# Phase 3 Plan: Learner Memory

**Status:** Ready for execution
**Phase Goal:** Convert graded answer evidence into structured learner memory.

## Requirements Covered

- MEM-01: System stores learner profile with role, stack, timeline, and
  preferences.
- MEM-02: System stores concept mastery states with evidence.
- MEM-03: System stores recurring misconceptions with supporting answers.
- MEM-04: System stores intervention history and review schedule.
- MEM-05: Temporary session context does not become durable memory without
  evidence.

## Tasks

### 1. Add learner memory package

- Create `internal/learnermemory`.
- Define profile, concept mastery, misconception, intervention, review schedule,
  and snapshot types.
- Add in-memory store with clear interface.

### 2. Add memory extraction workflow output

- Extend `workflows.StubRunner.ExtractLearningMemory` to return evidenced
  memory update candidates from a graded answer.
- Ensure candidates without evidence are not applied.

### 3. Wire diagnostic answers to memory

- Inject learner memory service into interview service.
- After grading an answer, extract and apply memory updates.
- Keep diagnostic session records and learner memory records separate.

### 4. Add memory read endpoint

- Add `GET /api/v1/learners/{userID}/memory`.
- Return learner profile, mastery, misconceptions, interventions, and review
  schedule.

### 5. Add tests and verification

- Test memory applies only evidenced updates.
- Test diagnostic answer submission updates learner memory.
- Test memory HTTP read endpoint.
- Run Go tests, OpenSpec validation, and line-count check.

## Out of Scope

- Persistent database.
- Memory ranking/decay.
- Progression readiness map.
- Frontend UI.

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*Plan created: 2026-04-26*