Personalized Teacher Lesson Preparation Assistant

The Problem: Personalization Challenges in Lesson Preparation

Teachers often face pressure in daily lesson planning, needing to adapt to different textbook versions (e.g., PEP, Jiangsu Education Edition), varied student learning situations across classes, and individual teaching styles. Traditional methods rely on manual writing, are time-consuming, and struggle to achieve personalization, leading to uneven teaching effectiveness, especially across multi-grade and multi-subject scenarios.

How the Skill Works: Core Process of Intelligent Lesson Preparation

The Personalized Teacher Lesson Preparation Assistant simplifies lesson prep through structured data and automated templates, with the following steps:

• Parameter Input: Teachers provide 6 key parameters, including grade, subject, textbook version, chapter, class learning data, and teaching style—for example, inputting "Grade 3 Math, PEP Chapter 5, class average score 85, interactive style".
• Data Reading: The skill automatically reads textbook_versions.json to locate the textbook, knowledge_graph.json to extract the chapter's knowledge graph (e.g., prerequisite dependencies), and curriculum_standards.json to align with curriculum standards.
• Lesson Plan Generation: Using the references/prompts/prep_template.md template, it outputs three versions: basic (focusing on knowledge coverage), advanced (with expanded content), and innovative (incorporating interdisciplinary elements).
• Extended Capabilities:
• Class Learning Dashboard: Generates visual reports based on student error and performance data to assist teaching decisions.
• Error Variant Generation: Automatically collects common class errors and generates variant exercises to reinforce weak areas.
• Master Templates: Supports creating private templates and accessing shared template libraries, such as importing exemplary teacher lesson plans.
• Emergency Prep: In time-sensitive situations, generates a simplified emergency lesson plan in 1 minute to ensure teaching continuity.
• Teaching Reflection: Analyzes prep and classroom data to automatically produce weekly reflection reports, optimizing future teaching.

A core advantage is the cross-version knowledge graph, covering 8 textbook versions × 15 subjects × all grade levels (elementary to high school), with a curriculum coding intermediate layer for version interoperability—for example, mapping the same knowledge point across different versions to unified standards.

Application Boundaries and Considerations

• Coverage Scope: Supports 15 subjects, but some like physics (starting from grade 8) and chemistry (starting from grade 9) have grade restrictions; high school includes modern media arts and AI-related subjects.
• Data Dependency: Relies on textbook metadata (3,272 entries) and curriculum knowledge points (330), requiring 3-5 days to update for new versions or subjects, with initial coverage gaps possible.
• Usage Limits: Generated lesson plans are template-based and parameter-dependent; teachers must adjust them for actual class conditions (e.g., student level fluctuations). Emergency plans may sacrifice deep personalization.
• Technical Prerequisites: Requires AI model support for parameter parsing and data reading. Teachers should ensure accurate inputs (e.g., textbook version codes) to optimize output.

This skill aims to free lesson prep from repetitive tasks, but final teaching decisions and implementation remain under teacher professional judgment.