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tutordesk-ai

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TutorDesk AI is an on-laptop teaching copilot built for Indian tuition teachers. A teacher photographs a textbook chapter and, in one click, gets a complete teaching pack — worksheet, homework, test, answer key, and a parent progress note — in their regional language, with auto-generated diagrams for younger classes.

Prep time drops from ~90 minutes/day to under 10. Everything that can run locally does, on a laptop, using models ≤8B.

This is a deliberately Backyard AI product: a specific user (the home/coaching tutor), real daily adoption, appropriately small models, and a polished Gradio app.

Problem Statement

Indian tuition teachers (home tutors, small coaching centers, evening/online tutors) spend 60–120 minutes/day manually creating worksheets, tests, homework, answer keys, and parent updates — often re-making near-identical material. This admin load steals time from teaching.

Existing AI tools assume English, cloud access, and CBSE-only context. Regional-board and regional-language teachers are underserved, and many work with patchy connectivity.

Vision

The assistant every tuition teacher opens before every class. A full lesson package from a single chapter photo, in under five minutes, in the teacher's own language.

Target Users

Primary: Independent tuition teacher — home tutor, small coaching-center owner, evening/online tutor (Classes 1–10).

Secondary: School teacher needing practice worksheets, unit tests, and revision material.

Hackathon Strategy (why this design wins)

The hackathon rewards one coherent product that legitimately touches several sponsor stacks, plus optional bonus-quest badges. TutorDesk is architected so each headline feature claims a different sponsor or award, while telling a single tight story.

Per organizer rules: max 32B params total, must run on a laptop, Gradio app on a Hugging Face Space, plus a demo video and social post. (OpenAI/NVIDIA sponsor tracks intentionally not targeted. JetBrains track skipped — not product-relevant.)

Sponsor / Award map

Headline Feature	Sponsor / Award targeted	Model / Mechanism
1. Worksheet-from-Textbook (vision)	OpenBMB — $10,000 (anchor)	MiniCPM-V 4.5 (8B) reads chapter photos/PDFs
2. Weekly Teaching Pack (multi-agent)	Best Agent — $1,000 + core Backyard AI	5-agent pipeline orchestration
3. Regional-language generation	Cohere — $5,000	Aya Expanse 8B / Tiny Aya (Hindi, Tamil, Telugu, Bengali, Gujarati, Marathi, …)
4. Illustrated / diagram worksheets	Black Forest Labs — $3,000	FLUX generates science/geometry/picture-question art
5. Photo Auto-Grading	OpenBMB + Well-Tuned/Tiny Titan + Best Agent	MiniCPM-V reads the sheet → fine-tuned Qwen3-4B grades it Indian-style

Bonus-quest badges & special awards

Badge / Award	How TutorDesk earns it
Well-Tuned	Publish a fine-tuned Qwen3-4B on Indian curriculum (CBSE + state-board Q&A)
Modal — $20,000 credits	Run the fine-tuning training job on Modal (finetuning use is eligible)
Tiny Titan — $1,500 (≤4B)	Text generation runs on the 4B fine-tune
Sharing is Caring	Publish agent traces (the multi-agent conversation logs) as a HF dataset
Off the Grid	Offer a fully-local mode — MiniCPM-V + Qwen3-4B via llama.cpp, no cloud APIs
Llama Champion	Local inference uses the llama.cpp runtime
Off-Brand — $1,500	Custom frontend via `gr.Server` (not stock Gradio layout)
Field Notes	Publish a build/report blog post
Best Demo — $1,000	The 90-min → 10-min teacher demo story

Core Features (MVP — the 5 that win)

Feature 1 — Worksheet-from-Textbook → OpenBMB

Teacher uploads a chapter photo or textbook PDF. The vision model extracts topic, concepts, and learning objectives, then generates a worksheet + quiz + answer key grounded in the actual chapter content.

Input: chapter image / PDF
Model: MiniCPM-V 4.5 (8B) (laptop-runnable; beats GPT-4o on vision at 8B)
Output: worksheet, quiz, answer key

This is the "wow" — content appears from a snapshot of the page.

Feature 2 — Weekly Teaching Pack (signature) → Best Agent

One click. Input: Class, Subject, Chapter (or the photo from Feature 1).

Output, via a 5-agent pipeline:

Curriculum Understanding Agent — topic, objectives, concepts
Question Generation Agent — questions across difficulty levels
Difficulty Validation Agent — ensures grade-appropriate level
Answer Generation Agent — answer key + step-by-step solutions
Report Writing Agent — parent update template

Bundled deliverable: worksheet + homework + quiz + answer key + parent note.

Feature 3 — Regional-Language Generation → Cohere

Any generated artifact can be produced in the teacher's language.

Phase 1: English, Hindi, Tamil
Phase 2: Telugu, Bengali, Gujarati, Marathi, Kannada, Malayalam
Model: Cohere Aya Expanse 8B / Tiny Aya (Tiny Aya covers South Asian languages and launched at the India AI Summit)

This is the differentiator for regional-board teachers — the underserved majority.

Feature 4 — Illustrated / Diagram Worksheets → Black Forest Labs

For Classes 1–5 and for science/geometry, generate printable illustrations: labeled science diagrams, geometry figures, and picture-based questions.

Model: FLUX
Output: worksheet art embedded in the printable PDF

Unexpected delight in an edu tool, and a clean BFL claim.

Feature 5 — Photo Auto-Grading → OpenBMB + Well-Tuned + Best Agent

Teacher photographs a student's filled answer sheet. A two-stage pipeline grades it and returns marks + per-question feedback, closing the loop: generate worksheet → student writes → photograph → auto-grade → parent note.

