What kind of learning do we need to make the most of the new technological revolution?

Rose Luckin. 
Learning is the key to success in the fourth industrial revolution and I was delighted to be asked to provide evidence to the Future of Work Commission at the House of Lords. It helped me to crystalize my thinking.
Learning is at the heart of the fundamental insight that motivated reformers and precipitated the creation of state-funded universal schooling in the Industrial Revolution. This insight was that when education fails to keep pace with technology, workers suffer, fall behind, and society starts to fragment. When learning and innovation progress in harmony then we all feel the benefits. Finland’s Minister of Education and Culture, for example, has said that she wanted her country to be “continuously learning” and developing “strong, transferable skills” in a society where people can “return to education when they need it.”
But what does this mean? In order to prosper the UK needs a workforce that can adapt to rapidly changing and often unpredictable circumstances in a way that continues to build the UK intellectually, economically and socially.
Knowledge is important, of course. We need people to understand mathematics, English, geography and so on. But on its own this is not enough, not least because routine cognitive knowledge is relatively easy fodder for Artificial Intelligence. The plethora of so-called 21^st century skills are also important and we need people to have those too. However, for me the key skill that we will all need is self-efficacy to propel us through our lives. By self-efficacy I mean that every individual needs to have an evidence-based and accurate belief in their ability to succeed in specific situations and to accomplish tasks both alone and with others.
Our sense of self-efficacy plays a key role in how we tackle tasks and challenges, and how we set our goals, both as individuals and as collaborators. It is something that can be taught and it requires an extremely good knowledge of what one does and does not know, what one is and is not so good at, where one needs help and how to get this help. This self-knowledge is not just about the subject specific knowledge and understanding we regularly teach in schools and universities, but also about one’s wellbeing, emotional strength and intelligence.

• The first implication of the recognition that self-efficacy is essential for success, is that we need a curriculum that fosters self-efficacy along with the intellectual and emotional intelligence skills that it requires. Benjamin Bloom’s famous study illustrated a significant improvement in learning gains for people taught on a one to one basis as compared to a whole class basis. The real message from this is that failure is not the learner’s fault. The right support can help everyone to learn and we need to help everyone to learn self-efficacy through effective teaching and through the intelligent use of AI, because…

• The second implication of recognising that self-efficacy is essential for success, is that we need to harness the power of the automation that is precipitating our concern to the future of the workforce. Artificially Intelligent systems are both one of the causes of the need for change and one of the solutions to the consequences of this change. The mapping and tracking of individual learners as they interact through and with digital technology can provide us with a wonderful harvest of important data that can be subjected to analysis, machine learning and AI modeling informed by what we know about learning from psychology, education and neuroscience. This analysis and modeling can provide detailed and nuanced information about each individual’s progress: intellectually, emotionally, socially, metacognitively and in terms of their developing self-efficacy. Metacognition is our ability to understand and regulate our own thinking. For example, to know what we do and do not know, to know when we are and are not learning effectively and to know how to plan our own learning, including when we need help from others. It is an ability that is well developed in successful learners.

Once we have this information about individual learner progress, we can aggregate it to draw conclusions about cohorts of learners, about the success of an education system or about the extent to which we have a population who have the knowledge, skills and abilities to meet the needs of an evolving workplace.

• The third implication is that self-efficacy in the future will not just involve all of us in being able to work on our own and with groups of other people as our job requires. It will also mean that we will need to know how to work with AI, because the overwhelming evidence about the future of work suggests that much automation will not be to replace entire jobs, but rather to automate parts of jobs. This will mean that human and AI will need to work together. To achieve such co-working effectively we will need to know what to expect from AI, what AI expects from us, and when we should challenge an AI and when we should follow its advice.

I suspect that when most people think about teaching people about AI, they imagine that people need to learn to code, know how to build neural nets and deep learning into our technology. It is certainly true that we need many, many more people with these technical skills if we are to build the AI systems that will allow us to benefit from the amazing developments in AI for health and for transport, for example. And this learning needs to start young and now.
However, this is only the visible tip of the iceberg. The bulk of the problem is that we need everyone to understand enough about AI to use it effectively for work, for leisure and for life. Everyone needs to know enough about what an AI is doing with their data to make informed decisions about whether or not they consent to their data being used in this way. The truth is that, at the moment, most of us don’t know enough about what is happening to our data and what an AI like Alexa or Siri is doing with it.
If we take these three implications together, then the biggest challenge we face for the Future of Work is the winning of the hearts and minds of the population.
If we:

• fail to recognize the importance of self-efficacy, because we are only measuring subject knowledge;
• fail to exploit the power of AI through fear of the security, privacy and protection of our personal data and that of our children; and we
• fail to teach people enough about AI to empower them to make key decisions about what it should and should not, could and could not, will and will not be able to do for society,

then we will be unable to galvanize the prosperity that should accompany the revolution.
The UK Government’s new Digital Strategy pledges to “give everyone access to the digital skills they need.” Let us hope they widen their thinking about just what these skills are.
 
Photo: Strelka Institute for Media, Architecture and Design via Creative Commons