Artificial Intelligence for Learning

How to use AI to Support Employee Development

Donald Clark

• Publisher: Kogan Page
• Year: 2020
• ISBN:
  9781789660821
• Edition: 1
• Topic: Business
• Subtopic: Human Resource Management
• Index: Business

Artificial intelligence is creating huge opportunities for workplace learning and employee development. However, it can be difficult for L&D professionals to assess what difference AI can make in their organization and where it is best implemented. Artificial Intelligence for Learning is the practical guide L&D practitioners need to understand what AI is and how to use it to improve all aspects of learning in the workplace. It includes specific guidance on how AI can provide content curation and personalization to improve learner engagement, how it can be implemented to improve the efficiency of evaluation, assessment and reporting and how chatbots can provide learner support to a global workforce. Artificial Intelligence for Learning debunks the myths and cuts through the hype around AI allowing L&D practitioners to feel confident in their ability to critically assess where artificial intelligence can make a measurable difference and where it is worth investing in. There is also critical discussion of how AI is an aid to learning and development, not a replacement as well as how it can be used to boost the effectiveness of workplace learning, reduce drop off rates in online learning and improve ROI. With real-world examples from companies who have effectively implemented AI and seen the benefits as well as case studies from organizations including Netflix, British Airways and the NHS, this book is essential reading for all L&D practitioners needing to understand AI and what it means in practice.

PART ONE

Introduction

01

Homo technus

AI has and will continue to change many areas of human endeavour. Almost everything we do online is mediated by AI: search through Google; social media, whether Facebook, Instagram, or Twitter; buy something on Amazon; entertain yourself through Netflix – all are mediated by AI. AI now touches almost all global, online services. Perhaps the only online sector that is not yet mediated by AI is learning.

The nature of work is also being shaped by AI, not only in the automation of manufacturing and warehouses, but also in our homes, offices and services. This change in the workplace, in itself, will surely have a profound effect on what we learn, why we learn and how we learn. This is already changing through AI-driven, online learning.

Technological revolutions

Technological revolutions are not new. We as a species have shaped and been shaped by technology, from the first intentional use of stone hand axes to artificial intelligence. There has been a relentless rhythm to this progress.

The problem with most descriptions of this technological progress is that they focus on the physical technology itself, stone tools (Stone Age, Neolithic), metals (Bronze Age, Iron Age), age of steam engines, railways, mass production, computers (Industrial Revolutions). We see this when AI for learning is couched in terms of the ‘Fourth Industrial Revolution’ (Seldon and Abidoye, 2018), which is neither the fourth nor industrial.

A far better lens through which to look at AI in learning is not in terms of industrial revolutions, but cognitive revolutions. It is more revealing to see AI in terms of those revolutions in learning technology, such as language, writing, alphabets, printing, the internet and now AI. Our physical technology is underpinned, supported and created by psychological technology that enables its very conceptualization, design, development and delivery. The stone axe was imagined, shaped and used by minds. Cave paintings were the product of sophisticated imaginations. Clay tablets, papyrus, manuscripts and the entire technology of writing were a psychological breakthrough that externalized and archived thought for others to access. Printing gave rise to the scientific revolution, the Reformation and the Enlightenment. The internet, more specifically the web, gave us global access to knowledge. Now we have AI, the next technological leap, again a product of pure psychological endeavour.

Technology that enables learning is often overlooked when the history of technology is written. It is all too easy to focus on the physical objects. But without learning technologies, no other technologies would have developed. We are the species that ‘learned’ faster than the others. Our evolution as a species over the last few million years has been one of learning to adapt. It is this that has given us global dominance, allowing us to walk on the moon and reach out beyond our solar system.

Without the ability to shape stone tools we would never have avoided predators, sought out prey and become that dominant species. Prehistoric technology like pointed axes allowed us to kill, crush, scrape and cut. With bone needles we could dress ourselves efficiently, with pots cook and with axes chop down trees for fuel.

We are called Homo sapiens but our genus ‘Homo’ emerged with the appearance of Homo habilis (handy man). They were so described because of their association with stone tools, but recent evidence has shown that tools were used by previous species. We are, more accurately, Homo technus, the species that uses tools and technology, both physical and, more importantly, psychological.

In addition to physical tools we are the masters of symbolic tools. It is difficult to see language as a tool, but if we define technology as something that exists outside of ourselves, that we create to exist outside of our minds and bodies, to enhance us psychologically and physically, then language is a technology. We create sounds that exist separate from us, travel to others across distance to be heard by others. It is the mainstay of communication, whether face-to-face, across the globe by telegraph then telephone and now face-to-face online. Voice underpins all other forms of technology and is being embedded in the powerful and personal mobile devices we have in our pockets, as well as in our homes, as the way of controlling the internet of things.

From Gutenberg to Zuckerberg, language, writing, printing, distribution, communication and sharing came together on the back of the internet in the form of the World Wide Web. The global scale and cumulative effect means it may prove more disruptive in the long term than all that went before. Some compare now to the 1930s, but truer historical comparison would be the 15th and 16th century, when printing shook the world. The internet has unleashed forces we are still struggling to understand. This deep tectonic shift in technology is still in its infancy. The same creative and destructive forces are being unleashed as were with writing and printing, and AI has given them a new impetus.

