# MasteryMath

> Agent-facing counterpart to the [human project page](/projects/masterymath/).

## Record metadata

- Record: 018
- Slug: masterymath
- Domain: Education
- Domain code: EDU
- Type: iPad app
- Status: Prototype
- Period: 2026
- Portfolio role: Applied prototype
- Publication state: Private source; public case study planned
- Case-study readiness: Artifact needs refinement
- Compendium edition: 0.4

## Summary

A local-first iPad mathematics prototype built around deterministic practice and inspectable mastery evidence.

## Overview

MasteryMath is the point where an abstract learning model had to survive contact with an actual interface. The project asks what MasteryGraph looks like when a learner is holding an iPad and needs the next problem, not a theory of the next problem.

A SwiftUI iPad MVP with deterministic problem generation and checking, local progress storage, learner practice flows, and a parent-facing dashboard.

Purpose: Test whether the MasteryGraph methodology can produce a calmer, more accountable learning tool.

## The problem behind the project

The project turns an abstract mastery model into a testable artifact and avoids using an LLM where exact mathematical checking is more reliable.

Mathematics also provides a useful boundary for AI. A model may help design the system or explain a concept, but exact problem generation and answer checking can remain deterministic, local, and inspectable.

Learners and parents are the intended users. Any future use with children requires careful privacy, accessibility, and pedagogical review.

## How it took shape

SwiftUI, SwiftData, deterministic math generation and validation, local-first storage, a structured mastery model, and smoke-tested app flows.

The SwiftUI and SwiftData MVP includes deterministic practice, validation, local progress storage, learner flows, and a parent dashboard. It has built and passed initial simulator smoke testing, making it a working prototype rather than a presentation-only concept.

Josiah defined the learning philosophy, feature boundary, local-first posture, and acceptance criteria while directing agent-assisted implementation.

A working build and core smoke tests exist. There is not yet evidence of sustained learner use or improved outcomes.

## What the project means now

What remains unproven is educational effect. Curriculum breadth, diagnostic validity, interface quality, and sustained learner use require real testing. The prototype's value is that it makes those questions concrete and exposes which parts of the broader methodology have actually been implemented.

The curriculum, interface, assessment validity, and content breadth need refinement before external use.

AI can help build the learning system while deterministic code remains the right authority for exact answers.

Refine the artifact, document the implemented methodology, and conduct a small consented usability test.

## Publication and interpretation notes

- Current classification: Prototype
- Portfolio readiness: Artifact needs refinement
- Publication boundary: Private source; public case study planned

## Additional agent context

Keep implementation evidence separate from educational efficacy. The app works as a prototype; its learning impact is untested.

## Related project records

- [MasteryGraph](/projects/masterygraph/llm/) — A learning methodology that treats knowledge as an inspectable graph of claims, prerequisites, evidence, and mastery.

## Navigation

- [Complete project index](/projects/llm/)
- [Human version of this record](/projects/masterymath/)
- [About Josiah's working method](/about/llm/)
- [Agent discovery map](/llms.txt)