Learning is a natural human function. It’s how we progress, grow and develop, especially in our early years. However, in our culture we have tended to see the end of university as the end of our formal education. Discover here how AI and machine learning can nurture lifelong learning and help us transition into more fulfilling and meaningful jobs.

When in a job, we transition into learning that’s beneficial to the organisations we work for and relevant to our existing skills and experience – and that is often legislation and process driven, rather than inspired and inspiring.

This misses out on an enormous opportunity, as learning throughout our lives is a real gift and allows us to give meaning to our daily activities and progression through life. Lifelong learning is more accessible now than ever before. It’s coming up to 50 years since the founding of the Open University, which was one of the first establishments to break down the barriers of formal education.

And with access to a whole world of learning through Massive Open Online Courses (MOOCS) and online platforms such as TedTalks, we have finally started to master the art of ‘in the moment’ learning, with content that is delivered in an engaging way available on mobile devices 24/7.

Asking the right question

Equally, training should never be the sole territory of the organisation. Of course, organisations will provide support in the form of virtual or face-to-face training, as well as coaching and mentoring, but every individual should take responsibility for their own learning and development. Through the books they read, the videos they watch, the conferences they attend.

As an organisation, a fundamental question should be whether you know what training your people undertake in their own time. Do you know what interests and skills they’ve developed for themselves? Have they taught themselves video production or coding in their spare time for example? Do you think that there could be a way to better utilise this potential if it was visible to you?

One of the key questions I like to ask when meeting new colleagues and project partners is what they do in their free time, and what their most meaningful achievement in their area of interest has been so far.

This way I found out that somebody I worked with briefly was not only ‘into kayaking’ as others said about him – he had taken part in the Olympics years ago. Likewise, a friend of mine works as a senior developer for one of the tech giants – with his formal training being in medicine. He has no formal coding qualification, and is self-taught, purely based on his own interests.

Staying agile

We all know that the world is changing and that the skills we might need in our business will be very different in five years’ time to the skills that are required today. With the advancements in neuroscience and artificial intelligence, we now have a way to understand not only how people learn but also how people think.

By combining statistical analysis with the latest machine learning algorithms, HR and L&D teams can now scientifically and accurately predict the current and future performance of people and model the best way of engaging staff in learning on an individual basis.

At a basic level this means instead of relying on someone’s past experience in a role, or asking them whether they have the skills needed to do a particular job, with AI and neuroscience we can actually test people to find out whether they have the potential to succeed. And if necessary train – or retrain – them to improve performance.

As human beings we want to understand where our potential really lies, to make certain that we are fulfilled in our jobs and in our lives.

This allows individuals to move roles according to their abilities rather than their qualifications or experience, and into emerging jobs where there is little information about what a ‘good’ candidate might look like. In this way, relevant talents and abilities can be moved across boundaries into areas where there is a skills gap.

This has the added benefit of going beyond targets to increase diversity and social mobility in a more genuine way. By assessing people’s potential, rather than their past achievements, we can look beyond whether they have been given the opportunity to gain a 2:1 or above from a Russell Group University.

Eliminating bias by taking any race, age and social background out of the equation.

The quarter-life crisis

We also need to recognise that there will be specific points in our lives where our priorities, and therefore interests, might change. We are all used to talking about a mid-life crisis where we impulsively make rash decisions (such as buying a new sports car).

However, research by LinkedIn has confirmed that we now have quarter-life crises. Usually in our 20s or 30s, we’ll experience feelings of extreme stress and anxiety as we transition into adulthood.

During these transition periods, self-reflection and learning become more important. As human beings we want to understand where our potential really lies, to make certain that we are fulfilled in our jobs and in our lives.

Whatever age you are, channelling these searching questions into learning and taking on new challenges is likely to be a more satisfying outcome in the long term than botox.

And keeping your mind active will keep you fit and healthy long after you get bored of your gym membership. Doing this pro-actively also means that the crisis part of mid-life might be avoidable.

Research from the American Bureau of Labor Statistics shows that the average baby boomer holds nearly 12 different jobs before age fifty. This number is projected to grow for Millennials and even more so for Generation Z just entering the jobs market.

Using AI can make changing a career far less daunting and risky, and it’s a fact of working lives that we are going to have to get used to.

Learning in older age

From a cognitive perspective, people are able to learn throughout their whole lives. During my own PhD research, we compared cognitive abilities across two specific age groups. Students who were aged between 18 and 27 years old and older people between 60 and 86 years of age.

We conducted various different experiments looking at Motor Orientation, Rapid Visual Processing, Spatial Working Memory, Crystallized Intelligence, Memory, and many others.

There were some differences in specific tests, such as reaction times. But the differences that we hypothesized between the two age groups, due mainly to the cognitive decline in the older age group, were not supported by the results.

Fast and constant change will continue to feel daunting, but AI can also make it liberating.

If retirees keep mentally active and physically healthy there is barely any difference.

Technology can help us achieve an understanding that goes well beyond surveys, questionnaires and traditional personality testing.

The combination of artificial intelligence, machine learning and data science opens up huge possibilities for training and learning at a crucial juncture in the relationship between man and machine. This is where critical thinking skills and decision making, as well as emotional intelligence, will become essential for most jobs outside of automation or AI.

A liberating future

We are already experiencing working lives that are far more unpredictable than previous generations. Fast and constant change will continue to feel daunting, but AI can also make it liberating – helping us to find our real potential and identify L&D pathways that support us to grow and adapt to the changing world around us.

This presents organisations with a real opportunity to direct and support people towards training and into careers where they are not only successful, but also fulfilled and happy. At the same time, such actions will help meet the continually evolving demands of our changing workplace and fill the skills gap.