As smart AI technology emerges, technology challenges and, in some cases, supersedes human competences. We enter another unpredictable phase of technological change. This, some argue, is an existential threat. Whatever it turns out to be, it is certainly changing the very nature of work. For all its dangers, AI will therefore certainly shape, in some form, how we learn. Unlike speech, writing, printing and the internet, this is software that matches, and in some cases even more than matches, us as humans.

Daniel Dennett, philosopher and polymath, in From Bacteria to Bach and Back, attempts a synthesis of human evolution and AI (Dennett, 2017). Just as the Darwinian evolution of life over nearly 4 billion years has been ‘competence without comprehension’, the result of blind design, what Dawkins called the ‘blind watchmaker’, an invisible process that drove biological evolution, so cultural evolution and AI is often competence without comprehension (Dawkins, 1996). His vision, which has gained some traction in cognitive science, is that the brain is a prediction machine, constantly modelling forward. He also sees cultural evolution as the invasion or infection of the brain by memes, primarily words. These informational memes, like Darwinian evolution, also show competence without comprehension and fitness in the sense of being advantageous to themselves.

His hope is that machines will open up ‘notorious pedagogical bottlenecks’, even ‘imagination prostheses’ working with and for us to solve big problems. We must recognize that the future is only partly, yet largely, in our control. Let our artificial intelligences depend on us even ‘as we become more warily dependent on them’.

Technology comes in revolutionary waves that have disruptive effects. It is combinatorial and cumulative, building upon previous revolutions, not something separate from us but ultimately a dialectic or an accommodation between it and we humans. We must also recognize that technology is almost always a double-edged sword that needs to be overseen and controlled, so that technology for good overcomes technology for evil. At heart these technologies define what we must learn and how we learn. They transform learning. We are Homo technus.

Culture

To understand AI in learning, one also has to dig deep into our cultural history, be almost archaeological, to uncover the historical paths that gave rise to AI. Eliezer Yudkowsky is right to warn us that ‘by far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it’ (Yudkowsky, 2015). This means understanding where it came from and how we got here.

AI has its origins in Greek philosophy and mathematics. It has also been interpreted for centuries by culture: poems, plays and novels. In addition to these ancient and older origins, a more recent art form, the movies, has almost defined AI in the modern mind.

Culture can illuminate, but also mislead, and there is no more misleading cultural forms than those that deal with AI. AI has been shown to us in Western culture largely through dystopian theatre, literature, then movies, with endless re-treads of the Prometheus (Frankenstein) myth. This has distorted thinking around AI for learning, but we do have something to learn from its presentation in culture, and in movies in particular.

From Aeschylus’s Prometheus Bound to Mary Shelley’s Frankenstein: The Modern Prometheus, the creation of a monstrous force took hold of the popular imagination, a myth fed straight into the movies in the 20th century. Asimov’s novels have provided the famous Three Laws of Robotics in his short story, Runaround (1942), and there is a slew of modern novels about AI that have started to appear. Typical is Ian McEwan’s Machines Like Me (2019), still stuck in the Mary Shelley Frankenstein myth, with Turing as the gratuitous Frankenstein.

Over the last 100 years, from Metropolis (1927) through the lens of movies, AI has largely been portrayed as dystopian and evil. AI has, in film, reflected our fears, often representing the fear of technology but also of the ‘other’, whatever that ‘other’ was at the time – the Cold War, crime, violence, helplessness, corporate greed, climate change and so on. There have been glimpses of a more sophisticated and subtler dynamic around AI, in Blade Runner and more recently a rush of movies around AI, as it takes hold in our lives through the internet.

There are several movie themes that have shaped the common perception of AI, primarily as robots. AI will lead to robots that will turn on us and kill us all. AI will take over the internet and kill us all. AI will fool us into thinking it is good but it is bad. This is similar to the popularity of child characters in horror movies, where our creations, our children, become our worst nightmares. These are variations of the Prometheus myth.

Technology is always ahead of cultural commentary. It will always be thus. Only now, over these last few years, as AI becomes operative in many domains, is it receiving subtler cultural appreciation and critiques, rather than robot fantasies.

Philosophy and mathematics

Technology is not a ‘black box’, something separate from us. It has shaped our evolution, shaped our progress, shaped our thinking and it will shape our future. There is a complex dialectic between our species and technology that is far more multifaceted than the simplistic ‘it’s about people not technology’ trope one constantly hears.

Descartes (1641) saw the body as a machine; others see Leibniz (1666) as the true progenitor of AI, with his theory that language mirrors thought and a universal language may be written that manipulates symbols representing concepts and ideas using logic to simulate reasoning. Note also that Descartes and Leibniz made significant contributions to mathematics, in algebra, geometry and calculus, influencing AI in other, more purely mathematical ways.

In the 20th century, Sartre in Being and Nothingness (1956) and Heidegger in The Question Concerning Technology ...