Up to 10% of education professionals could be out of work by 2025 due to the increasing use of machine learning and artificial intelligence, judging by recent reports on job automation. But how much can, or should, we take humans out of the learning process?

This is the question Professor John Preston posed to Prospect delegates at the Education and Children’s Services group’s professional seminar and biennial general meeting in Bristol in November.

He says that while there will still be a role to play for education professionals who embrace and use new technology to do their jobs, success will depend on them being able to contribute more to the learning process than machines can.

But a resurgence in competence-based learning and qualifications poses a threat to even this augmented role for humans, Preston argues.

He says the idea behind competence-based learning is that a job or skill can be broken down into behavioural components that can be learned and measured as either acquired or not acquired.

He first started writing about it in the 1990s following the competence-based educational training (CBET) movement the decade before, but has returned to the subject because it has resurfaced “in a big way” in education and professional development.

His new book, Competence Based Education and Training (CBET) and the End of Human Learning: The Existential Threat of Competency, explores how, while machines are becoming more human-like, humans are becoming more machine-like and are being pushed towards more machine-like processes in terms of the work that they do.

He argues that competence-based learning is inhuman because it focuses solely on input and output – and then measures the output in a binary way, as either competent or not competent.

So, for example, a trainee barista can be shown how to make a cup of coffee to exacting standards, focusing on presentation and taste, and then measured on whether they have acquired the skill or not. The training is not concerned with how the trainee barista learns and they are not encouraged to question any aspects of it. Whether the cup of coffee they make is bad, average or excellent, the only scoring possibilities are a fail or pass.

Preston says that human learning, by contrast, is an internal process that occurs over time and with variable results. “It’s messy, obviously, because we’re human.”

The focus on competence and competence-based learning removes humanity from the learning process and in so doing poses a risk to education professionals, says Preston.

So why is it experiencing a resurgence? Preston points out that among its attractions is that it can be lifelong and life-wide. If complex skills and qualifications can be broken down into behavioural components that can be taught and measured using machines, then learning becomes disconnected from time and place – it can be done anywhere at any time and for a range of skills.

It can be used in prisons, community settings and social work settings, where ordinarily it might be more challenging to organise traditional education programmes.

Also the marketisation of learning – with greater involvement by the private sector – and the increasing use of more complex technology is resulting in different ways of using competence-based learning.

It is becoming increasingly “gamified”, where video game design and game elements are used to capture and sustain interest. It is moving online and can also be done in augmented and personalised ways, such as with pay-as-you-learn models.

But Preston warns that this focus on competence and the binary – competent/not competent – measurement of it will pose the biggest risk to education professionals, because it inevitably leads to everything becoming more average.

If a learner only needs to meet a minimum set of standards to be declared competent in a skill, it removes any motivation to exceed these requirements and excel.

Preston argues that education professionals will be reduced to meeting a very average digital standard, which is what algorithms and robots can do.

So machines and humans will compete with each other to provide the optimal function – with artificial intelligence systems using machine learning and humans using a type of so-called discovery learning based on this instant and binary feedback.

Uber drivers will be competing with driverless cars and, in education, professionals will be competing with artificial intelligence systems.

So will humans still have a role to play in the learning process?

Preston thinks so, but only by focusing on what they can offer that machines cannot, such as the ability to improvise when in novel situations, using their tacit knowledge of what’s happened before, as well as relationships, humour, rapport and idiosyncrasy.

These are the things that make us human, he says, and we can use them in an improvisational set of skills to ensure that we are offering more than machines can, and more than just competence.

Steve Thomas, Prospect national secretary, said: “Professor Preston’s thought-provoking speech painted a picture of the future challenges with the expansion of machine-learning threatening the role of education professionals. 

“Prospect’s Education and Children’s Services members have long worked with standardisation and a focus on assessment. However, we share Professor Preston’s view that human agency should retain a key role in learning in particular and will make that argument strongly where professional roles are potentially under threat.”