Back

Moving Intelligence Forward with AI

Rose Luckin, UCL EDUCATE
  • articles

AI is already having a big impact on education and many are predicting the various ways in which the robots will take over the world. Professor Rose Luckin looks at the current state of play and suggests a new model of intelligence for educators that focuses on the importance of being human.

The article was first published by Teaching Times, May 2019

Artificial Intelligence (AI) is not just the stuff of movie and science fiction, it is here now and many of us use it every day. For example, when we search on the internet, use a voice activated assistant like Apple’s Siri or Amazon’s Alexa, or when we use an e-passport gate at the airport. AI is here, it is not going away and it will impact on education.

A basic definition of AI is one that describes it as ‘technology that is capable of actions and behaviours that require intelligence when done by humans’. The desire to create machines in our own image is not new—we have, for example, been keen on creating mechanical ‘human’ automata for centuries. However, the concept of AI was really born 63 years ago in September 1956 when 10 scientists at Dartmouth College in New Hampshire spent the summer working to create AI.

Following on from this there were some early successes. For example, expert systems that were used for tasks such as diagnosis in medicine. These systems were built from a series of rules through which the symptoms a patient presented could be matched to potential diseases or causes, so enabling the doctor, aided by their AI, to make a decision about treatment. These systems were relatively successful, but they were limited, because they could not learn. All of the knowledge that these expert systems could use to make decisions had to be written at the time the computer program was created. If new information was discovered about a particular disease or its symptoms, then in order for it to be encompassed by the expert system, its rule-base had to be changed. In the 1980s and 90s useful systems were built, but certainly we were not anywhere near the dreams of the 1963 Dartmouth College conference.

Then, in March 2016 came a game-changing breakthrough. A breakthrough that was based on many years of research. A breakthrough that was made when Google Deepmind produced the AI system called AlphaGo that beat Lee Sedol, the world ‘Go’ champion. This was an amazing feat—a feat that could seem like magic. While many of the techniques behind these machine learning algorithms are very sophisticated, these systems are not magic and they do have their limitations. Smart as AlphaGo is, the real breakthrough was due to a combination that one might describe as a perfect storm. This perfect storm arose due to the combination of our ability to capture huge amounts of data, with the development of very sophisticated AI machine-learning algorithms that can process this data, plus affordable computing power and memory. When combined, these three factors provide us with the ability to produce a system that could beat the world Go champion.

Each of the elements in that perfect storm: the data, the sophisticated AI algorithms and the computing power and memory are important—they are the power that enables us to build AI that can learn and improve. But just like any other technology that we might use in education, we need to use AI judiciously and we need to make sure that it is addressing the educational needs of our institutions, teachers and learners.

AI as EdTech

We can certainly use AI to tackle some of the big educational challenges and to support teaching and learning.

For example, companies like alelo (www.alelo.com) build educational technologies that use AI to help students to learn across a range of subjects, including learning English. London-based CENTURY Tech are another AI company that have developed a machine-learning platform that can personalise learning to the needs of individual students across curriculum areas to help them achieve their best. A further reality is that, in addition to being able to build intelligent platforms, such Century , we can build intelligent tutors (such as those produced by Carnegie Learning) that can provide individual instruction to students in a specific subject area. These systems are extremely successful; not as successful as a human teacher who is teaching another human on a one-to-one basis, but the AI can, when well-designed, be as effective as a teacher teaching a whole class of students.

In addition to intelligent platforms and intelligent tutoring systems, there are many intelligent recommendation systems that can help teachers to identify the best resources for their students to use, and that help learners identify exactly what materials are most suitable for them at any particular moment in time (see for example, www.bibblio.com and www.teachpitch.com).

It is not just when learning particular areas of the curriculum that AI can make a big difference. AI can also help us to build our cognitive fitness, so that we have good executive functioning capabilities, can pay attention when needed, remember what we learn, and focus on what needs to be done. This system, called MyCognition (www.mycognition.com), for example, enables each person who uses it to complete a personal assessment of their cognitive fitness and then train themselves using a game called Aquasnap. AI helps Aquasnap to individualise training according to the needs of the particular person who is playing.

Specialist Knowledge Required

However, there are more ways that AI impacts on education than through the way we can use it to support teaching and learning. A second way that AI impacts upon education can be seen in the way that we need to help people understand what AI is, so that they can use it safely and efficiently. We need everyone to have a basic understanding of AI, so that they have the skills and the abilities to work and live in an AI-enhanced world. This is not coding, this is understanding why data is important to AI and what AI can and cannot achieve. We also need everyone to understand the basics of ethics, but we need a small percentage of the population to understand a great deal more about this so that they can take responsibility for the regulatory frameworks that will be necessary to try and ensure that ethical AI is what we build and use. And then there is the real technical understanding of AI that we need to build the Next Generation of AI system. Again, a small percentage of the population will need this kind of expert subject knowledge.

AI and the Fourt'h Industrial Revolution

Finally, we come to the third category of ways in which AI impacts upon education, and that is the implications that AI is bringing to the workplace and our lives through what is sometimes called the Fourth Industrial Revolution. These implications that mean the automation of some jobs and the changes in some jobs, because AI can do some of what humans have been doing brings the need for changes to our education systems.

