AI,learning and cognitive load
It’s sometimes good to reflect on previous learnings from the literature to contextualize what we see and read today.
A recent article from the always interesting Zoe Scaman led me to look back at a paper from the late 1980s on learning and cognitive load (see link in comments). Sweller’s Cognitive Load Theory distinguishes "germane cognitive load" (beneficial for learning) from "extraneous load" (wasted effort). Getting the balance right is important. Learning requiring effort enhances retention, while making learning easier often impairs it. Reducing cognitive load too much leads to atrophy.
This is important as organizations (and indiviudals) seek to integrate AI more and more.
There is a real risk of erosion of domain expertise and critical thinking (the result of practice and experience) if there is too much reliance on AI to reduce cognitive effort and avoid hard thinking. Scaman warns of this impact " a quiet erosion of capability, person by person, decision by decision. And it compounds. Every month of outsourced thinking makes it harder to do the thinking yourself. The muscles weaken. The instincts dull....capabilities are built through struggle. Through thousands of hours of wrestling with ambiguity. Through the compound learning that comes from doing the hard thinking yourself. Through sitting with a brief for three days and letting your subconscious chew on it before you touch a keyboard. Through the boredom and frustration and false starts that eventually crystallise into something genuinely new."
Ericsson’s deliberate practice theory argues that expertise requires effortful practice..... precisely the struggle that unthinking use of AI circumvents.
The full article is very thought provoking.
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Cognitive Load During Problem Solving: Effects on Learning
The role of deliberate practice in the acquisition of expert performance
