Truth, Lies and Enlightenment: how AI can help us to build knowledge and understanding in the echo chambers of life

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.

More relevant than ever…Information plenty, but knowledge famine: are we succumbing to an illusion?

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 wrote thsi piece originally for  Learning to Live – Creativity, Money and Love

We have the technology to eradicate exams, tests and stress forever, so why aren’t we using it?

The recent leaking of SAT papers and the growing body of evidence on the stress and anxiety experienced by students who have to sit a battery of tests and exams highlight an area of serious concern. It is all particularly frustrating because it does not have to be like this.

Artificial Intelligence (AI) could wipe out all this pain and change schools forever: it could do away with the need for exams.

This is not to suggest that we should do away with Assessment. It is essential that we know how students are progressing in their knowledge, understanding and skills, and how teaching practices and educational systems are or are not successful. However, assessment does not have to mean tests and exams.

exam_stressArtificial Intelligence is difficult to define because it is constantly shifting and interdisciplinary. However,  AI systems can be described as computer systems that have been designed to interact with the world through capabilities (for example, visual perception and speech recognition) and intelligent behaviours (for example, assessing the available information and then taking the most sensible action to achieve a stated goal) that we would think of as essentially human.[1]

AI has been in the news recently with the AlphaGo programme beating a human champion Go player for the first time and the prospect that Google’s driverless car will soon be available for us to try (). On the negative side there are concerns about the impact of increasingly sophisticated AI on our economy and in particular the jobs market.

 

However, the sort of AI I am talking about here is specific to education and has the catchy acronym AIEd. It has been the subject of academic research for more than 30 years and promotes the development of adaptive learning environments and other tools that are flexible, inclusive, personalised, engaging, and effective. At the heart of AIEd is the scientific goal to “make computationally precise and explicit forms of educational, psychological and social knowledge which are often left implicit.”[2] In other words, in addition to being the engine behind much “smart” EdTech, AIEd is also a powerful tool to open up what is sometimes called the ‘black box of learning,’ giving us deeper and more fine-grained understandings of how learning actually happens.

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Artificial Intelligence tools and techniques could do away with the need for stop and test assessments and all the stress and anxiety that goes with them. There would be no more need for marking and re-marking, no appeals about results, none of the machinery of exam sitting that dominates the summer term in secondary schools with its “Silence, exam in progress” signs and the commandeering of sports facilities for use as exam halls. There would be more time for teaching, more time for sport and more time for curriculum enrichment.

 

AIEd provides the technology to conduct fine-grained analysis of learners’ skills and capabilities as they learn so that their developmimages-1ent can be tracked continuously and appropriate support provided. Instead of traditional assessments that rely upon evaluating small samples of what a student has been taught, AIEd-driven assessments could be built into meaningful learning activities, perhaps a game or a collaborative project, and will assess all of the learning (and teaching) that takes place, as it happens[3]. AIEd also offer the capability to track the 21st Century Skills that the modern workplace requires and that traditional assessment miss. Skills such as critical thinking, collaboration and initiative.

There is of course a considerable commercial ecosystem surrounding the current assessment system and this may cause some hesitation about adopting the AIEd continuous assessment and support approach. There are also significant ethical issues that need to be considered, such as who has access to the data-stream about student performance and can it be edited or commented on by parents, teachers or the student. The adoption of an AI driven assessment system would be a huge cultural change and not everyone would understand it or feel comfortable with it. Many innovations do not meet with immediate popularity: electric vehicles for example, but over time they are accepted, their benefits are appreciated and their popularity grows.

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Unfortunately, there is a hesitation in the UK to exploit either the social and economic potential of AIEd or its commercial benefits. Funding is poorly targeted and as a consequence the UK is at risk of losing its internationally leading research base and its competitive edge. We need to move from the cottage industry of existing UK AIEd research, to a rich ecosystem of disciplined innovation. And we need to move from siloed and short term funding to a funding landscape that reflects AIEd’s enormous potential.

 

But, most importantly of all we need to engage teachers and learners, employers and workers, in the design of the AIEd systems that are developed to provide both the assessment and the learning benefits that this technology has to offer.

 

This blog post can also be found on the UCL IOE blog. It draws on the following publication, where readers can find out more about AIEd: https://www.pearson.com/content/dam/corporate/global/pearson-dot-com/files/innovation/Intelligence-Unleashed-Publication.pdf

 

[1] ODE: The Oxford Dictionary of English (Oxford Dictionaries online). Oxford University Press, Oxford (2005) AND Russell, S.J., Norvig, P., Davis, E.: Artificial intelligence: a modern approach. Prentice Hall, Upper Saddle River (1995).

