Who could be anything but delighted to see this headline? A-level results: Malala Yousafzai gets a place at Oxford, this is excellent news and a great boost for those campaigning for equal education. In fact, the publication yesterday of A level results in the UK has spurred me to take a slight diversion from worrying about who is moving my brain or my cheese. I certainly would not want to detract from the hard work that any students have put into their A level studies or to take the shine off their success. It is wonderful to see the smiling faces of successful students across the newspapers.
However, success does not come to all and even on a celebration day, or perhaps I should write especially on a celebration day, I think we need to consider alternatives to the stressful stop and test regime that pervades most education systems. I wrote about this in Nature Human Behaviour earlier this year under the heading: ‘Towards artificial intelligence-based assessment systems’ and it looks like it has been read a few times because it is ranked 5,746th of the 237,966 tracked articles of a similar age in all nature journals which puts it in the 97th percentile. This does not seem bad given that it was only a ‘comment’ piece and not a full paper. On a less positive note in an internal REF assessment exercise it was only ranked as 2*, which is not great and probably reflects the difficulty for academics in publishing more popular style articles. However, the modest success of the article in terms of the altometrics that Nature run encourages me to believe that there is some interest in exploring the possibilities that the intelligent design and application of AI could afford for National assessment systems. I therefore draw attention to this possibility here and hope to encourage further debate. The key point I wanted to convey in the Nature Human Behaviour article was that there are alternatives to exams, that are less stressful, less expensive and that allow teachers and learners to spend more time on teaching and learning (shouldn’t this be the point of education?).
This message may not be what others have selected to focus on, but for me, the most important thing is that we have an assessment system that is holistic, fair and that let’s all students evidence their knowledge, skills and capabilities.
As a child I was always frustrated by the phrase: “curiosity killed the cat”. This was a frequent retort when I was trying to understand how things worked. Well, I am not reporting any cat killing incidences here, but my curiosity about myself driven by my new ‘misfit’ may have been a primary factor in my newly sprained ankle!
Over enthusiasm to meet that target of 1000 activity points motivated me to get walking and launched me down some steps in a most ungainly and unfortunate manner. No broken bones, but some swelling and plummy bruising have resulted in my needing to rest up for a few days. Resting up in a Sydney winter is hardly a chore, the sun is out and the sky is blue and I indulged in exploring the ABC TV channel and in particular a great program called The AI Race.
The program presented data from a study into the risks to Australian jobs from AI powered automation. I was relieved to see that professors are only likely to have 13% of their job automated, whilst carpenters are predicted to have 55% of what they do done by smart technology. Might this be the same in the Uk, or different I wondered? The ABC reporter explored various jobs and met up with employees. For example, Frank: a truck driver, was not persuaded that autonomous trucks would be able to replace his experience and intuition about the behaviour of other humans whether pedestrian or driver. The autonomous vehicles would not be able to help out other drivers stranded on the roadside or provide human customer service on delivery of a load either. He was definitely not convinced that AI was going to replace him any time soon.
Further jobs were explored: the legal profession for example where law students were stunned by an AI para legal that could search through thousands of documents to find a specific clause in no time at all. The law students berated their education for not preparing them for a world of automation.
On the one hand we have Frank, who does not believe that AI can replace him, and on the other we have a group of law students who are persuaded that AI can already do a lot of what they are studying to be able to do. Nobody seems very curious about how they might better prepare themselves for AI’s onslaught on their workplace. So, how might I persuade them that understanding more about their own intellect could help them work more effectively with AI? The key to future success has to be that people need to focus on developing the expertise that AI cannot achieve: the still unique human qualities that will be at a premium. Self-knowledge and Self-efficacy are important elements of this expertise, but how do we motivate people to develop themselves? To start answering this, I looked at the best selling self-help books for guidance. People buy these so maybe I can learn something about how to appeal from their sites – which of these might work best?
It is far too long since I last posted to this blog: too many jobs and too little time would be my fist attempt at an excuse. But, perhaps it is just that I am not effective enough, that I need better self-regulatory skills, more intelligence and a better understanding of my own strengths and weaknesses. I talk quite a lot about intelligence and about how AI developers have not yet designed artificially intelligence systems that understand themselves and have metacognitive awareness, but maybe I too lack these abilities? So, how might I become more self-effective?
