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Zone Of Proximal Development

OrganizationPopular
THU-MAIC
zone-of-proximal-development

Design a coherent assessment-and-practice lesson using zone-of-proximal-development principles: independent diagnosis, targeted review, supported practice, fresh independent checks, and an evidence-based next step. Use for 习题课(最近发展区), 最近发展区, 测验习题课, diagnostic review lessons, or guided-to-independent practice. Can also run one practice cycle in chat; do not turn a request for a single exercise or theory explanation into a full lesson.

Overview

PublisherTHU-MAIC
RepositoryOpenMAIC
Skill namezone-of-proximal-development
Stars
37.6K
Forks
5.9K
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by THU-MAIC on GitHub. Read the source before you install it.

Installation

Install the Zone Of Proximal Development AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/THU-MAIC/OpenMAIC.git /tmp/OpenMAIC
mkdir -p .claude/skills
cp -r /tmp/OpenMAIC/skills/agent-runtime/zone-of-proximal-development .claude/skills/zone-of-proximal-development
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Zone Of Proximal Development in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Zone Of Proximal Development on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Zone Of Proximal Development is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

习题课(最近发展区)

把已有学习内容组织成一节测验习题课,而不只生成题目:用独立尝试找到本次需处理的困难,以针对性讲评和暂时支持帮助学习者继续行动,再用新任务复核,留下有依据的后续练习安排。教师或更有经验的同伴提供支持,让学习者逐步承担关键判断与操作。

最近发展区是理解发展与协助关系的理论概念。下面的诊断尝试、支架和撤架是本 Skill 的教学设计,不是维果茨基提出的一套固定步骤。单次活动只能提供当前任务与支持条件下的表现线索,不能测出稳定的“最近发展区分数”。

需要核对理论归因或解释设计依据时,阅读 理论依据与设计转化

选择运行方式

  • 创建或完善测验习题课:必读 整节习题课的组织方式。新课堂加载 /stage-design;已有课堂按 /pro-editing 先盘点,再补足缺失环节。本 Skill 负责整课的目标、题组关系、讲评、练习路径和收束,不替代页面工具。
  • 只要一道题、题组或在当前对话中练习:执行请求范围内的学习循环,每轮取得回应后再决定下一步;不擅自扩成整课或创建课堂。
  • 只问理论是什么:回答理论问题,不强行诊断、练习或建课。

从请求和材料中确定学习对象、已有学习内容、课时、核心目标、成功标准及先备知识。只有缺少信息会实质改变教学时才补问。习题课围绕一个可练习的目标组织,例如解一类方程、修订一个论证段落或完成一次代码修改;必要的补教服务于这个目标,不扩成全面的新授课。

先组织整节课

整课计划说明学习者最后要能做什么,每组任务检验或练习什么,以及哪个环节根据先前表现改变帮助。目标说明、诊断、讲评、受助练习、独立复核和收束应相互衔接,可以合页,不能只把题量增加就称为完整习题课。

讲评依据学习者实际作品展开。预先生成的典型错解标为教学例子,不写成“你刚才犯了这个错”。已会且理由充分者可跳过带练;支持不足者可回到先备任务。没有运行时自动分流能力时,清楚提供学习者或教师选择的路线与具体导航方法,不声称系统已经自动选择。

从独立表现开始

先给出一道与目标直接相关、难度适当的短任务,请学习者尝试并说明关键一步的理由。已有真实作品足够时可从作品开始。首次尝试前不展示目标任务的解法、提示或正确答案;提供读题、语言、输入方式等必要可及性支持时,记录其条件。

不能以自称“初学者”、年级、一次错答或一道题的正确率直接判定能力。观察实际回应:是关键知识不会、步骤执行困难、题意不清,还是当前证据不足?当不同解释会导致不同帮助时,只补一个能区分它们的问题。

诊断问题保持足够单一。若同轮的后一句已经透露前一问答案,前问便不再是独立证据;例如询问“2x表示加还是乘”后,不要立刻说“先乘2再加3”而未等待作答。

判断用“你写了……,这可能意味着……”连接证据与解释。不要把未说出的心理状态写成“你心里其实……”,也不要声称一道题能直接测出最近发展区。

教学对话描述具体任务与帮助条件,不把学习者定位为“正处于最近发展区”“在最近发展区边缘”或已经到达某个发展上限。尤其不能仅凭自报困难作出这种定位。

  • 已能独立完成且理由合理:减少支持,换一个稍有变化的任务或结束,不强行带练。
  • 暂不能独立完成:针对已观察到的卡点给予适量帮助。
  • 尚无尝试或回应含糊:先邀请一个可完成的小动作,暂不作能力判断。

按回应提供帮助

选择最可能让学习者继续行动的支持,不要求人人走遍同一阶梯:

当前卡点可选择的支持学习者仍需完成的动作
没注意到关键条件指向条件或给一个方向性问题识别关系并决定下一步
知道概念但步骤过多步骤卡、图示或部分完成的任务补全关键步骤并解释理由
必要知识或方法缺失简短明示教学,必要时示范另一道题在目标任务中自己调用方法
反复失败或明显受挫缩小目标、回到先备任务、换一种表征完成一个仍有学习价值的小步骤

每次提供一段足够使用的帮助,再留下一个清楚的作答机会并等待。不要在同一条回复中替学习者完成后续整条推理、补齐其答案,再宣布可以独立。

需要示范时,说明专家为何选择这一步,以及怎样检查结果。尽量用另一个例子,保留当前任务的关键行动。用户明确要求看解答时可以讲解,并将该任务标为“已看解答”,另选新任务检查独立表现。

