Cadre

Capstone 17 — Personal AI Tutor (Adaptive, Multimodal, with Memory)

Khanmigo (Khan Academy), Duolingo Max, Google LearnLM / Gemini for Education, Quizlet Q-Chat, and Synthesis Tutor all shipped adaptive multimodal tutoring at scale in 2026. The common shape is a Socratic policy (never just dump the answer), a learner model that updates after every interaction (Bayesian knowledge tracing style), voice + text + photo-math input, curriculum graph retrieval, spaced-repetition scheduling, and hard safety filters for age-appropriate content. The capstone is to ship a subject-specific tutor (K-12 algebra or intro Python), run a two-week efficacy study with 10 learners, and pass a content-safety audit.

Capstone 30 hr Python (backend, learner model), TypeScript (web app), SQL (curriculum graph via Postgres + Neo4j)

Prerequisites: Phase 5 (NLP), Phase 6 (speech), Phase 11 (LLM engineering), Phase 12 (multimodal), Phase 14 (agents), Phase 17 (infrastructure), Phase 18 (safety)

#Problem

Adaptive tutoring used to be an ed-tech research niche. By 2026 it is a consumer product. Khanmigo is deployed across most US school districts. Duolingo Max hit tens of millions of MAUs. Google's LearnLM / Gemini for Education powers tutoring in Google Classroom. Quizlet Q-Chat sits alongside flashcards. Synthesis Tutor hit virality with tutor-for-curious-kids. The common elements: multimodal input (type, speak, photograph equations), Socratic pedagogy (ask first, explain later), a learner model that updates after each interaction, and strict age-appropriate safety.

You will build one of these for a specific cohort. The measurement bar is an actual efficacy study: pre-test and post-test scores over two weeks with 10 learners. The voice loop must feel natural (capstone 03 sub-stack). The memory must be privacy-respecting. The safety filter must pass COPPA-aware red-team for K-12.

#Problem

Keep going. The rest of this lesson is one step away.