Apache-2.0 · Offline-first · Multilingual · Built on the public education commons
Give India's 1 crore teachers their hours back.
OpenPathshala is an open-source AI toolkit that automates the highest-friction classroom workflows — grading handwritten answers, setting question papers, generating mother-tongue notes — designed to run on every tier of Indian school infrastructure, from no-electricity rural schools to fully-connected urban ones, at a cost of single-digit rupees per task.
Live demo runs on open LLMs (Groq). The same router targets Claude in production.
UDISE+ 2024-25
The central design constraint
One country, three completely different schools.
A national tool that assumes connectivity excludes the schools that need help most. Here's the real infrastructure picture from UDISE+ 2024-25 — the gap between "has electricity" and "has working internet" is the whole problem.
Source: UDISE+ 2024-25, Ministry of Education, Govt. of India. Functional %, national average.
Tiered degradation, not feature gating
The same workflow runs everywhere — it just degrades gracefully.
Premium schools don't get features poor schools are denied. Every tier gets the workflow; the model quality and latency adapt to what the school has.
No power · No internet
Paper + a phone that's briefly charged. Capture now, queue, sync and process later. Fully on-device / edge open models — zero recurring API cost.
- Photograph scripts when a device is free
- Batch-grade overnight, print & return
- On-device OCR + small open LLM
Shared devices · Intermittent net
The majority of Indian schools. A single mini-PC at a Block Resource Centre serves a cluster. Open models do the bulk; a frontier model is reserved for hard judgment.
- Edge node syncs when online
- Open-model transcription + selective escalation
- Per-school budget caps
Connected · 1:1 or lab
Funded / private schools. Full cloud routing — the value (teacher-hours returned) dwarfs the cost. Frontier vision + reasoning on every task.
- Real-time grading & feedback
- Misconception analytics for the class
- DIKSHA / WhatsApp integrations
See it in action
How AI does the heavy lifting.
Animated walkthroughs of the core workflows — from a phone photo of a handwritten script to a graded result, from a textbook to a ready exam paper, and a camera that watches an exam hall so a teacher doesn't have to.
Live prototype — really runs
Try the two MVP workflows.
These call open LLMs server-side through the same 3-tier router the architecture specifies. Each result shows you which model tier handled it, the latency, and the per-task cost. Teacher-in-the-loop: every output is a reviewable draft, never a final mark.
⚠️ Shared public demo — rate-limited and capped. Outputs are AI-generated drafts for demonstration; in a real deployment a teacher reviews and overrides every result.
§7 of the spec — the most important cost decision
Route by judgment required, not by default to the biggest model.
Most classroom tasks are cheap extraction. A minority need reasoning. A small fraction need deep reasoning. Honest routing — a 70/20/10 split — cuts cost by more than half with negligible quality loss.
A toolkit, not a single app
The workflow catalogue.
Each module is chosen because it returns hours to a teacher — not because it's technically interesting. The two highlighted are the live MVP.
Built in the open
A genuine public good — Apache-2.0, built on the commons.
OpenPathshala extends India's public education infrastructure — DIKSHA / Sunbird, Bhashini, openly-licensed NCERT content — instead of rebuilding it. The toolkit must run with no paid API at all (open models are the floor); frontier models like Claude are the quality ceiling it routes to when a school can fund it.
- ✅ Permissive Apache-2.0 license — maximizes adoption & dependency
- ✅ Provider-agnostic router — Groq / Ollama / vLLM / Claude
- ✅ DPDP-Act-aligned privacy: on-device redaction, consent ledger, teacher-in-the-loop
- ✅ Offline-first so it reaches the schools that need it most
The path to credibility
Ship publicly → deploy to 5–10 real schools → measure teacher-hours saved & grading accuracy → apply via the discretionary "ecosystem depends on it" path with evidence.