The noise around the use of “artificial intelligence” as a replacement for high-quality instruction from an expert seems to grow louder each day.
But that’s all it is.
Noise.
There is no evidence that AI is anywhere near the level of proficiency routinely ascribed to it. More often than not, it is being used as a way to avoid confronting the actual challenges of school improvement.
We know an astounding amount about how children learn mathematics, one of the most difficult biologically secondary things a person will ever do. But we cannot click a button and become expert teachers of mathematics. That takes time. We have to make mistakes. We need to think. We need to reflect. As Henry Rollins says, knowledge without mileage is bullshit. And there is something important in that.
Ultimately, when educators chase silver bullets, of any kind, the only ones who lose out are the children.
Over the coming months, I’ll be collecting my thoughts on these five papers and the state of mathematics teaching between now and 2036. The following papers represent some of the best of what we know about mathematics teaching, and some of the best work we have on analysing the efficacy of particular approaches. There is no snake oil here.
Start by engaging with these papers, and I’ll be back on 15th July with the first in a series of accompanying audio blogs.
If you want to keep up with the whole series, you can support the podcast at Tier 3 at www.ko-fi.com/tdape or by subscribing on Spotify.
Until next time, thanks for listening.
Five Papers That Will Shape The Future of Mathematics Teaching
A Meta-analysis of the Worked Examples Effect on Mathematics Performance - Barbieri et al.
A Meta-Analysis of the Efficacy of Teaching Mathematics With Concrete Manipulatives - Carbonneau et al.
On the learning benefits of confidence-weighted testing - Sparck et al.
Principles for the design of a fully-resourced, coherent, research-informed school mathematics curriculum - Foster et al.
Primary Maths In Action
