How PrepGraph learns.

You don't advance until you've actually shown you can do it.

PrepGraph is mastery-based, not exposure-based. You advance when you've demonstrated you can apply a concept — not because you sat through a video. No padding stats with easy questions you already know.

We make you pull knowledge out, not just put it in.

Sessions begin with cold retrieval of concepts from earlier work. Mid-session checks. End-of-session mixed recall. Delayed retrieval across days and weeks. The EEF rates retrieval practice as one of the highest-impact, lowest-cost interventions in education research.

Concepts come back at the right time. If you start forgetting, they come back sooner.

Blocked practice early, mixed practice in the middle, time-bound interleaved practice at exam stage. Review timing adapts to each student's forgetting pattern, not a one-size schedule. The Nature 2025 systematic review of intelligent tutoring systems calls this the single most-overlooked feature of generic AI-tutoring tools.

We never explain more than your working memory can hold.

Length and complexity of explanations adapt to where the student is. Confused → switch to a worked example. Intermediate → guide with questions. Advanced → challenge with variation. Long explanations to struggling students are a known failure mode of generic AI tutors — we design against it.

You learn what kind of student you are.

After sessions, short reflection prompts: what was hardest? Was it the concept, the math, or how the question was phrased? Your dashboard tracks not just "% correct" but consistency, confidence, and the kinds of mistakes you tend to make. EEF rates metacognition as a high-impact, low-cost approach.

The tutor will not hand you the answer.

PrepGraph asks you to attempt before it explains. It nudges with hints when you're stuck, then with guiding questions, and only explains in full at the end. Brookings' analysis of GenAI tutoring identifies this "teach, not tell" pattern as the single highest-leverage design principle.

A wrong answer is data. We learn from it.

When you get a question wrong, PrepGraph identifies what kind of mistake it was — a concept gap, a slip, a misreading — and chooses the next problem to address that specific issue. Over weeks, your weak-areas list visibly shrinks. This is the difference between a question bank and a learning system.

In schools, AI is the layer beneath the teacher.

Teachers see class misconception heatmaps and get specific intervention recommendations. Parents see weekly evidence-based reports. Students get a Socratic tutor. None of this replaces a human teacher — Brookings' GenAI tutoring research is explicit that the strongest evidence comes from hybrid models where AI handles personalisation and humans handle judgment.