The article explores the distinction between human learning and AI behavior. While AI can produce outputs resembling learning, such as essays or problem-solving, it lacks the personal relevance and consequences that characterize human learning. Learning, traditionally measured with internal constructs like 'understanding,' is flawed, as these cannot be objectively observed. True learning involves adaptive behaviors shaped by experiences, feedback, and consequences, setting human learning apart from machine output.
AI reveals how poorly we define learning by mimicking the outputs we use to measure it.
Only behavior shaped by relevance and consequence can distinguish human learning from machine output.
But the resemblance ends at the surface.
Humans do.
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