This thought is one that I intend to worry at while I am completing a research trip to the University of Sydney to work with my colleague Judy Kay. We are working on Personal Analytics for Learners (PALs), or more precisely interface designs for PALs (or iPALs).
In order to help me thing this through I wanted to learn more about some of the work that Judy and her colleague Kalina Yacef have been doing in collaboration with medics and health professionals to develop better data analytics and interfaces for personal health information for education. For example, the iEngage project provides a digital platform for children with information, education and skills to help them to achieve their physical activity and nutritional goals. It connects with ‘misfit‘ activity trackers to provide continuous feedback and summarise the daily activity on a dashboard.
To this end, I bought myself a ‘misfit’: a somewhat cheaper version of a ‘fitbit’ with a great name :-). I am now tracking my sleep and my pulse as well as my physical activity and diet in order to try and understand more about my personal wellbeing. This is nothing new and millions of other people do this too. I notice that popular technology stores stock a good range of fitness tracking devices and increasingly more reasonable prices.
So, in order to also help me be better at understanding my mind and my cognitive progression and metacognitive skills and regulation, I now need a ‘mindset” to help me track how well I am thinking, learning and regulating my working and learning. The interface to such a ‘mindset’ is the idea behind the iPAL that Judy and I are currently designing. I find it interesting to speculate about the kinds of data that we could collect about our intellectual and social interactions that would help us track and better understand our intellectual mental wellbeing as well as our physical welding and fitness. This kind of ‘fitbit’ for the mind might help me to be less distracted by non-priority activities and spend more time on priorities, such as writing.
A search for ‘fitbit for the mind’ yields some hits, though not terrifically interesting ones. There is an article in new scientist about eye-tracking to tell you more about your reading habits, and a mindfulness app that can be linked to fitbit data. The problem here is that we are being offered some automatic tracking of just one type of mental activity – reading, or mindfulness and actually we need something way more sophisticated to tell us about how we our intelligence and self-awareness is progressing. Perhaps something that looks at multiple data sources and provides us with an overview of our activity in a way that motivates us to want to know more about our intellectual fitness in the same way that activity trackers help us understand more about our physical fitness.
Earlier this month, there was a more interesting article in Newsweek that talks about ‘iBrain’ and the possibility for us to be able to track our brain’s electrical output and see markers for the likely occurrence of a range of mental health disorders from anxiety, depression, and schizophrenia, to dementia and Alzheimer’s before symptoms appear. Such information might help early intervention and monitoring. This reminds me of the rise of personal DNA services, such as 23 and me. If people are interested in their DNA and what it might tell them about how they should adjust their lifestyles to avoid certain conditions that they look to be susceptible to, then maybe people are also curious about their intelligence and how they can understand it better.
Over the next few blog posts I plan to explore what such a device might be like, what data it might collect and how I might best benefit from the sorts of information it could provide.
AI is both a cause and a solution to the problem of a world where there is far more information than any one person can possibly effectively process to construct their own understanding about what they believe and what they don’t. AI can amplify the echo chamber by promoting the most believed over the most evidenced. BUT it can also help us to recognize valid information from noise, IF we know the right questions to ask and IF WE KNOW HOW TO WORK WITH OUR AI we can develop deep understanding and escape from the maze of invention…
Early in my career I was advised that if I wanted to get a point across when teaching, during an interview, as part of a presentation or when debating, I must repeat the point I wanted to make three times. There is an empirical basis for this advice: something eloquently explained my Malcolm Gladwell and the motivation for my blog identity: The Knowledge Illusion. Put simply, when people are provided with more information about X, they believe that they know more about X, when in fact they often know less about X. I wrote about this many blogs ago (transcribed below for ease of reference) to draw attention to the essential need to help people decipher the huge volume of information that comes their way so that they can discern what is genuine from what is fake.