[2] Self, J.: The defining characteristics of intelligent tutoring systems research: ITSs care, precisely. International Journal of Artificial Intelligence in Education (IJAIEd). 10, 350–364 (1999).

[3] Hill, P. and M. Barber (2014) Preparing for a Renaissance in Assessment, London: Pearson.; DiCerbo, K. (2014). Why an Assessment Renaissance Means Fewer Tests. http://researchnetwork.pearson.com/digital-data-analytics-and-adaptive-learning/assessment-renaissance-means-fewer-tests

What the Research Says about How AI Benefits Education

Thursday 24th March at 1pm
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https://www.eventbrite.com/e/where-is-the-evidence-that-artificial-intelligence-can-benefit-education-tickets-22502360165

Through a mix of presentations and discussion we will explore the evidence about if and how Artificial Intelligence (AI) can support teaching and learning. For those who would like to know more about AI and Education please see the report published earlier this month.

https://www.pearson.com/content/dam/corporate/global/pearson-dot-com/files/innovation/Intelligence-Unleashed-Publication.pdf

Speakers include:
Prof Benedict du Boulay – University of Sussex
Dr Wayne Holmes – Zondle
Dr Kaska Porayska-Pomsta – UCL Knowledge Lab
Prof Gautam Biswas – Vanderbilt University, USA
Dr Manolis Mavrikis – UCL Knowledge Lab

Junaid Mubeen – Whizz Education

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WhenThursday, March 24, 2016 from 1:00 PM to 4:00 PM (GMT) Add to Calendar WhereUCL Knowledge Lab – UCL Institute of Education. 23- 29 Emerald Street . London, London WC1N 3QS GB – View Map

When
Thursday, March 24, 2016 from 1:00 PM to 4:00 PM (GMT) Add to Calendar
Where
UCL Knowledge Lab – UCL Institute of Education 23- 29 Emerald Street , London WC1N 3QS, United Kingdom – View Map

Educational AI can know about teaching, learners, and subjects like math or history.

I said in AI in Education provides smart knowledge modeling tools that AIEd systems build computational models of the process of teaching, the subject matter being studied and of the learner as they progress. The table below provides some examples of the sorts of information that can be stored in the Pedagogical model, the Domain model and the Learner model.

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To delve deeper into just one of these examples, learner models are ways of representing the interactions that happen between the computer and the learner. The interactions represented in the model (such as the student’s current activities, previous achievements, emotional state, and whether or not they followed feedback) can then be used by the domain and pedagogy components of an AIEd programme to infer the success of the learner (and teacher). The domain and pedagogy models also use this information to determine the next most appropriate interaction (learning materials or learning activities). Importantly, the learner’s activities are continually fed back into the learner model, making the model richer and more complete, and the system ‘smarter’.

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This post is an adapted extract from Intelligence Unleashed published by Pearson.

Artificial Intelligence in Education provides smart knowledge modeling tools.

At the heart of AI in Education (AIEd) is the scientific goal to “make computationally precise and explicit forms of educational, psychological and social knowledge which are often left implicit.”  In other words, in addition to being the engine behind much ‘smart’ ed tech, AIEd is also a powerful tool to open up what is sometimes called the ‘black box of learning,’ giving us deeper, and more fine-grained understandings of how learning actually happens (for example, how it is influenced by the learner’s socio-economic and physical context, or by technology). These understandings may then be applied to the development of future AIEd software and, importantly, can also inform approaches to learning that do not involve technology.

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For example, AIEd can help us see and understand the micro-steps that learners go through in learning, or the common misconceptions that arise. These understandings can then be used to good effect by classroom teachers. AI involves computer software that has been programmed to interact with the world in ways normally requiring human intelligence. This means that AI depends both on knowledge about the world, and algorithms to intelligently process that knowledge.

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This knowledge about the world is represented in so called ‘models’. There are three key models at the heart of any AIEd application: the pedagogical model, the domain model, and the learner model. Take the example of an AIEd system that is designed to provide appropriate individualised feedback to a student. Achieving this requires that the AIEd system knows something about:
• Effective approaches to teaching (which is represented in a pedagogical model);
• The subject being learned (represented in the domain model);
• The student (represented in the learner model);

In my next post I’ll provide examples of the sorts of information that can be found in each of these models.

