面向 Claude Code 的循证学习引擎——第一性原理课程、带凭据的自由回忆验证、FSRS 调度记忆和可探索工件。学习任何内容;保留所学。
learning-science 开源项目推荐(74 个)
learning-science 话题在 GitHub 上没有填写简介,这里按 Star 数汇总最相关的热门仓库。
热门项目 前 20
按 Star 数排序展示前 20 个 · GitHub 共 74 个仓库165 evidence-grounded AI skills for teachers, school leaders and EdTech builders—pedagogy, learning science, curriculum, assessment and regeneration. Claude, Codex and Hermes.
🌳 AI驱动的技能树,用于终身人类学习。从K-12到职业与社会智力的30+技能,基于认知科学。|人类养成记:AI 驱动的终身学习技能树
The data layer for smarter educational AI. Integrate trusted instructional content and research directly into your AI-powered tools — improving precision, relevance, and instructional alignment.
Evaluation for AI outputs against trusted educational rubrics. Measure and improve content quality with research-backed rubrics — ensuring rigor, reliability, and alignment to classroom needs.
Universal AI agent skill that turns any material into learning-science-based self-study courses with retrieval practice, spaced repetition, and tutor mode.
🎓 A teaching skill for AI coding agents — teaches from your level, then verifies real learning by restatement. Adapted from Matt Pocock's teach (MIT).
AI-agnostic skill for writing BrainLifts: structured context docs that turn research into DOK knowledge trees, insights, and spiky POVs.
把硬核长内容拆成obsidian的思维导图 · 通用 AI 提示词框架(Claude/ChatGPT/Gemini)
Research-backed concepts for educators teaching AI literacy. How to preserve human thinking, judgment, and agency while using AI productively.
Stay sharp while AI does the work: a Claude Code plugin for judgment, understanding and transfer — one-way door gates, change briefs, and lessons that come back when you need them.
Local-first mastery tutor for AI agents with guided HTML lessons, hands-on practice, transfer checks, spaced review, and learner-owned progress.
An Agent Skill for AI teaching that reasons about the learner's current understanding and chooses the next teaching move, grounded in cognitive and learning science.
Distraction-free AI learning. Stay with one concept at a time using clear explanations, useful analogies, visual structure, and active recall.
Glossy interactive React lab for human and agent memory systems.
An Intelligent Lecturing Assistant
Agent Skill that quizzes you on its own answers — one multiple-choice question after each substantive reply, graded and explained. Ambient retrieval practice against AI deskilling. Instruction-only: no scripts, no network, no data sent. MIT.
Evidence-driven Agent Skill for systematic learning: retrieval, discrimination, transfer, performance, and long-term continuity.
伏羲框架 · 万物皆可学 — Everything Can Be Learned|十阶时间线 × 证据分级 × 开源工具链
Eleven skill dimensions per word, FSRS-6 scheduling per skill, and no mastery until you can produce the word in writing. Each mistake is sorted into one of seven error categories, and no word enters practice before it's taught. Runs in the browser from one HTML file, with no server, no account and no network requests
关于这个话题
- 清单取自 GitHub 话题页并按 Star 数排序,每次展示前 20 个项目;原始页面见 https://github.com/topics/learning-science。
- 该话题在 GitHub 上共有 74 个仓库, 本站收录的是其中最热门的 20 个,不是全量列表。
- Star / Fork 每日自动刷新一次,实时数据以 GitHub 为准。
- 当前 20 个项目累计 3373 Star,覆盖 4 种主要语言,数据更新于 2026-11-07。
- 中文介绍来自 GitHub 官方话题说明(github/explore)的机器翻译,可点「查看原文」对照;译文缺失时会先展示原文。