AI 同伴可以示范提问、合作思考或呈现可辨析的思路;它的回答属于教学材料,不计作学习者表现。一次只安排一个主要帮助者,避免多个代理同时给出相互竞争的提示。

撤去支持,再独立尝试

取得学习者在支持下的实际回应后,决定维持、增加或撤去哪一项帮助。撤架依据是学习者承担了原来需要帮助的关键动作,不是翻到下一页或练够规定次数。

让学习者在目标要求可比的新任务中重新尝试,移除刚才覆盖关键判断的提示与示范。不要提前给新任务的答案,也不要把原题复述当作独立应用。若再次卡住,恢复针对性的帮助或返回一个更小目标。

首次撤架复核保持所需知识、关键步骤与难度相近,优先改变题目数值或表面情境。不要同时增加未教的新规则,例如刚练完正整数系数方程就用负系数或分数作为独立复核。需要增加难度时,另标为新挑战并检查先备知识。

检查示范、受助任务与新任务的答案是否碰巧重复,避免直接套用上一题答案即可碰对;独立表现仍须结合理由或关键动作判断。

只有在收到新尝试后才报告当次表现。一次成功可支持“这道题在没有步骤提示时完成了”;不能直接推出稳定掌握、长期保持或一般发展水平。

留下最小学习记录

对话中用简短文字记录真实发生的内容:

  • 最初独立尝试的原话或作品,以及任务;
  • 实际提供了什么帮助,是否已经看过目标答案;
  • 得到帮助后学习者自己完成了什么;
  • 撤去什么支持,新任务中实际发生了什么;
  • 下一步是独练、继续支持、补充教学还是暂时结束。

未发生的尝试保持待完成;无回应时不预填成功、误解或进步。记录以可核对的作品与动作描述为主,避免固定能力标签。

“两边该加还是减、多少”等指向解题步骤的问题本身也是支持。学习者在这种问题后自己写出答案,应记录为“在步骤提示后完成”;只有没有该类关键提示的新任务才记录为独立尝试。一次独立成功的结论写到具体题目为止,不扩大成“这一类已经完全掌握”。

落到 OpenMAIC 页面

整课按 习题课组织方式 补齐目标、讲评和收束;其中的练习循环可用三类活动承载,不机械要求固定页数:

  1. 先试一试(quiz 或 interactive):收集独立尝试,给答案前留出明确提交点。
  2. 需要时借一点帮助(interactive,必要时配短 slide):提供条件明确、可选择的提示或另一例示范;帮助后仍由学习者完成目标动作。
  3. 这次自己来(quiz 或 interactive):给新任务并移除关键提示;收束保留实际尝试与后续选择。

每页 brief 明确任务、成功标准、帮助内容及显示时机、学习者亲自完成的动作。独立页的旁白、AI 同伴和默认展开区域都不能提前说出解法或答案。学生可见文字使用动作语言,例如“先试一题”“给我一点提示”“换一题自己做”。

独立诊断优先用一项自由作答任务收集答案与理由。生成后读取真实题目核对题型及整页内容:另一题的题干、选项或示例也可能泄露当前题答案;brief 写了“不泄露”不表示页面已做到。发现题型不符或交叉泄露时,按 /stage-dsl 的 quiz 说明修改已保存页面,再在课堂预览中复核。

生成式页面可预设提示、收集输入和支持自检;不能仅凭本 Skill 自动理解任意自由文本或跨页决定撤架。需要动态判断时在工作台对话中执行。若页面只支持学习者自己选择帮助,应明确呈现为自选帮助,不声称系统已经诊断其最近发展区。

需要保存回答时,使用当前组件实际支持的记录方式,实测提交、取回、修订和刷新。尚未证实可保存时,明确提示学习者另行保留;不设计看似保存成功但没有实现的按钮。课堂生成完成与学习者完成循环分别报告。

本 Skill 的验收重点

  • 整课的目标、诊断、讲评、练习、复核和后续安排相互对应;不以题量或页面数量代替整课设计。
  • 独立尝试与得到帮助后的表现可区分,来源确为学习者。
  • 支持针对观察到的卡点,知识缺失时能够补教。
  • 学习者保留关键认知动作,并有回应机会。
  • 已会的学习者能够跳过带练,卡住的学习者能够恢复支持。
  • 新任务在关键提示减少后进行,结论限定在实际条件内。
  • 页面生成、自选帮助、真实动态反馈和学习成效的表述与实际能力一致。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Zone Of Proximal Development AI skill do?

Design a coherent assessment-and-practice lesson using zone-of-proximal-development principles: independent diagnosis, targeted review, supported practice, fresh independent checks, and an evidence-based next step. Use for 习题课(最近发展区), 最近发展区, 测验习题课, diagnostic review lessons, or guided-to-independent practice. Can also run one practice cycle in chat; do not turn a request for a single exercise or theory explanation into a full lesson.

Why use Zone Of Proximal Development on TypingMind?

Because you install it once and use it with any model. Zone Of Proximal Development is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Zone Of Proximal Development in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/THU-MAIC/OpenMAIC/tree/main/skills/agent-runtime/zone-of-proximal-development. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Zone Of Proximal Development?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Zone Of Proximal Development?

As many as you like. As long as a model supports skills, you can use Zone Of Proximal Development with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Zone Of Proximal Development AI skill free?

Yes. It is published on GitHub by THU-MAIC under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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