This is the fourth in a series of AI in Education specials, where I speak to the world’s leading experts about how AI is changing teaching and learning.
Dylan Kane's piece in Five, Twelve, Thirteen entitled "Alpha Schools Secret Sauce" discusses how Alpha School's motivation strategies may play a more important role in their success than AI. He would be a good interview for your series.
This is very interesting, but it would be good to have more details of Alpha’s actual curriculum. This adaptive learning approach clearly works where specific skills develoment is essential.... maths, reading, writing etc. It’s less clear how it would work for traditional subjects, Literature, History etc. whose ‘guts’ lie well beyond skills development. Alpha students seem to gain very high SAT scores, not to mention “crush academics” (Austin Scholar)…hopefully their Guides are ok! :) Still, it would be great to know more of the academic flavour there.
I think Carl's idea of learning being either deterministic or ethereal ignores a more chaotic or probabilistic possibility.
In one sense, weather patterns are entirely deterministic and in another, very real sense, weather is still a huge challenge to predict when it matters most. The number of variables and interactions between them expands the more we know and understand. I think learning is more like weather than something strictly deterministic, with the added complication that we can and do try to influence learning more directly than we (intentionally) influence local weather.
> track the optimal spacing interval for every student on every concept, predict when forgetting is about to tip from productive to destructive, and schedule the next retrieval opportunity accordingly.
Absolutely, but the bar is high for software to enter this space.
Look at [Anki](http://ankisrs.net), which is the most powerful flashcard app on the market -- free on Mac and Windows, payware on iOS. It will take a long time for an AI app to catch up. Remnote is also powerful and in the game.
There are many ways in which reflective practice can go wrong. I developed the concept of [productive practice](https://luccogzest.substack.com/p/the-cupa-framework-for-evaluating) which combines cognitive science (of deliberate practice and memory) and educational psychology (test-enhanced learning) and other psychology. I've expounded on it in 4 chapters of
This article presents a fascinating debate on the role of AI in education! It’s interesting how Carl Hendrick views AI as a tool for personalizing learning and improving educational outcomes through data, while Adam Boxer remains skeptical based on past tech failures. I’m curious to see how this conversation evolves, especially in light of the rapid advancements in AI technology. It definitely raises important questions about the future of teaching and learning!
Dylan Kane's piece in Five, Twelve, Thirteen entitled "Alpha Schools Secret Sauce" discusses how Alpha School's motivation strategies may play a more important role in their success than AI. He would be a good interview for your series.
This is very interesting, but it would be good to have more details of Alpha’s actual curriculum. This adaptive learning approach clearly works where specific skills develoment is essential.... maths, reading, writing etc. It’s less clear how it would work for traditional subjects, Literature, History etc. whose ‘guts’ lie well beyond skills development. Alpha students seem to gain very high SAT scores, not to mention “crush academics” (Austin Scholar)…hopefully their Guides are ok! :) Still, it would be great to know more of the academic flavour there.
More on this soon with a future guest who has just got back from a visit!
Good to hear! Thanks.
I think Carl's idea of learning being either deterministic or ethereal ignores a more chaotic or probabilistic possibility.
In one sense, weather patterns are entirely deterministic and in another, very real sense, weather is still a huge challenge to predict when it matters most. The number of variables and interactions between them expands the more we know and understand. I think learning is more like weather than something strictly deterministic, with the added complication that we can and do try to influence learning more directly than we (intentionally) influence local weather.
> track the optimal spacing interval for every student on every concept, predict when forgetting is about to tip from productive to destructive, and schedule the next retrieval opportunity accordingly.
Absolutely, but the bar is high for software to enter this space.
Look at [Anki](http://ankisrs.net), which is the most powerful flashcard app on the market -- free on Mac and Windows, payware on iOS. It will take a long time for an AI app to catch up. Remnote is also powerful and in the game.
There are many ways in which reflective practice can go wrong. I developed the concept of [productive practice](https://luccogzest.substack.com/p/the-cupa-framework-for-evaluating) which combines cognitive science (of deliberate practice and memory) and educational psychology (test-enhanced learning) and other psychology. I've expounded on it in 4 chapters of
* [_Cognitive Productivity with macOS: 7 Principles for Getting Smarter with Knowledge_](https://leanpub.com/cognitive-productivity-macos)
and
* [_Cognitive Productivity: Using Knowledge to Become Profoundly Effective_](https://leanpub.com/cognitiveproductivity/).
It's time retrieval practice be subsumed by the concept of productive practice...
This article presents a fascinating debate on the role of AI in education! It’s interesting how Carl Hendrick views AI as a tool for personalizing learning and improving educational outcomes through data, while Adam Boxer remains skeptical based on past tech failures. I’m curious to see how this conversation evolves, especially in light of the rapid advancements in AI technology. It definitely raises important questions about the future of teaching and learning!