for each answer.
Remedial learning paths
A dynamic path for each student based on their gaps.
Parent dashboards
Performance heatmaps for each child.
School-wide insights
Patterns across classes for curriculum planning.
Results
- In production across multiple schools in India
Technology
- AI
- DeepSeek LLM, computer vision (OpenCV), TensorFlow
- Language
- Python
- Web
- Angular
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