I still follow the “say things three times” advice in my endeavour to communicate what I consider to be valid, some might say truthful, information. My objective is to persuade people that my perspective, opinion, or information presentation is the stuff to be believed. However, I accept that it is entirely up to my audience to decide whether or not they are won over. The importance of this subjective experience and the belief that an audience are actively analysing the information that comes their way is ever more important. In a world of echo-chambers and deluge of social media, we need people to be able to look at a stream of data and information and make intelligent decisions about what they believe to be the stuff of knowledge.
The problem is not new. It was JFK who once observed that “No matter how big the lie; repeat it often enough and the masses will regard it as the truth.” This is an enormous insult to the intelligence of the “masses”, but unless we pay attention to helping these “masses” to navigate through the morass of mediocracy that social media precipitates, proliferates and perpetuates then we will return to the pre-enlightenment era when the world was flat and knowledge was the privilege of those who knew how to decipher the written word and who acted as the mouth-piece for and the collective intellect of their communities: the “masses”.
The word “masses” is no longer widely used so let’s just refer to the “masses” as the people: the global human race whom education is intended to equip with the skills and abilities to think and make sense of the world and the information others produce about it. To consider what it is we need to do to help people to make sense of the world it is worth travelling even further back in time to the views of Roman Emperor Marcus Aurelius that: “Everything we hear is an opinion, not a fact. Everything we see is a perspective, not the truth.” We need to encourage a nuanced belief system where people are provided with the skills, confidence and resources to construct their own understanding from the tidal wave of data and information that threatens to engulf them.
Again, history can help to inform us. The scientific revolution set the stage for the age of enlightenment that transformed the human race and promoted the importance of reason. Influential thinkers like Bacon, Locke and Descartes paved the way for the likes of Voltaire, Kant and Smith. Life was so much simpler then of course, but the huge increase in what it is possible for an individual to try to understand and know does not discount the important role that influential thinkers can play.
The birth of the www and social media represent a new generation of publications that play the role of the encyclopedias and dictionaries in the age of enlightenment. BUT who are the key philosophers and scientists who can catalyze the popular debates in the way that the philosophers of the enlightenment did? Stephen Hawking would probably be high on the list of influential thinkers who many people (the “masses”) might be able to name. Who else?
Whilst the volume of information and data about the world has ballooned, the number of influential thinkers who can help people find their way to knowledge and understanding has may not have kept pace. Technologies that harvest the ‘wisdom’ of the crowd often promote the loudest shouters and the most-followed, rather than the considered and grounded reasoning of the real intellectuals. The demise of expertise has exacerbated the problem as professional predictions have failed to materialize…. Let’s just stop there for a moment.
Could the real problem be that we, the people, don’t know how to interpret expertise? We want simple answers when there are none to be had. In schools we still encourage the belief that rote learning and subject specific information of the type that can be reproduced by a single person when challenged with a standardized test sufficient. This outdated approach gives the impression that knowledge and understanding are way more simple than they really are. They encourage people to believe that there is a body of stuff that they need to learn and reproduce, and that if they can do this they will be knowledgeable. However, what we should be doing is ALSO encouraging people to constantly probe, prod, compare and conclude for themselves their understanding of the world so that they can apply this knowledge to solve the problems they encounter every day.
The surge of tweets that give the impression that meaningful things can be said in 140 characters is not always helpful either. There is certainly something to be said for trying to distil understanding into a short text — it is difficult and can test how much we really understand. However, the believe that a tweet can be the whole story in and of itself is misguiding. Knowledge and wisdom need to be worked at, by questioning, analyzing, aggregating and synthesizing to reach our own evidence-based beliefs about what we know and what we understand. Someone else’s tweet might start this process, but we have to finish it for ourselves.
Ai can help us to do the work here. AI can analyze and visualize complex data and information in order to literally help us see the ‘wood from the trees’. AI can be built to model human understanding and to justify the decisions and predictions that it makes. AI can explain to us how to complete complex activities, such as solving mathematical equations or managing a complex power plant. BUT Artificial and Human Intelligence must work together to help people extract the truth from the lies. We as humans must ensure that we know enough about what AI is capable of doing to ensure that we ask the right questions. We must learn to be discerning enough to challenge the AI when we are not convinced by what it is telling us.
