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SIH Buddyby Ganeev Singh
Dev
All problem statements
SIH26101Proceed with cautionacceptance 2/5

Develop an AI enabled learning platform that identifies competency gaps, recommends personalized training through integration with the iGOT Karmayogi ecosystem, and capable of generating Quizzes and Multiple choice questions (MCQs) from uploaded learning materials to strengthen capacity building in India's Official Statistical System.

MoSPI · Smart Education · Software

Another learning platform where both distinguishing features — the government platform integration and the competency framework — are unavailable, leaving a quiz generator many teams will also show; only take it if distractor quality and question traceability become the entire pitch.

Data: nssta.gov.in, mospi.gov.in

What it actually is

Statistical officials need to keep learning as the tools change, and the government training platform has plenty of courses, but nobody can tell which ones are relevant to their particular job. The ask is a system that profiles an official's competencies, finds the gaps, and recommends courses from that platform — and separately, generates quiz questions automatically from any material a trainer uploads.

What to build

Two capabilities of very different weight. The assessment and recommendation half builds a competency profile from role, qualifications, experience and prior training, evaluates it against a competency framework for official statistics spanning the four domains the statement enumerates — statistical, technical, digital governance and behavioural — identifies gaps and recommends learning against them. The question generation half takes an uploaded document, presentation or recording and produces multiple choice questions with distractors, correct answers and explanations, and this is the part that will actually work end to end, so the question is whether you can make it good rather than merely functional — meaning questions that test comprehension rather than surface recall, distractors that are genuinely plausible, and every question traceable to the passage that supports it.

Smallest thing that wins the room

Upload a statistical methodology document and generate a question set where the distractors are plausible enough that a judge has to think, with each question linked back to the passage it came from.

How crowded this one gets

A guess, projected from the 2025 statements — the last year where both the submission counts and the winners were published.

Moderate150–360 teams expectedroughly 1 in 130–301 wins it

Quieter than 29% of the 226 · #161 of 226 by expected field

A normal-sized field. Your idea has to be good, not miraculous.

Why: central ministry statements sat below the average.

This is a guess, not a fact

Nobody has published 2026’s numbers yet. This is an analysed estimate from last year’s pattern, so please do not take it as the truth — check the live counter on the SIH portal before you decide anything. The range covers the middle half of likely outcomes, so one statement in two lands outside it. Entry closes at 500 ideas per statement, so no range goes past that — a statement that reaches the cap fills and shuts rather than drawing an unlimited crowd. The model reads only three things a team can see before choosing — software or hardware, the theme, and what kind of body posted it — and those explain about a quarter of the variation in last year’s field sizes (R² 0.25 on held-out statements). Trust the band more than the number, and the ordering more than either. It cannot see how good your idea is, which is the part that actually decides it.

The scores

The number is the shorthand. The line under it is the reason.

What you will be writing

  • LLM question generation with distractor quality control
  • passage-grounded question traceability
  • competency framework mapping for official statistics
  • semantic course matching to identified gaps
  • role-based learner and administrator dashboards
  • SSO and government API integration patterns
  • Capacity building and training
  • Official statistics
  • Learning platforms

Prior art to read before you start

automated assessment generation from documents · competency gap analysis and course recommendation · learning analytics dashboards

Analysed by Claude Opus. Every score above is a judgment call with its reasoning attached — kindly cross-check this against the official statement on the SIH portal before your team commits to it.