Teaching Review Planner

Addressing Common Pitfalls in Review Sessions

Traditional review classes often fall into the traps of "averaging out efforts," "repetitive lecturing," and "last-minute cramming." xiaozhi-teach-review-planner addresses these issues by shifting the focus from "re-pumping knowledge" to "dimensional elevation based on real data." It does not rely on LLMs to generate content out of thin air; instead, it uses structured data to help students build higher-dimensional connections on top of existing knowledge.

Core Capabilities and Key Steps

• Data-Driven Identification of Key Points: Strongly relies on the chapter structure of curriculum standards, chapter relationship judgments provided by teachers, and historical wrong-question data extracted by student-analyzer. It generates a list of key and difficult points through a three-dimensional identification process to avoid uniform effort.
• Visualized Knowledge Graphs: Uses a four-tier structure to outline chapter flow, marking review priorities and difficulty levels in Markdown to make the knowledge network clear and visible.
• Rhythmic Review Strategies: Based on the principles of distributed practice, spaced repetition, and active recall, it provides a 3-7-14 day phased strategy (covering unit, midterm, final, and pre-exam sprints) and designs review activities such as active recall, concept mapping, cross-subject connections, and error archives.
• Pre-exam Support and Effect Tracking: Offers three layers of pre-exam support focused on regular routines and review pacing. After the review phase, it writes effect data back to student-analyzer in an aggregated format, creating a data loop.

Scope and Considerations

• Not a Psychological Diagnostic Tool: Pre-exam psychological support is limited to learning strategies (e.g., routines, pacing) and does not perform psychological diagnosis or treatment. Severe anxiety must be referred to professional psychological counseling.
• No Score Promises: It only provides objective advice on "what to review" based on existing data, making no promises regarding exam scores, rankings, or admission results.
• Privacy and Compliance: Strictly enforces field-level sensitive information protection, prohibits the use of anxiety-inducing rhetoric, and strictly forbids exposing student names and specific scores in public review materials and data write-backs.