By Kate Pasterfield
In the last episode of our Learning Science Unpacked series, Paul Howard-Jones and I venture into the future of learning, exploring how it might look in the years to come.
We explore technological and philosophical trends in learning and consider their impact, before concluding that the best future for workplace learning lies in understanding engagement, building of knowledge and consolidation (EBCs).
What can we expect from the future of learning?
Discussions about the future can sometimes seem a bit farfetched – reminiscent of the dreamlike ideas in Jules Verne. However, many of the technological developments explored below are much more advanced than you might expect – and their potential impact on the future of learning could come sooner than you think!
Moving things with your mind
The possibility of evolving the human race by enhancing our abilities with technological implants may seem as improbable as the Terminator. Surely, we won’t seek to boost our brains with artificial components and become cyborgs?
I hate to break it to you… but we might. Brain Machine Interfaces (BMIs) are currently in research and development, with possible implications for the future of learning.
One example is the implantable Link from Elon Musk’s company Neuralink. The motivation driving BMI development is to give people control over their computerised environment without the need for external buttons, a mouse, or devices.
There are obvious applications in the medical field: for example, enabling those with debilitating conditions like paralysis.
But some companies are developing these advanced technologies specifically to solve workplace challenges. In Japan, a sharp drop in skilled construction workers is predicted in the coming decade as the population ages. Companies like Meltin are building cyborg technology to bridge the gap.
One current challenge with BMIs is not that we can’t receive signals from the brain; rather that we have limited understanding of how to interpret them.
Yes and no answers are relatively easy to ascertain, but anything more complex relating to concepts, or translating reliably meaningful information is beyond the grasp of today’s science.
Moreover, this computational calculation needs to operate in real time, adding a further layer of challenge. These factors mean that everyday use of BMIs is still a little way off – but we might begin to see their application in around 10-20 years.
Improving focus with electricity
Wearable technologies like the Foc.us work by delivering an electrical pulse to the brain, an approach that successfully enhances cognitive function.
The US military is already using the method to train staff before they go into demanding or high security situations where they need to identify anomalies and know how to respond. They discovered that the rate at which staff learned went up considerably whilst wearing a transcranial stimulation device – or ‘magic hat’. So could ‘magic hats’ be a commonplace feature of the future of learning?
Other research is showing that children struggling to improve their mathematics abilities are finding that their learning improves after using the device.
But should we be concerned?
Certainly, there are unknowns, like what are the long-term impacts? Might unregulated home use lead to brain damage? And does increasing one cognitive capacity diminish another? Further, wearing a device to enhance learning does not help people develop better strategies for learning itself, limiting agency. Who should benefit from this advantage, and how can it be equitable for all?
Keep an eye on transcranial stimulation. Consumer devices are being used by gamers to increase their performance, and although it’s unlikely to become a workplace staple, for specific jobs, it’s already proving to have application.
Got a smartwatch? Thanks to the myriad of data-gathering wearables out there, from smart garments, to apps, headbands, and intelligent eyewear, we’ve become accustomed to analysing our biofeedback. What was the reserve of the GP check up – heart rates, breathing patterns, blood oxygen levels – have now become as much a part of our daily self-monitoring as stepping on the scales.
Market available EEG headsets like Muse and Mendi measure electrical brain signals that tell us how relaxed or attentive we are, supporting focus and enhancing sleep.
Neurofeedback can help people learn to self-regulate their brain rhythms. In one study, conservatoire musicians received training on attention and relaxation, using neurofeedback. The results showed that their performance improved versus a control group, despite being in a high stress situation.
Anyone involved in workplace interactions would also see the immediate relevance of another finding, showing that groups of people who work better together and produce more output are more correlated in their physiological responses, measured by skin conductance and temperature.
This has fascinating implications for personal learning, performance, and training delivery.
Biofeedback can help people enter a state of peripheral alertness when performing complex tasks, or it may support trainers to identify which parts of their session are engaging their audience.
And, importantly, biofeedback is already here.
Finding rather than knowing: a Googlable knowledge economy...
Hypothetically speaking, could we see a day where we simply inject or ingest knowledge?
In fact, attaining knowledge may not be a target for future learning – it’s knowing where to find it.
The effect of ‘Googling’ for answers has encouraged us to turn to computers when we need information, rather than rely on our own recall of facts.
Research by Betsy Sparrow, shows people forget things that they believe will be available to find externally. They are better able to remember where an item has been stored rather than the identity of the item itself, suggesting that the processes of human memory are adaptive for efficiency.
What does this mean for training itself? At Sponge, I notice training now often signposts people to resources they may need, rather than attempting to train people deeply in all possible scenarios, especially relevant to the arena of ethics and compliance.
A fundamental shake-up?
Is learning fit to meet the demands and complexities of the world we find ourselves in?
Elon Musk didn’t think so. He set up his own school to prepare his kids for a highly ambiguous world, based on the philosophy of equipping children to think, collaborate, make judgements, and solve problems – arguably skills most companies consider a top priority.
Reimagining the learning experience may become a more urgent priority as we contend with rapid shifts, and one way this may affect learning is by acknowledging an emergent ‘foraging learner’.
Key takeaways: facing the frontier is all about understanding how we learn
It can be unnerving to reflect on the speed at which technology is moving, and it is worth remembering that our biology is not evolving at the same rate.
Our physical hardware will remain relevant no matter what the technological advances are, so we need to understand how it works to optimise learning experiences that are fit for the world we work in.
By leaning into the neuroscience, deeply understanding the processes by which people learn and committing ourselves to creating experiences tailored to meet their needs, we would do a service to people and workplaces everywhere.