Many people and organisations, including the World Education Forum, are telling us that we are now entering the Fourth Industrial Revolution—the time when many factors across the globe, including the way that AI is powering workplace automation, are changing the workplace and our lives forever. Not everyone is as optimistic, and there are an increasing number of reports that consider the consequences for jobs of the increased automation taking place in the workplace. A report called ‘Will robots really steal our jobs?’ published in 2018 by PWC illustrates that transportation and storage appear to be the areas of the economy where most job losses will occur. Education will be the least prone to automation. We could interpret that as meaning that education will not change. However, I believe that education will change dramatically. It will change as we use more AI, and it will change as what and how we teach changes in order to ensure that our students can prosper in an AI-augmented world. Reports such as this also make it perfectly clear that the impact of AI, automation and the Fourth Industrial Revolution will not be felt by everyone equally. Of course, those with higher education levels will be least vulnerable when it comes to automation and job loss. We therefore need to provide particular support for those with lower levels of education.

Personally, I do not think all these reports are that useful, interesting as they are. We humans are rather poor at prediction and the differences of opinion across the different reports indicate the complexity of predicting anything in such fast-changing circumstances. Trying to work out what to do for the best in a changing world is a little bit like driving a car in dense fog along a road that you don’t know. In these circumstances, a map about the road ahead has limited use. What we really need is to know that we have a car that is well-equipped, we have brakes that work, lights that work. We want to be warm and we want to know that as a driver, we understand how to operate the car, we understand the rules of the road, we have eyesight that’s good enough to help us to see in the limited visibility ahead and we can hear what is going on so that we can respond to impending danger that may indicate its presence by being noisy—a huge truck thundering towards us, for example.

A New Model of Intelligence

So, what’s the equivalent of this good car and good driver when it comes to what we need in order to find our way through the fog of uncertainty around the Fourth Industrial Revolution? This is a subject that I have studied and written about quite a lot and a subject that is covered in the book Machine Learning and Human Intelligence: The Future of Education in the 21st century. Here I can only skim over the way that I unpack the intelligence that we need humans to develop if we are to find our way through this foggy landscape. This is the intelligence that can help us to cope with the uncertainty and it can help us to differentiate ourselves from AI systems. This is an interwoven model of intelligence that has seven interacting elements:

  1. The first element of this interwoven intelligence is: interdisciplinary academic intelligence. This is the stuff that is part of many education systems at the moment. However, rather than considering it through individual subject areas as we do now, we need to consider it in an interdisciplinary manner. Complex problems are rarely solved through single disciplinary expertise, they require multiple experts to work together. The world is now full of complex problems and we need to educate people to be able to tackle these complex problems effectively. We therefore need to help our students see the relationships between different disciplines. We need them to be able to work with individuals who have different subject expertise and to synthesise across these disciplines to solve complex problems.
  2. Secondly, we need to help our students understand what knowledge is, where it comes from, how we identify evidence that is sound enough to justify that we should believe that something is true. I refer to this as meta knowing, but of course we can use the terminology of epistemology and personal epistemology to describe this meta knowing.
  3. The third element of our intelligence that we really need to develop in very sophisticated ways is social intelligence. It is very hard for any artificially intelligent systems to achieve social intelligence, and it is fundamental to our success. We increasingly need to collaborate in order to solve the kinds of complex problems that we will be faced with on a daily basis.
  4. Fourthly, we need to develop our meta cognitive intelligence. This is the intelligence that helps us to understand what we need to know to understand how we learn, how we can control our mental processes and how we can maintain our focus and spot when our attention is skidding away from what it is we are trying to learn. These metacognitive processes are fundamental to sophisticated intelligence and again, they are hard for AI to achieve.
  5. The fifth element of intelligence we must consider is our meta emotional intelligence. This is what makes us human. We need to understand the subjective emotional experiences we sense and we need to understand the emotional perspectives of the others with whom we interact in the world. This emotional intelligence is also hard for AI. AI can simulate some of this, but it cannot actually feel and experience these emotions.
  6. We also need to recognise the importance of our physical presence in the world and the different environments with which we interact. We as humans, are very good at working out how to interact intelligently in multiple different environments. This meta contextual intelligence is something at which we can excel, and something that AI has great trouble with. Context here means more than simply physical location. It means location, but it also means the people with whom we interact, the resources that are available to us and the subject areas that we need to acquire and apply in order to achieve our goal.
  7. If we can build these interwoven elements of our human intelligence, then we can really achieve what’s important for the future of learning and that is: accurate perceived self-efficacy. By this I mean that we can see how we can be effective at achieving a particular goal, at identifying what that goal consists of, identifying what aspects of that goal we believe we can achieve now, what aspects we need to learn about and train ourselves to achieve. In order to be self effective, we must understand, then apply all the elements of intelligence so that we can work across and between multiple disciplines with other people with effective control and understanding of our mental and emotional processes.

Let me take a moment to stress something important here. This is about intelligence. It is not about 21st century skills or so-called soft skills. It is about something much more foundational than any skill or knowledge. It is about our human intelligence. I also want to emphasise that we can measure the development of our intelligence across all seven elements. They can all be measured, and importantly, they can all be measured in increasingly nuanced ways through the use of AI. This enhanced and continual formative assessment of our developing intelligence will shed light on aspects of intelligence and humanity that we have not been able to evidence before. We can use our AI to help us to be more intelligent, and this is very important.

The truth of the matter is that being human is extremely important.

In summary, we can therefore say about AI in education that AI is smart, but humans are and can be way smarter.