Thanks for writing this, it clarifies a lot, and I'm really pondering how Mollick's insights on persistent expertise directly shape the practicall development of truly disciplinary-specific AI literacy within the K-12 framework, which feels like such a crucial next step.
In my talks with students and others, I have been using a ruler to get at this…it can be used for so many purposes, from Geometry to art to biology, general and specific tool for drawing and measuring. It can also be used to thwack students on the hand to punish them.
So too, with LLMs, though as a language tech it comes with a great of confusing baggage.
This is also 100% relevant for corporate learning and development. In the workplace you have to really clarify what quality looks like in your domain, and train people how to review and edit based on those principles.
This is a change away from the ambiguous waffle full of business jargon that we've come to accept as the gold standard. AI is really good at hiding errors, context misalignment, and over confident statements beneath its corporate tone. This should force us away from valuing business-speak (now easily achieved) to valuing domain specific criteria.
Thanks for writing this, it clarifies a lot, and I'm really pondering how Mollick's insights on persistent expertise directly shape the practicall development of truly disciplinary-specific AI literacy within the K-12 framework, which feels like such a crucial next step.
In my talks with students and others, I have been using a ruler to get at this…it can be used for so many purposes, from Geometry to art to biology, general and specific tool for drawing and measuring. It can also be used to thwack students on the hand to punish them.
So too, with LLMs, though as a language tech it comes with a great of confusing baggage.
Doesn't matter. Companies are run by their CFOs.
This is also 100% relevant for corporate learning and development. In the workplace you have to really clarify what quality looks like in your domain, and train people how to review and edit based on those principles.
This is a change away from the ambiguous waffle full of business jargon that we've come to accept as the gold standard. AI is really good at hiding errors, context misalignment, and over confident statements beneath its corporate tone. This should force us away from valuing business-speak (now easily achieved) to valuing domain specific criteria.