Calling education: wake up and smell the coffAI, don’t miss a great opportunity to drive prosperity for all

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.

 

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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.

7fba0586036d8b0f2cdf47df1d037557There 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.maxresdefault.jpg

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 shutterstock_260422808.jpgof 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.

 

 

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

Here is what ‘smart’ looks like in an AI tutor

 

So, what would a piece of education technology driven by AIEd look like? Here is a simplified picture of a typical model-based adaptive tutor.

ModelBasedTutor

 

It is based on the three core models as described above: the learner model (knowledge of the individual learner), the pedagogy model (knowledge of teaching), and the domain model (knowledge of the subject being learned and the relationships between the different parts of that subject matter). AIEd algorithms (implemented in the system’s computer code) process that knowledge to select the most appropriate content to be delivered to the learner, according to their individual capabilities and needs.

While this content (which might take the form of text, sound, activity, video, or animation) is being delivered to the learner, continuous analysis of the learner’s interactions (for example, their current actions and answers, their past achievements, and their current affective state) informs the delivery of feedback (for example, hints and guidance), to help them progress through the content they are learning. Deep analysis of the student’s interactions is also used to update the learner model; more accurate estimates of the student’s current state (their understanding and motivation, for example) ensures that each student’s learning experience is tailored to their capabilities and needs, and effectively supports their learning.

Some systems include so-called Open Learner Models, which present the outcomes of the analysis back to the learners and teachers. These outcomes might include valuable information about the learner’s achievements, their affective state, or any misconceptions that they held. This can help teachers understand their students’ approach to learning, and allows them to shape future learning experiences appropriately. For the learners, Open Learner Models can help motivate them by enabling them to track their own progress, and can also encourage them to reflect on their learning.

One of the advantages of adaptive AIEd systems is that they typically gather large amounts of data, which, in a virtuous circle, can then be computed to dynamically improve the pedagogy and domain models. This process helps inform new ways to provide more efficient, personalised, and contextualised support, while also testing and refining our understanding of the processes of teaching and learning.

In addition to the learner, pedagogical, and domain models, AIEd researchers have also developed models that represent the social, emotional, and meta-cognitive aspects of learning. This allows AIEd systems to accommodate the full range of factors that influence learning. Taken together, this set of increasingly rich AIEd models might become the field’s greatest contribution to learning.

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

Annotation may be old-fashioned, but it is effective for learning and can be supported with technology

5510a4270443ecab4f9b41f11c9e8152When I was at school and university I spent a lot of time taking notes. These notes were sometimes much better than others, but they were always invaluable for revision – even if only to identify the gaps in my knowledge.

I still take notes, in meetings and at talks, for example. Increasingly these notes are technology based and much more structured than those of my youth. In fact, part of the reason I blog is to help myself remember key findings and issues as well as to flag them up for others. Another of the 19 learning acts that I have been discussing is:

Annotation. This can be described as an interaction with existing knowledge material (particularly text) such that the learner is able to construct a personal elaboration of that material. This might take the form of elaborative commentary closely attached to the original (“marginal notes”) or it might be a form of personal précis or reflection (“summarising” etc.). Knowledge is thereby elaborated or personalised.

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This practice suits situations where a body of well-formed material is available for study. Its cognitive benefit arises from the effort of actively re-casting material and selectively linking it with existing knowledge. Clearly annotation is something that can be done more or less skilfully and demands practice and support.

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Technology can structure annotation as well as providing an infrastructure for composition and storage. There are many tools for writing and filing the results of annotation and a whole range of practices for organisation.

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.

Strategic browsing can support learning, but many learners do not have the requisite skills

I want to return to the 19 Learning Acts that have been observed to occur when students are learning with digital media and to consider the act of Browsing.

A while ago I wrote about the OECD report into 15-year-olds’ educational attainment in maths, reading, science and digital skills. The negative message from this report was that less use of the internet is linked to better reading performance and frequent use of technology in school is linked to lower performance. BUT digging deeper into the data revealed that what students most commonly do with computers is dominated by browsing the internet, with 42% of students doing this once a week or more. When students did schoolwork at home, once again browsing was the most popular activity.

So what is browsing and how can it be more effective?

Browsing is an interaction with knowledge structures whereby the learner searches for relevant items. This search may be improvised and opportunistic: this is the more traditional sense of “browsing” OR it can be guided by principled rules in which case we would call it “strategic” browsing. It is fine for learners to take part in some exploration through unguided browsing, but it is essential that educators also help learners to develop the skills necessary to become more strategic.

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The sorts of skills needed can be found in many accounts of 21st Century skills,  for example, that proposed by the World Economic Forum. These include critical thinking and persistence, both of which would help learners to be more strategic in their browsing, particularly if we add in a good dose of meta cognition. Critical thinking and meta cognitive skills can be taught and they have been show to support learning.

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