This means that now more than ever we must educate the educators. Because educators must instill in us, the people, the investigative skills that we need to ask the right questions so that we can differentiate evidence from falsehood. Educators must encourage the confidence and self-efficacy in us that will help us believe our own minds. Educators must engender the perspective taking and integrative thinking that will enable us to work together to solve problems and to develop the influential thinkers we need now more than ever to enlighten us.
I am curious about knowledge, not in philosophical sense, but in a practical one. I worry about what it means to know something in a world that is increasingly complex, ill defined and interconnected: a world that demands that we develop, and that we ensure that our children develop, the knowledge capacity to solve the problems it manifests and those that we create.
The first recollections that I have of my own curiosity about knowledge date back to 1966 when I was eight years old and growing up in Manfred Mann’s semi-detached suburbia: dad, mum, older brother and me. My father was an aircraft engineer and my mother taught typing and shorthand to women whose working lives were about to be dramatically changed by the word processing power of the digital computer. My brother was 3 years older than me, and his lack of interest in formal education was causing my parents some concern. Their reaction was to invest in ‘knowledge books’, or at least that’s how they saw the children’s book of knowledge and the encyclopedia that now filled up the bureau bookshelf. To keep us up to date, there was also the weekly general knowledge magazine that plopped on the doormat with a reassuring thud: the weight of its knowledge there for all to hear.
I suspect that my parent’s reaction to their son’s educational malaise was not an unusual one amongst the aspiring middle class families of our neighbourhood. My brother’s reaction to the new literary arrivals was cool; he was far more concerned with exploring the world of the woodland around our housing estate, than with sitting at home and reading about it. My father however, became quite addicted to the weekly general knowledge magazine. He did not have a great deal of time to read, but each evening when he went to bed he would sit in his paisley pyjamas and thumb through the pages. The stock of copies soon grew on the nightstand as his pace of reading failed to match the frequency of their arrival. The corners became slightly curled as the months and years passed and the dust gathered in and around the pile that now extended from the nightstand to the floor. His interest never waned and I do believe there were a pile of old issues by his bedside when he died many years later.
Forty years on and it’s a sunny day and I’m walking along the Euston Road in London. I pass the entrance to the British Library and a sign catches my eye, the sign says: “Step inside – Knowledge freely available”. I dislike the suggestion that one can walk into the British Library and just pick up some knowledge like going into Tesco and buying some bananas. I can relatively quickly formulate an explanation for myself about why the sign irritates me, because I have a clear idea about what I believe knowledge to be. I have moved on from the conception of knowledge loved by my father and represented by the pages of his books and articles. I know that I have to construct knowledge from the evidence available to me, that it is not handed to me by others, though they can certainly help me along the way, and that I can aspire to continually increase my knowledge by weaving together the information resources distributed throughout my world.
This is not the case for many of the youngsters who attend our schools and colleges. For them knowledge is still to be found in the dusty concepts in the out of date magazines on my father’s nightstand or on the shelves of a library they never visit.
“But what of the internet and world wide web?” I hear you wonder. These technological masterpieces offer information resources wherever we are and whenever we need them. These must surely pave the way for us to become more knowledgeable, both personally and as a human community?
The sheer abundance of this information has thrown into sharp relief our understanding of the relationship between information and knowledge. It makes my modest collection of childhood encyclopedias and my father’s overflowing magazine collection look like a speck of dust on the library shelf. I fear however that our understanding of what knowledge is and what it means to know something has not progressed in tandem with this technological progress. This puts us at risk of succumbing to the illusion that we know more than we actually do, because the more information we have the more we become certain that we know something.
Without helping young people to develop an understanding of what knowledge is in a digital age they cannot progress beyond the well meaning, but limited conception of knowledge promoted by the books and magazines that appealed to my parents. Those of us who understand what we mean by knowledge can indulge ourselves, as my father did with his magazines. But, without actively engaging people in the excitement of connecting the knowledge construction process to their own particular context, we merely encourage them to pass the opportunity by in the same way as my brother did all those years ago.
In a time of information plenty we are at risk of a knowledge famine.