Pipeline (text model is vision-blind, so vision must run first):

MiniCPM-V 4.5 (vision) reads the sheet → extracts the student's answers as structured text (question-by-question).
Fine-tuned Qwen3-4B (text) grades each answer against the answer key, applying Indian marking conventions — CBSE / state-board step marks, partial credit, method marks — not a generic Western rubric. This is where the Indian-curriculum fine-tune pays off.

Output: total score, per-question marks, mistakes flagged, and a one-line parent note auto-drafted from the result.

Demo note: use neat/printed answers — messy handwriting challenges any 8B vision model. Handwriting robustness is a roadmap item.
Fine-tune requirement: training data must include marking schemes, model answers, and step-wise grading examples, not just questions — so the model learns to mark, not just generate.

Why it's strong: real load-bearing AI, completes the teacher workflow, and stacks three claims (OpenBMB vision + Well-Tuned/Tiny Titan marking + Best Agent pipeline). Also feeds the published agent-trace dataset.

Roadmap (post-MVP — do NOT demo, keep as slides)

Deferred to avoid scope sprawl that hurts polish/delight scores:

Full Parent Progress Report (manual inputs) — Feature 5 already auto-drafts a parent note from grading results; the rich standalone report is a fast-follow
Student Performance Analyzer (Excel/CSV upload → weak/strong students, common mistakes) — natural next step: aggregate Feature 5 grading across the class
Remedial Learning Plan Generator (week-by-week improvement plan)
Question Bank Builder (extract questions/topics/difficulty from uploaded papers)
Handwriting-robust grading

Model Stack

Role	Model	Sponsor / Award	Notes
Vision (textbook + answer-sheet reading)	MiniCPM-V 4.5 (8B)	OpenBMB	Built on Qwen3-8B + SigLIP2; runs on laptop. 4.6 (1.3B) is the lightweight/Off-the-Grid fallback (lower OCR accuracy on dense pages)
Text generation + Indian-style grading	Fine-tuned Qwen3-4B	Modal + Well-Tuned + Tiny Titan	Trained on Modal; ≤4B for Tiny Titan; grades with marking-scheme logic
Multilingual generation	Cohere Aya Expanse 8B / Tiny Aya	Cohere	Regional Indian languages
Image generation	FLUX	Black Forest Labs	Diagrams & illustrations

All language models ≤8B; total stack well under the 32B cap. ✅

Local mode (Off the Grid + Llama Champion): MiniCPM-V + fine-tuned Qwen3-4B served via llama.cpp, no cloud APIs. Cohere/FLUX features degrade gracefully or fall back to local substitutes when offline.

Fine-Tuning Strategy → Well-Tuned + Modal + Tiny Titan

Fine-tune Qwen3-4B for:

Educational content & question generation
Difficulty classification
Indian curriculum alignment (CBSE + state boards)
Indian-style grading — apply marking schemes, step marks, and partial credit (powers Feature 5)

Training job runs on Modal (finetuning is an eligible Modal use). The resulting model is published to the Hugging Face Hub to claim the Well-Tuned badge, and its 4B size qualifies for Tiny Titan.

Dataset

CBSE question papers, state-board papers
Teacher-created worksheets, educational PDFs
Open educational resources
Marking schemes, model answers, and step-wise graded solutions (required for Feature 5 grading quality)

Agent Traces as a Dataset → Sharing is Caring

The multi-agent pipeline's conversation logs (curriculum → question → validation → answer → report) are captured and published as a Hugging Face dataset: "Indian-curriculum worksheet-generation agent traces." This earns the Sharing is Caring badge and doubles as a reusable open-education resource (and a Field Notes blog topic).

User Interface → Off-Brand

Platform: Gradio, with a custom frontend via gr.Server (claims Off-Brand)
Deployment: Hugging Face Space
Design: mobile-first, teacher-focused, one-tap workflows
Every output is print-ready (PDF) — teachers print and hand out

Success Metrics

Prep time: 60–120 min/day → < 10 min (target headline metric)
Weekly time saved: ~7–10 hours per teacher
Content generated: worksheets / tests / homework per week
Satisfaction: "Would you use this again tomorrow?" → target 80%+

Demo Story (Backyard AI + Best Demo)

A tutor teaching Classes 6–10 spends ~90 minutes/day on prep.

With TutorDesk AI:

Photograph today's chapter (MiniCPM-V reads it)
One click → Weekly Teaching Pack (5-agent pipeline)
Output in Tamil (Cohere) with diagrams for the Class-6 group (FLUX)
Print worksheet + test; next day, photograph a student's answer sheet → auto-graded Indian-style (MiniCPM-V + fine-tuned Qwen3-4B), with a parent note auto-drafted

Time: 90 minutes → under 10. ~7–10 hours saved every week.

A real, specific user; real daily adoption; appropriately small models; a polished app — directly aligned with Backyard AI judging, while stacking OpenBMB, Cohere, Black Forest Labs, Modal, and six bonus badges.

MVP Checklist

Must-have for submission:

Feature 1 — Worksheet-from-Textbook (MiniCPM-V 4.5)
Feature 2 — Weekly Teaching Pack (5-agent)
Feature 3 — Regional-language output (Cohere Aya)
Feature 4 — Illustrated worksheets (FLUX)
Feature 5 — Photo Auto-Grading (MiniCPM-V reads → fine-tuned Qwen3-4B grades)
Fine-tuned Qwen3-4B published (Modal-trained)
Agent traces dataset published
Local mode via llama.cpp
Custom gr.Server frontend on a HF Space
Demo video + social post + Field Notes blog