Learning in the age of AI
Blog
Writing on inquiry and learning: what it takes to truly understand when answers come instantly.
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Getting an answer is not the same as learning
In an age when AI delivers finished answers instantly, we look at why reaching the right answer and building real understanding can come apart.
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Why fluent explanations feel like understanding
How can an explanation that reads well diverge from what you actually understand? Through processing fluency, lecture-style explanations, and the illusion of explanatory depth, we examine the metacognitive problem of the AI era.
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The more common information becomes, the more expensive judgment gets
In an age when AI produces answers and explanations effortlessly, we look at why the ability to judge what to believe, what to choose, and how it connects matters more than the sheer amount of information.
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In the age of AI-made answers, what should humans learn?
Even in an age when search and memory can be handed over to AI, why do we still need knowledge inside our own heads, and how should the goals of education and assessment change?
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Why even skeptical users get lost in AI chat
Even users who refuse to take AI answers at face value, and set out to verify them carefully, lose the original context and their place in the verification as questions branch. We look at why.
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Thought is a tree. Why is chat a log?
Questions branch and loop back, yet AI chat stacks everything in time order. We look at why a conversation that is fully saved can still lose the structure of thinking.
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Effort that builds thought, and effort that blocks it
The difficulty learning needs and the burden an interface adds are not the same thing. Through cognitive load and cognitive offloading, we ask what AI should take over and what it should leave to people.
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Why we built Thinket
How losing the context of a question the deeper a conversation with AI went led to Thinket's design: branching, paths, and wrap-up.
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