learning-system

Automated study planning and progress tracking is handled by orchestrating a four-stage learning workflow that unifies data storage and generates spaced-review schedules across arbitrary domains.

Spaced repetition scheduling works by integrating knowledge metadata with learning stages to persist progress in JSON data models, generating deterministic review cycles that maximize retention.

This learning system supports individual learners, academic cohorts, and corporate training by applying cross-domain cognitive-learning workflows that adapt to any knowledge area and content format.

The best way to start is by defining a knowledge point, then launching the four-stage workflow to automatically create a study plan, generate topics, and begin Stage 1.

No external databases are required; the study planning approach uses transparent frontmatter-driven discovery and local data models like progress.json, knowledge-list.json, and topic markdown files for deterministic persistence.

A four-stage learning workflow coordinates knowledge metadata, active progress tracking, and automated review cycles, whereas standard note-taking lacks adaptive planning and data-driven gap analysis.