I was struck yesterday by the juxtaposition of the Economist’s dubbing of Theresa May as “Theresa Maybe“and the Telegraph article authored by the PM about her desire for a shared society that will tackle “everyday injustices”. How exactly will the shared society work, and in particular in what ways will education be changed in order to achieve the worthy goal of a fairer society for all? I feel there is already a lack of decisiveness in the lack of detail about what kinds of policy will deliver this solution to the everyday injustices faced by many learners across all ages and sectors of education. (From an interesting article in the Huffington Post)
Education can change lives for the better, but sadly it often does not and those who are privileged are able to benefit from better opportunities for learning. So here is a suggestion for unlocking some of this inequality.
My colleague, Wayne Holmes and I were asked to write an article for ‘How we get to next‘ and we used this article to pitch the benefits of an AI classroom assistant to help and motivate teachers to ensure that all learners are involved in activities that meet their needs. We tell the story of Jude, a teacher in the year 2027.
“And at Jude’s side, there’s her AI Teaching Assistant, Colin, whom she’s named after a childhood friend. In fact, so many aspects of how Jude understands her students’ learning are different now, thanks to her machine aide.
Through working with Colin, she has become somewhat of a metaphorical judo master, harnessing the data and analytical power of AI to tailor a new kind of education to each of her students. Her role at the helm of the classroom, however, is fundamentally unchanged.
Since Colin makes ongoing assessments based on daily student performance and engagement in the classroom, there is simply no longer any need for what were often inaccurate and stressful evaluations. The AI aide’s primary task is to build and maintain learner models for each child based on a combination of data gathered over time with things like voice recognition (which identifies who is doing and saying what in a team activity) and eye tracking (to note engagement and focus). The profiles are updated continuously, monitoring students’ progress against analysis of their emotional and motivational state.”
I know that well designed AI can help us build a much fairer education system in which all learners benefit and prosper, and that we have the technical and human capacity to create the right type of AI. A better educated population would then surely help us to tackle some of the other major challenges that a shared society agenda might face, such as inequalities in the health system and problems related to immigration.
AI assessment systems could provide a fairer eleven plus selection, it could also start to address the vexed question of assessing potential rather than just current ability. We know that well designed AI systems that assess learning, are accurate in their assessment.AI assessment can tackle more than subject specific knowledge and reasoning, it can also evaluate skills such as planning and knowing what we know. AI assessment would also provide a fairer assessment system that would evaluate students across a longer period of time and from an evidence-based, value added perspective. We also know how to prevent people from gaming AI assessments, in addition to which AI Assessment systems would also offer tutoring for everyone and support and formative feedback to help students learn and improve. If there is to be a revamp of the grammar school system then we must explore these possibilities.
Theresa May’s plans for new or expanded grammar schools in England have brought a torrent of comment, debate, criticism and rhetoric since these plans were inadvertently revealed last week. Most of the discussions seem to have focused on whether or not grammar schools are the right mechanism to aid social mobility. This is an extremely important issue, but let’s put the rights and wrongs of selection and grammar schools to one side for a moment and look at the eleven-plus examination itself.
The eleven-plus examination is the key to the door of one of the 164 grammar schools in England, or one of the 69 grammar schools in Northern Ireland. The examination is sat by children in their last year of primary school and it varies depending upon where in the country it is taken. In fact, the situation is very complicated with a wide range of approaches even within the same county. For example, in Yorkshire there are three Local Authorities with Grammar Schools: Calderdale has 2, Kirklees has 1 and North Yorkshire has 3. The 2 grammar schools in Calderdale use Verbal Reasoning tests, and Maths and English examinations using GL Assessment, University of Edinburgh and the school themselves as their examiners. However, the 1 school in Kirklees uses tests in Verbal Reasoning and Non-Verbal Reasoning, plus an English examination and a Numerical Reasoning test. These are all examined by University of Durham. The situation in North Yorkshire is different yet again, with 2 schools using Verbal Reasoning and Non-verbal Reasoning tests examined by NFER and the 1 remaining school administering and examining its own selection tests.
