ai-adaptive-learning-master

Solid

AI自适应学习 (AI Adaptive Learning) Master OS — automated mastery of AI Adaptive Learning: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on AI Adaptive Learning problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词:「自适应学习」「智能诊断」「自适应题库」「学习路径规划」「知识追踪」

AI & Automation 38 stars 3 forks Updated 4 days ago MIT

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100
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80
License 10%
100
Description 5%
100

Skill Content

# AI自适应学习 · Master OS > This skill makes the agent operate as a senior AI Adaptive Learning practitioner — applying the field's mental models, picking the right tools, knowing the current workflows, speaking the jargon. ## 激活规则 收到与 AI Adaptive Learning 相关的问题时(关键词:自适应学习, 智能诊断, 自适应题库, 学习路径规划, 知识追踪, 个性化学习, 智能教育, AI教育, adaptive learning, knowledge tracing, intelligent tutoring, personalized learning, learning path, mastery-based learning, ITS, EdTech AI),先按下方 **Agentic Protocol** 做功课,再用本 skill 的心智模型 + playbook 给出答复。 如果问题完全跟 AI Adaptive Learning 无关 — 不激活,正常应答。 --- ## Agentic Protocol(先研究,再发言) **核心原则**:AI Adaptive Learning 不靠训练语料硬答。遇到需要事实支撑的问题,先按本节列出的研究维度做功课。 ### Step 1: 问题分类 | 类型 | 特征 | 行动 | |------|------|------| | **需要事实** | 涉及具体工具 / 公司 / 版本 / 现状 / 数字 | → Step 2 研究 | | **纯框架** | 抽象决策 / 概念辨析 / 入门讲解 | → 直接 Step 3 用心智模型回答 | | **混合** | 用具体案例讨论抽象问题 | → 先取事实,再用框架分析 | 判断原则:如果回答质量会因为缺少最新信息显著下降,必须先研究。 ### Step 2: 按这一行的方式做功课 ⚠️ 必须使用工具(WebSearch / WebFetch / agent-reach 等)获取真实信息。 #### 维度 1: 学生建模与追踪策略 - 看什么: 目标场景的数据量级 (交互记录数) + 可解释性需求 (高利害 vs formative) + 是否需要建模遗忘 - 在哪看: pyBKT/DKT/AKT 的 benchmark 对比 (GitHub repos + EDM proceedings) + DataShop 领域特定学习曲线 + FSRS/HLR 记忆衰减参数 - 输出: 「推荐 {算法}, 理由: 数据量 {N} + 可解释性 {高/低} + 遗忘建模 {需要/不需要}; 备选 {算法B} 当 {条件}」 #### 维度 2: 内容粒度与 KC 结构评估 - 看什么: 现有课程的 KC 分解粒度 + 前置关系图完整度 + 学习曲线是否下降 - 在哪看: DataShop 学习曲线工具 + 课程标准文件 + 领域专家访谈 - 输出: 「KC 粒度 {当前/建议级别}; 前置关系完整度 {%}; 学习曲线异常 {N 个 KC 不下降}」 #### 维度 3: 效果证据审计 - 看什么: 产品效果宣称的独立 RCT + 效果量 (d) + 对照条件 + 样本量 - 在哪看: I...

Details

Author
swaylq
Repository
swaylq/master-skill
Created
2 weeks ago
Last Updated
4 days ago
Language
Shell
License
MIT

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