What can we really learn from the case of Calculators in mathematics to the case of GenAI in education?
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Here is the original image of the Embedded Processes model from: Cowan et al. "The relation between attention and memory." Annual Review of Psychology 75.1 (2024): 183-214.

9/ The Embedded Processes model requires more knowledge and integration to grasp. Yet it is parsimonious and dynamic: we can follow one thread, from initial representation through binding and deactivation.

8/Further more:
-The “WM limitations” stem from the limited attention capacity (3-5 items), and time of LTM activation (<1min).

- Information can enter aLTM without attention (e.g. priming effects), or consciously either top-down (executive function) or bottom-up (prediction error) processes

7/ As attention fades or shifts, the new construct remains briefly in the activated (but unattended) LTM, before becoming inactive. Manipulation and the time in aLTM influence later consolidation and storage chances.

6/ Cowan Identifies an activated subset of LTM (aLTM), a small part of which is in the focus of attention (FoA).
Information is selected for and may be binded, and combined with activated representations while in the FoA.

🧠Memory is working without a "Working Memory

4/ The model can explain “cognitive load” - when input exceeds WM capacity. But it doesn't address the Attention-WM or the WM-LTM interactions: How do “items” move from temporary storage to LTM representation?

3/ This simple model highlights three cognitive functions as three “Stores”: attention, Working memory, and long-term memory (LTM) operating like a “conveyor belt”: selecting, processing and then storing information.

Let's follow a new pattern through the phases of memory: Encoding, Consolidation, Storage, and Retrieval.
Notice how the new pattern evolves from a "live" experience to newly-formed, then stored connections, ready for reactivation upon retrieval.
And... what happens next?

Let's follow a new pattern through the phases of memory: Encoding, Consolidation, Storage, and Retrieval.
Notice how the new pattern evolves from a "live" experience to newly-formed, then stored connections, ready for reactivation upon retrieval.

And... what happens next?

This model highlights two key features of neuroplasticity:
1) Existing nodes & connections can be inactive or reactivated.
2) Activating new patterns can sometimes forge new connections (but generally not new nodes).

Cognitive neuroscience uses simplified network models like this one to demonstrate how learning & memory might work at the network level.
Nodes represent neurons, lines their connections (synapses), and the patterns - our knowledge.

'Neuroplasticity' is everywhere, but what do we really mean when we talk about the ever-changing brain?
Let's dive deeper than the buzzword and explore the evidence with a model.