Learning needs a plan for the revolution we can already glimpse

African American male wearing virtual reality headset

Take a room of economists and ask them about the startling prediction that 47% of US jobs are at high risk from automation, and they won’t react with surprise. Instead, there will be debate about the economy’s ability to generate new jobs, or an argument about the difference between a skill being automated and a job (for example, nurses may no longer administer drugs in 15 years time, but they will still offer empathy and warmth).

And, in speeches from the likes of the Chief Economist of the Bank of England and former US Treasury Secretary Larry Summers, onto books with evocative names like The Rise of the Robots, The Future of the Professions, and The Fourth Industrial Revolution, the concern that we will see more and more jobs replaced by smart algorithms has entered the intellectual mainstream.

However, as we think about how to alleviate the negative effects of living and working alongside increasingly intelligent machines, I see little evidence that Educationalists are having conversations as creative as the Economists.

For example, a great deal of intellectual hard graft is currently being spent on the idea of providing an unconditional basic income to all citizens – that is, a baseline income to all citizens that, amongst other things, may offer some alleviation to the income inequality that robots may bring.

Although voters in Switzerland rejected implementing this in a recent referendum, the very fact that there was a public debate – and vote – indicates the type of conversation that we also need to be having about the future of learning.

We hope that an ambitious new piece of research that we’re doing with colleagues from Nesta, and with Professor Michael Osborne and others, will make a contribution to this.

Chart course for future learningAnd, there is an urgent need for this conversation to escape the confines of academia to influence the expectations that parents have for their children. For example, I recently took a taxi ride where I asked the driver what hopes he had for his son.

“For him to be a London Black Cab driver,” he replied, “… so that he’ll enjoy a job for life.”  (Soon after this, I read in the paper about the closure of London’s largest centre for black cab drivers to learn “the knowledge” – a test of a London taxi driver’s skills at navigating without a sat nav. And we still have self-driving cars to come!)

A pre-requisite for this conversation – and the beginnings of a sensible strategy for learning in fifteen years’ time – would begin with some analysis of how the trend towards automation is likely to play out.

It would also identify the other trends that will shape the future demand for knowledge and skills – for instance, an ageing society, globalization, the Internet of Things, the rise of the sharing economy and social movements like wellbeing at work.

Then it would harness informed predictions of how these trends will affect the jobs that exist now, which would lead to more detail on what really matters to learning – the type, and mix, of knowledge and skills that learning needs to provide in the future.

This is roughly what our project will do, and the more research minded amongst you will enjoy this explanation by the lead researcher we’re working with at Nesta.

Our hope is that this programme will provide some of the needed detail, and a sketch of some of the plausible answers, that policy makers and education providers like Pearson will be wise to consider.

For example, by raising questions like:

None of these questions are easy, and there are few tried-and-tested answers. But when I began advising the UK government in the 1990s we also didn’t know how to effect school improvement at scale, and now, from the Punjab to London, it’s visible what can be achieved.

I recognize the challenge today is maybe even more demanding, so let’s respond by taking the conversation wider, the serious questions about learning deeper, and the practical action of innovation much more seriously.

We will share the stages of this research as it becomes available, using a variety of techniques like user personas, expert blogs and the results of a machine learning algorithm to allow, we hope, a collective predicting and imagining of what the future of jobs might look like. It’s a vital topic, and one where the learning component has been, to date, too neglected.

 

This article originally appeared on Pearson.com and was re-published here with permission.