The most important idea when it comes to AI & education:
WHOEVER DOES THE THINKING GETS THE LEARNING
More:
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AI is coming—thick & fast.
LLM’s like ChatGPT are outperforming humans at an ever-increasing range of tasks, their adoption is spreading quicker than any technology before, and they are the least intelligent they will ever be.
However, just because AI is powerful doesn’t mean that it’s good for learning.
Setting aside issues related to accuracy, bias, and privacy—the current generation of LLMs are optimised for helping users SOLVE PROBLEMS, not helping users GET BETTER AT SOLVING PROBLEMS.
Labels play an important role in education. They help students access targeted support and guide us in responding to particular needs.
However, they can also have unintended downsides—they are a double-edged sword.
Labels can influence expectations.
Teachers who know a student’s diagnosis can—often unconsciously—lower their expectations, asking fewer complex questions or offering less peer collaboration.
*Diagnostic overshadowing* can thwart inclusive teaching.
What's useful for teachers to know:
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Diagnostic overshadowing is a term originating in medical contexts (and introduced to me by @Barker_J).
It describes the phenomenon where doctors inadvertently place too much emphasis on a patient's diagnosis, overshadowing other significant health concerns.
For example, a patient diagnosed with depression might have their physical symptoms—like fatigue or headaches—mistakenly attributed to their mental health condition, potentially overlooking a critical underlying physical illness requiring separate treatment.