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Generative AI tools are revolutionizing how we access information, make decisions, and form opinions, but they also introduce new risks. In an age where AI can generate confident-sounding claims, charts, and arguments at scale, critical thinking is more essential than ever.
Beyond the Obvious: How to Think in the Age of Generative AI
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该课程共有4个模块
In this module, students will explore why not all patterns in data are meaningful. They’ll learn how randomness creates illusions that our minds instinctively misinterpret, how correlation can be confused with causation, and why spurious patterns often emerge when data is selected, framed, or misunderstood.
涵盖的内容
10个视频11篇阅读材料5个作业1个应用程序项目1个讨论话题1个插件
Even perfect-looking data can mislead if it’s built on biased foundations. In this module, students will explore how flawed sampling, missing or invisible data, and selective reporting distort what we think we know, and how to critically examine not just the source of the data, but the way it’s framed, filtered, and used.
涵盖的内容
9个视频7篇阅读材料5个作业1个讨论话题1个插件
Numbers don’t lie, but they can mislead when context is missing. In this module, students will explore how aggregation, base rate neglect, and selective framing distort our understanding of data. They’ll learn to question the comparisons they’re shown and think carefully about the ones they’re not.
涵盖的内容
8个视频13篇阅读材料5个作业1个讨论话题1个插件
In the age of GenAI, large language models can become powerful tools to sharpen rather than replace our judgment. This module shows students how to use GenAI tools to test assumptions, simulate disagreement, surface alternative perspectives, and reflect more critically on their own reasoning.
涵盖的内容
10个视频9篇阅读材料6个作业1个讨论话题1个插件
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