Post-Doctor Visit Summary.

The problem

Learners and teachers spend hours adapting generic material to the right level. A concept has to be found across textbooks, videos, notes, AI chat, and worksheets, then reshaped by hand into explanations, examples, and practice — and repeated whenever a student is confused or a new subject or grade comes up.

The highest-severity pain points are consistent: generic resources are not matched to the learner's level or prior knowledge, teachers lose time differentiating lessons, learners don't know which explanation to trust, and general-purpose AI outputs can be inaccurate, too advanced, or not pedagogically sound.

The solution

Adaptive Education is a Custom Adaptive Lesson Generator for both learners and educators. A student or teacher enters a topic — or uploads their own lesson material — and selects subject, grade or learning level, goal, and output type, then receives a structured, level-appropriate lesson with explanations, examples, checks for understanding, and next steps.

Rather than generic chat, the design is pedagogy-first: separate learner and teacher workflows, level-appropriate scaffolding, teacher-editable outputs, and transparent source grounding when material is uploaded. It works across math, science, humanities, languages, health sciences, exam prep, and professional skills.

How it works

A master prompt casts the model as an instructional designer and adaptive tutor. It first infers the learner's level, subject, objective, time available, and user mode, asking clarifying questions when essential inputs are missing. The output is structured into a learning objective, prerequisite check, concise lesson, examples and analogies, practice questions with feedback, common misconceptions, next steps, and teacher notes for educator modes.

The approach pairs a frontier multimodal LLM with retrieval-augmented generation (RAG) over uploaded or approved materials — chunked by concept, embedded, retrieved per generation, and shown as source references. The build uses GPT-4.1-mini for initial testing to control token cost, with Image-gen-2 for visuals, plus safety filters, logging, and educator feedback loops. Guardrails cover accuracy, age appropriateness, and high-risk domains.

Who it's for

The primary personas are a student who needs a topic explained at the right level and pace, and a teacher or tutor who needs to quickly create differentiated lessons, activities, and checks for understanding. Secondary users include parents, homeschool educators, adult learners, instructional designers, and school administrators.

The customer base is B2B2C — schools, districts, universities, tutoring centers, and training organizations sponsoring access — alongside a B2C channel for individual learners and independent teachers. Revenue is planned as freemium for individuals with paid subscriptions, institutional licensing, and team plans.

Why it matters

The market is large and growing. Grand View Research estimates the global AI-in-education market at USD 5.88B in 2024, projected to reach USD 32.27B by 2030 at a 31.2% CAGR, within a broader EdTech market valued at USD 187.01B in 2025.

Currently a 0-to-1 concept at early MVP stage, the plan is a closed pilot of 20–50 learners and 5–10 educators across several subjects and levels before wider beta. MVP quality targets include 90%+ on required structure, 85%+ on level alignment, 90%+ on safety and privacy checks, and zero critical safety failures — reflecting that in education, accuracy and safety are non-negotiable.

At a glance

Project

Post-Doctor Visit Summary

Built by

Iqbal Jaffer

One-liner

AI adaptive-learning platform that turns any material into personalized explanations, visuals, and quizzes, with separate learner and teacher workflows.