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Design a sequence where students compare AI's handling of the same question across disciplines, developing a mental model of where AI is reliable vs. distorting based on knowledge type.
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Design a sequence where students compare AI's handling of the same question across disciplines, developing a mental model of where AI is reliable vs. distorting based on knowledge type.
中文简介正在准备中。当前显示原文说明。
以OpenDesign内部结构为示例，使用tech-sharing模板制作技术演讲HTML幻灯片，整理内容结构、依据和演讲者备注，形成面向工程师的讲解资料。
使用深紫色渐变制作支持企业 AI 引入决策的 HTML 幻灯片，为管理层、IT 和安全团队梳理风险管理、投资回报与部署计划。
以OpenDesign现场演示为主题，制作带演讲者备注和动画的HTML幻灯片，并整理从结构、讲解内容到视觉呈现的各个部分。
使用终端风格模板制作 HTML 幻灯片，说明 OpenDesign 中自带 API 密钥的模型选型，帮助技术团队判断成本、质量、连接方式及模型分工。
After any AI-generated explanation, require the learner to identify one place it could be wrong, one thing to check, and one source to consult. Builds epistemic vigilance — treats AI output as a claim to evaluate, not truth to absorb.
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Design a Funhouse Mirror activity where students use their own domain expertise to detect AI distortions, omissions, and overconfidence. Use when students know a subject well enough to evaluate AI claims about it.
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Design a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.
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Design a multi-round questioning sequence for interrogating AI chatbot answers, tracking how responses shift and distinguishing genuine updates from sycophantic capitulation. Use when teaching students to probe AI critically.
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Design a structured protocol for auditing AI-generated text against Ennis's six CT standards. Use when students need to critically evaluate AI output in any subject.
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Map which elements of an assignment benefit from AI assistance vs. which AI use undermines. Use when redesigning tasks for AI-age classrooms or setting defensible AI use policies for specific assignments.
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Design a fact-checking protocol for AI-generated text, extending SIFT with AI-specific adaptations for hallucination detection. Use when students need to verify AI claims and citations.
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Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.
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Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.
中文简介正在准备中。当前显示原文说明。
Design a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.
中文简介正在准备中。当前显示原文说明。
Design a Funhouse Mirror activity where students use their own domain expertise to detect AI distortions, omissions, and overconfidence. Use when students know a subject well enough to evaluate AI claims about it.
中文简介正在准备中。当前显示原文说明。
After any AI-generated explanation, require the learner to identify one place it could be wrong, one thing to check, and one source to consult. Builds epistemic vigilance — treats AI output as a claim to evaluate, not truth to absorb.
中文简介正在准备中。当前显示原文说明。
Map which elements of an assignment benefit from AI assistance vs. which AI use undermines. Use when redesigning tasks for AI-age classrooms or setting defensible AI use policies for specific assignments.
中文简介正在准备中。当前显示原文说明。
Design a structured protocol for auditing AI-generated text against Ennis's six CT standards. Use when students need to critically evaluate AI output in any subject.
中文简介正在准备中。当前显示原文说明。
Design a multi-round questioning sequence for interrogating AI chatbot answers, tracking how responses shift and distinguishing genuine updates from sycophantic capitulation. Use when teaching students to probe AI critically.
中文简介正在准备中。当前显示原文说明。
Design a fact-checking protocol for AI-generated text, extending SIFT with AI-specific adaptations for hallucination detection. Use when students need to verify AI claims and citations.
中文简介正在准备中。当前显示原文说明。
Use this skill when staff already use AI assistants, coding tools or AI features and the company needs rules people will follow, and asks "write our AI usage policy", "can staff use ChatGPT", "generative AI policy", "what data can we put into AI tools", "approved AI tools list", "rules for AI agents and connectors" or "AI literacy for staff". Finds out what people actually use, sets a short list of approved tools with a data traffic light, rules for checking outputs and for agents that act, a fast route to request a new tool, and writes a two-page rule set, the approved tools list and rollout notes.
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「新任職員向けに生成 AI 基礎研修を作りたい」「SD 研修資料を短時間で組み立てたい」 「自分自身のリテラシー棚卸しをしたい」「新人に AI の使い分けを 30 分で説明したい」 「ハルシネーションの見抜き方を教えたい」場面で使う、大学職員向け AI リテラシー入門スキル。 プロンプトの基本構造・ハルシネーション検出・サービス選定を、30〜60 分のコア研修に そのまま流用できる章立てで提供する。
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