Case Study
TalimAI
AI-planned computing curriculum for grades 5–12, with a teacher-in-the-loop gate
01Problem
Computing teachers for grades 5–12 spend hours planning differentiated daily lessons, and off-the-shelf content ignores what a specific class actually did yesterday. TalimAI generates a structured, grade-calibrated computing day for every class each night — built on what that class covered the day before.
02Constraints
- Nothing AI-generated can reach a student unreviewed — a real classroom needs a human gate.
- A “day” is not one quiz: it has to span warm-up, core skill blocks, an applied project, an optional challenge, and a wrap-up, calibrated to grade level.
- Institutions, not individuals — the system has to model multiple campuses, teachers, and classes with real permissions.
03Approach
- Generate a full 4–6 hour day nightly per class — warm-up → core skill blocks → applied project → optional challenge → wrap-up — calibrated to grade level and to the previous day’s coverage.
- Support 9 distinct task formats (multiple choice / short answer, real code exercises, debug challenges, spot-the-bug / ordering, AI-concept design projects), not a single quiz type.
- Close the loop: objective tasks auto-grade instantly; open-ended work gets AI-assisted scoring a teacher can review and override; results feed the next day’s plan before it is generated.
The core product decision is a human gate: the AI plans, the teacher decides.
- Every generated day is a draft — a teacher reviews and publishes it before any student sees it. Nothing reaches a student unreviewed.
- Gamification layers on top of real academic content, never instead of it: XP, streaks with streak-freezes, deliberately class-scoped (not global) leaderboards, and a persistent per-student portfolio that carries artifacts forward day to day.
- Teachers can upload and author their own content alongside the AI curriculum, so the tool augments them rather than replacing them.
- Institution admins see real, itemized cost accounting for AI-generation spend — explicitly not a black-box number.
04Architecture
Multi-tenant from the schema up — multi-campus, multi-teacher, multi-class, with granular per-teacher permissions — wrapped around a nightly AI generation pipeline and a grading loop that feeds the next day’s plan. (This was a self-directed build without a public repo, so the write-up stays at the level the product itself makes visible.)
- ▸Multi-tenant data model — campuses, teachers, classes, per-teacher permissions
- ▸Nightly AI generation pipeline — one calibrated day per class
- ▸Grading loop — auto-grade plus AI-assisted scoring with teacher override
- ▸Cost accounting for AI-generation spend, surfaced to institution admins
05Decisions & Tradeoffs
Made every generated day a draft behind a teacher publish-gate.
↳Adds a manual review step (and no fully-unattended flow on lower tiers), but nothing unreviewed can reach a student.
Scoped leaderboards to the class, not globally.
↳Less viral competition, but keeps motivation healthy and comparisons fair within a real cohort.
Surfaced itemized AI-generation cost to institution admins.
↳Exposes spend that some products hide, but makes the tool’s economics honest and defensible to a budget owner.
Gated access behind an application/review instead of self-serve signup, with tiers differentiated by manual vs. unattended nightly generation and roster ceiling.
↳Slower top-of-funnel, but fits an institution-sold product where onboarding is high-touch.
06Outcome
Shipped
- A nightly-generated, grade-calibrated computing curriculum for grades 5–12 across 9 task formats.
- A teacher review-and-publish gate, plus teacher-authored content alongside the AI curriculum.
- An auto plus AI-assisted grading loop that informs the next day’s plan, gamification layered on real content, and itemized AI-cost accounting for admins.
- Tiered plans (Starter / Growth / Enterprise) differentiated by manual vs. unattended nightly generation and roster ceiling.