This post is an adapted extract from Intelligence Unleashed published by Pearson.

 

AI used for Education must be driven by teachers not inflicted on teachers

Teachers need to be central agents in deciding how AI is used in education. Perhaps we should talk about EdAI rather than AIEd to make this point. It is teachers who will be the orchestrators of when, and how, to use AIEd tools. In turn, the AIEd tools, and the data driven insights that these tools provide, will empower teachers to decide how best to marshal the various resources at their disposal.

Orchestrator1More than this, though, teachers – alongside learners and parents – should be central to the design of AIEd tools, and the ways in which they are used. This participatory design methodology will ensure that the messiness of real classrooms is taken into account and that the tools deliver the support that educators need – not the support that technologists or designers think they need.

Teachers who take part in these processes will gain increased technological literacy, new design skills, and a greater understanding of what AIEd systems can offer.

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The increased introduction of AI-powered tools will serve as a catalyst for the transformation of the role of the teacher. AIEd is well placed to take on some of the tasks that we currently expect teachers to do – marking and record keeping, for example.
Freedom from routine, time-consuming tasks will allow teachers to devote more of their energies to the creative and very human acts that provide the ingenuity and empathy needed to take learning to the next level.

As this transformation takes place, teachers will need to develop new skills (maybe through professional development delivered through an AIEd system). Specifically they will need:
• A sophisticated understanding of what AIEd systems can do to enable them to evaluate new AIEd products and make a sound judgement about its value to them, and their students
• To develop research skills to allow them to interpret the data provided by AIEd technologies, to ask the most useful questions of the data, and to walk students through what the data analysis is telling them (for instance, using Open Learner models)
• New teamworking and management skills as each teacher will have AI assistants in addition to their usual human teaching assistants, and they will be responsible for combining and managing these resource most effectively

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Most excitingly, with the evolution of the teacher’s role will also come the evolution of the classroom, as AIEd tools allow us to realise what it is unrealistic to expect any teacher or lecturer to do alone. For example, making the positive impact of one-to-one tutoring available to every child, or realising effective collaborative learning (a difficult activity to keep on track without some form of additional support).

 

This post is an adapted extract from Intelligence Unleashed published by Pearson.

Will Artificial General Intelligence (AGI) cope with ‘messy’ real world learning?

I was catching up with reading my weekend papers and came across the Observer Tech Monthly profile of Demis Hassabis, the founder of DeepMind. I have never met Demis, but the Observer piece echoes the descriptions I have been given by those who do know him. He is incredibly bright and also extremely modest: a nice ‘ordinary’ North London guy. I feel comforted that someone like this is at the forefront of our efforts to extend the boundaries of Artificial Intelligence (AI) and the achievements of DeepMind are certainly impressive. However I am less convinced about the real potential of Artificial General Intelligence or AGI, especially when it comes to its application to education.

download2I can buy into the vision of a world where smart people work with smart machines to solve wicked problems, such as cancer. And I can see that there is indeed too much information for many of us humans to process, so some artificially intelligent help would be great. I like the idea that AGI will “automatically convert unstructured information into actionable knowledge…. to provide a meta-solution for any problem” But that’s where it falls down for me, I can’t believe that the structured knowledge will be applicable to any problem.

IMG_8934The reason I hold this view is twofold. Firstly, much of the knowledge that helps us negotiate our way through the world is highly contextualized. There is significant evidence that a learner’s context impacts significantly upon their learning process and that in essence each individual person has his or her own individualized learning context. Secondly, teaching and learning in the real world provides extremely messy data. It’s this very messiness in teaching and learning settings that is crucially important. Partly because one never knows if what appears to be mess is actually important for learning. For example, a disagreement between two children will probably upset at least one of them and that in turn will impact on their learning. I would need to see some clear examples of contextualized AGI (is that a contradiction in terms?) and its propensity for messy real world learning settings to be convinced that AGI for education is a way forward.

hero-learning-pathsIt’s not just DeepMind whose remarkable systems are not to my mind suitable to take over education. I was on a panel with Jose Ferreira CEO of Knewton last month and it became clear that Jose believes that Knewton is smart enough to play the role of a teacher. It certainly is impressive technology. However, Knewton relies upon ‘clean’ data and that is not what classrooms are like. To my mind the most likely outcome for AI within Education is not for AGI, but for AI components to provide teachers with a selection of smart tools that teachers can use with learners as and when they think it appropriate. It really is the smart combination of Human and Machine intelligence that will win the day.

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