Research2025–26

Drishti.

Make every mark traceable.

A human-in-the-loop marking platform connecting answer-sheet intake, AI-assisted evaluation, moderation, and QR-verifiable records.

MY CONTRIBUTION
Research writing & capture-system architecture
CONTEXT
Team Shiny · IIT Patna
STATUS
Software demo · research in progress
ReactTypeScriptExpresstRPCDrizzle ORMHuman-in-the-loop AIESP32-S3 CAM
WORKFLOW / DRISHTI
  1. 01Capture + intake
  2. 02Assisted evaluation
  3. 03Human moderation
  4. 04Audit trail

Make every mark traceable.

01THE PROBLEM

What needed solving?

Answer evaluation needs readable capture, correct script-to-record mapping, and a way to review differences between human and AI assessments.

02MY CONTRIBUTION

Where I came in.

My work covers the research manuscript, literature review, cost modelling, and an ESP32-S3 CAM capture-rig architecture. The optical design considers lens calibration, lighting, and capture quality before evaluation.

Team repository

03KEY DECISIONS

The thinking behind it.

01

Keep humans in the decision loop

Assisted evaluation is separate from final judgement. Moderation makes disagreements visible and preserves a review trail.

02

Start with capture quality

Unreadable input undermines every later step. The capture design considers optical budget, calibration, and dual-LED illumination.

03

Separate modelling from validation

Analytical estimates guide the prototype but require empirical evaluation before operational claims.

04OUTCOME & NEXT STEPS

Built. Learned.
Still improving.

Research manuscript in preparation, with a documented capture architecture and evaluation workflow. Team and upstream credit are preserved.

Where it stands today

The software works with uploaded or browser-captured answer sheets. The repository documents the physical camera provider as a planned, uncertified integration; a separate team post describes a hackathon capture-system build. The manuscript is in preparation, and measured grading accuracy and hardware performance remain to be validated.

What comes next

Evaluate capture quality and review outcomes using an appropriate, permissioned test dataset.