The complexity in the selection process is not helpful to poorer parents, who do not have the time, and possibly not the capability, to navigate the process. In addition to which the examination approach is traditional and outdated. The need to look deeper than the selection process to the eleven plus examination itself was highlighted in an interesting discussion on the Radio 4 Today programme last week. The discussion was between Laura McInerney, the editor of Schools Week, and Sean Worth, from Policy Exchange. Sean pointed out that the current mechanism for selecting children for grammar schools can be gamed and that we therefore need to change the examination if we are to ensure that the poorest children are not disadvantaged. Laura McInerney also pointed out the major problem for poorer children accessing grammar schools is that we “put a test in the way”, especially divisive when the parents of poorer children can’t pay for tutoring to get their offspring through the eleven plus examination.
The Guardian published a depressing article on the problems inherent in the eleven plus test ‘‘Tutor-proof’ 11-plus professor admits grammar school test doesn’t work’. The article reports the failure of a ‘coaching resistant’ test developed by CEM at the University of Durham for use in Buckinghamshire. CEM has now withdrawn the claim that the test could assess “natural” ability. Prof Coe director of CEM is reported as saying: “Whatever system you use it is imprecise, there are false positives and negatives and probably more of those than people realise.” He goes on to reflect that whilst he does not agree with creating if we are to have more then we need to try and make the system fairer. I couldn’t agree more – and the need for a radical rethink is echoed in what the IOE’s Tina Isaacs says about the problems of coming up with any test that can assess future potential.
So, let’s take the test away and develop a radically different, socially equal eleven plus. We are lucky enough to be in a very different situation today from that which existed when the original eleven plus was introduced in 1944. There is now a realistic and economically attractive alternative at our fingertips. We have the Artificial Intelligence (AI) technology to build a superior assessment system should the proposed reforms become a reality. AI provides a powerful tool to open up the ‘black box of learning,’ to provide a deep, fine-grained understanding of when and how learning actually happens. Intelligent algorithms can process information about each learner and reach a view about their progress, knowledge and understanding of a subject or skill over a ‘period of time’. Unlike the eleven plus examination, this ‘period of time’ could be a whole school semester, a year, several years and beyond.
Of course there are serious ethical questions around AI being used in education and these must be explored. But the over-riding and uncontested fact in this debate is that education is the key to changing people’s lives. We trust AI with our personal, medical and financial data without a thought, so let’s trust it with the assessment of our children’s knowledge and understanding. Let’s open our minds and explore the challenges to build a new generation of eleven plus assessment that genuinely irons out the inequalities and gives all children a chance to shine.
 Hill, P. & Barber, M. (2014). Preparing for a renaissance in assessment. London: Pearson., DiCerbo, K. E. & Behrens, J. T. (2014). Impacts of the digital ocean on education. London: Pearson.
A recent article in the THES got me thinking. David Matthews reported under the title: The robots are coming for the professionals, and asked if universities need to rethink what they do and how they do it now that artificial intelligence is beginning to take over graduate-level roles? This motivated me to write a blog post for THES that was published on 9 August: Four ways that artificial intelligence can benefit universities, in which I suggested that HE needs to embrace the positives of AI, not just look at the negatives.
These issues are not limited to HE, in fact this is a wake up call for all of Education. We must engage with these technologies and those who are developing them NOW in order to ensure that the AI that we end up with in classrooms, homes and the workplace is informed by what we know about learning and NOT what we know about what the technology can do.
There is a huge and growing interest among those who invest in new technology ventures, specifically Artificial Intelligence (AI) techniques and methods. For example, between 2011 and 16 May 2016 Sentient Technologies received over 143 million USD in funding (Data from CB Insights) Much of the excitement about AI has focused on general purpose AI i.e. intelligence that is applicable across a variety of industries and activities. This is being promoted for technology businesses as a force for good. For example, Antoine Blondeau, the CEO of Sentient, has stated that: “From healthcare to finance to e-commerce, we’re focused on changing people’s lives.” Sentient is reported to be working on financial platforms and on an AI nurse to diagnose patients with sepsis.It is a business that like many who are adopting AI methods has no problem in attracting funding.
