AI reinforces established patterns reflected in its training data.
Humans challenge established patterns based on unique intuition.
This difference is at the core of understanding AIβs strengths and limitations, and developing AI Fluency.
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β The AI Time Paradox
π€ Ever rush to save time with AI only to end up frustrated with mediocre results? You're not alone. Here's why this happens (and how to fix it)...
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β¨ Every week, subscribers receive:
π Clear, jargon-free prompt engineering strategies
π Curated AI updates that matter to educators
π Practical ways to prepare students for an AI world
There are no βrightβ or βwrongβ answers from AI.
There are only βusefulβ and βuselessβ responses.
The job of the human user is to maximize the chances of useful AI responses, by:
1. Knowing what you want
2. Articulating what you want (prompting)
3. Iterating until satisfied
4. π€ Prompting needs to be thought of not as single prompts, but as an iterative process going back and forth between Human and AI. In this setting, what you instruct and at what point in the process is more important than how you instruct.
Quick insights on the prompt engineering debate:
1. β Yes, prompting matters. Run similar prompts with key word differences, or include/remove phrases like chain-of-thought βexplain your reasoning,β and you can see the difference.
3. π Human vision reigns supreme. If you donβt know what you want, you wonβt be able to effectively prompt the AI.
πΊ(Interstingly, this is a lot like what I do as a band conductor.)
Got a generic response from AI when you needed something creative?
(But you thought it was a good response because it cited sources?)
Turns out, your AI might be TOO smart. Here's the counterintuitive fix... π§΅
#AIinEducation#EdTech#teachertwitter
You need:
π Online AI for:
β’ Current research
β’ Citations
β’ Facts
β¨ Offline AI for:
β’ Original lessons
β’ Creative solutions
β’ Custom strategies
When you need innovation over information, tell your AI to know less.
It's counterintuitive, but it works every time.