AI 驱动的智能教学资源引擎——让分层教学触手可及。智能体自动完成感知解析、教学规划、内容生成与差异化适配，为教师精准产出多层级教学资源。 An AI teaching-resource engine that makes tiered instruction practical. Agents handle perception, planning, content generation, and differentiated adaptation — producing multi-level teaching resources precisely matched to each class.
接收教师输入（文本/语音/OCR），解析核心意图，提取年级、学科、课标、时长等教学约束条件，输出标准化结构参数。Takes teacher input (text/voice/OCR), parses intent, and extracts grade, subject, curriculum-standard, and duration constraints into structured parameters.
结合教育学规则与课标要求，拆解教学目标、规划执行路径、选择适配工具，输出详细执行方案。Combines pedagogy rules with curriculum standards to decompose goals, plan execution, and select tools.
依据执行方案，基于教育微调 LLM 生成结构化教学资源——教案、作业、课件、教师指南等。An education-fine-tuned LLM generates structured resources: lesson plans, exercises, courseware, teacher guides.
自动对齐各学科课程标准Auto-aligns to subject curriculum standards
基于循证的差异化教学方法Evidence-based differentiated methods
针对小组的个性化学习支持Personalized support for student groups
AI 赋能 K12 课堂教学，让每位学生获得个性化学习体验。AI-enabled K12 teaching that gives every student a personalized experience.
智能机器人助手根据学情自动生成基础、标准、拓展三级资源。Auto-generates foundation, standard, and extension resources based on each cohort's readiness.
整合教材、课件、作业等资源，一站式教学资源管理；连接教材/课件与知识库，实现高效、个性化备课。Unifies textbooks, courseware, and assignments — connecting them to the knowledge base for efficient, personalized lesson prep.
团队成员 Sophia Liu 赴北京参加 AMD 开发者峰会展示，OpenClawEdu 荣获大赛冠军，现场介绍了项目背景、系统架构与教育应用场景。 Team member Sophia Liu presented OpenClawEdu in Beijing after it won champion at the AMD AI Developer Summit, covering the project's background, architecture, and education use cases.
