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Project Summary: Intelligent Learning Ecosystem

1. Current State & Strategic Goal​

The platform currently operates with two powerful but disconnected systems: a Spaced Repetition System (SRS) using a stock FSRS algorithm for individual learning, and a Knowledge Graph that categorizes content. The strategic goal of this project is to bridge these two systems, transforming the platform into a cohesive, intelligent ecosystem that learns from the collective behavior of its community to enhance the learning experience for every individual.

2. The Phased Implementation Plan​

This project will be executed in five distinct, sequential phases. Each phase builds upon the last, ensuring a logical and robust development process from foundational data enhancements to the final user-facing features.

  • Phase 1: Configuration & Foundational Data Layer: The essential first step. We will prepare the application by defining all necessary configuration parameters and enhancing our database schema to capture the detailed "before and after" state of every user review. This rich data is the fuel for all subsequent intelligence.
  • Phase 2: FSRS Parameter Optimization: A critical prerequisite. We will build a background service that analyzes each actor's learning history to compute their unique, personalized FSRS parameters. This allows us to understand each actor's individual learning patterns and is the cornerstone of the "Learner Reputation" model.
  • Phase 3: Community-Derived Complexity (CDC) Algorithm: The core analytical engine. In this phase, we will build the background service that aggregates the millions of review logs from the entire community. Using the personalized data from Phase 2, it will calculate a robust, objective "difficulty score" (cdc_score) for every concept in the knowledge graph.
  • Phase 4: Complexity-Adjusted Initial Scheduling (CAIS) Algorithm: The primary user-facing benefit. We will integrate the cdc_score from Phase 3 directly into the SRS. This will solve the "cold-start" problem by providing users with smarter, more appropriate initial review intervals for new material based on its known community difficulty.
  • Phase 5: Deployment, Monitoring & Future Iteration: The final step to ensure a production-ready system. This phase covers the database migration strategy, setting up monitoring for the new background jobs, and creating a backlog for future enhancements based on the research.

3. Desired End State​

Upon completion of all five phases, the platform will have evolved from a passive content delivery tool into an active learning partner. It will possess a deep, data-driven understanding of its own content's difficulty and will leverage the collective intelligence of its user base to create a more efficient, personalized, and effective learning journey for everyone.