However, the same is not yet true of organisations who are adopting AI for education. Yes, there are things like Udacity, that claims it will change HE, and Knewton whose CEO Jose Ferreira, really does believe that his technology will replace human teachers. Such an outcome would make ‘driverless classrooms’ into a science reality. These commercial AI in Education ventures are well funded. BUT it is hard to find mass investment in the application of AI to education, despite the fact that the Educational Technology sector is predicted to grow from £45bn to £129bn by 2020. And to my mind much more significantly, despite the fact that education is the real key to changing people’s lives.
We need to take a fresh look at education if we are to ensure that the global population is able to reap the potential of the AI revolution that is sweeping across the workplace. AI is both a cause of the radical changes to the workplace that prompted David Matthews to write his piece in the THES and a provider of an answer to the problem of how we make the most of the workplace automation that AI is enabling. The purpose, methods and outcomes of education need re-thinking and AI can help us to tackle the challenge of this re-thinking if we invest in its development and build on the thirty years plus of research in AI for Education.
The importance of the social and economic significance of the developments in autonomous systems and AI was reflected at the annual meeting of the World Economic Forum 2016 in Davos, where the focus was on ‘The Fourth Industrial Revolution‘. This revolution “is characterized by a range of new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human.”These radical changes do not however seem to manifest themselves in a concerted effort to use AI to revolutionize education. This oversight is shortsighted to say the least. The few exceptions that one can find where AI is being applied to education at some scale have a very narrow perspective and are a long way from changing people’s lives in the positive way that we want and need. For example: Knewton, is just one a a host of companies who believe that Subject Knowledge is the key to unlocking education for all. Through, for example, making artificially knowledgeable adaptive tutors who can personalize their content to meet an individual learner’s needs. This is all very well, but there is so much more to education than subject knowledge and so much more to AI than adaptive educational content
So what are the key attributes of AI for Education that will enable it to start attracting the sort of investment that Horizons Ventures and Tata Communications have made in Sentient Technologies? What are the attributes of AI that will persuade research funders that AI for education is a subject they must prioritize and that it must be a truly interdisciplinary enterprise that is not driven purely by technologist’s dreams. For a change let’s focus on disadvantaged learners’ dreams and see if we can work with technology to turn these dreams int o reality.
One key attribute of AI for Education is the ability that Educationally driven AI techniques and algorithms bring to the analysis of the vast amounts of data about learners that is routinely harvested by the increasing amount of technologies in the world around us from CCTV, to smartphones, wearable technologies and online courses, such as MOOCs. For example, we can
Conduct fine-grained analysis of learners’ skills and capabilities so that their development can be tracked at the student/employee, workplace, school, area, and country level;
Enable the collation of a dynamic catalogue of the best training and teaching practices across a range of environments and as a result enable us to educate and train the future workforce in an economically productive manner.
A second key attribute of Educationally driven AI is that it can help us to tackle the toughest educational challenges, including learner achievement gaps, teacher skill shortages, continuous professional development for educators. If we think about the business of education for a moment, imagine the AI teaching assistant that can be used to stretch the brightest pupils, while the human teacher devotes their expertise to giving the less able learners the sensitive human support that they need in order to progress. The teacher would train their personal assistant to work in the way that the teacher and their students need and would demand that the AI assistant explain the decisions it has made about students and the educational opportunities the assistant has provided.
But perhaps what we need to focus on first is using AI systems that go beyond the machine learning and neural network techniques that dominate the work of the main AI protagonists within and beyond education, from Knewton to Google DeepMind. The types of AI we need within education is the AI that enables the technology it powers to explain its reasoning, to justify its decisions and to negotiate with its users. This is the sort of AI technology that could help us address one of the toughest challenges within the current workplace: The lack of understanding about how humans can best work with AI systems so that the result is AI augmented human intelligence that is greater than the sum of its parts. We need workers who understand how to make the best use of the power that AI automation can bring to industry and commerce. Workers who understand enough about AI to know where and how human intelligence can work with AI to achieve a blended intelligence that can increase productivity. And what is beautiful about all this is that the appropriate type of AI can help us educate and train people to understand enough about their AI colleagues to work alongside them effectively.