Tax the Robots


Is this going to be our future?


This is part 1 of a series of blogs on technology, AI and the implications and possibilities for education. Lockdown and the isolation COVID-19 brings has proved we don’t have all the knowledge, there is still much to be learned about us and the world we inhabit. As a teacher, trying to find a way through the haze and confusion, this process has planted a seed in my mind I would like to cultivate. Does technology have the capacity and potential to save humanity?

I appreciate it’s a vast and profound question so I am going to stick to what I know: education. Think of these blogs like a thought experiment. As part of investigation I want to explore your thoughts and ideas around some of the challenges and barriers surrounding the very technology that has been designed to liberate us. I welcome you to contribute comments, challenge questions and share your thinking to build a dialogue around these topics. Much like the robots that feed on the statistics we supply to them, I would like to devise my own algorithms and tap into your biological (human not robotic) minds.

What is EdTech?

EdTech, short for Educational Technology carries vast potential for how we learn and consume knowledge. Trends around progression and AI dominate our social feeds, often laded with bombastic statements about how we might ‘know better’. The recent COVID-19 lockdown forced the world to educate remotely, relying on technology as the mechanism to teach.

But, is the education sector ready for this change?

We live in a complex yet connected world. Technology is the perfect paradox, it can connect us and isolate us. In March this year, 849 million learners felt this when the world shut down. Students and teachers found themselves in an uncharacterised territory ‘working together remotely’.

In this blog series I explore the hyperactive EdTech sector of the education and weigh up the view that EdTech might be a solution without a problem. Do we need learning algorithms, isolation bots and hologram teachers, or, are we OK as we are? Will technology collapse under the weight of its own hype? And, most importantly, should we tax the robots? So, let’s start with a look at knowledge through the lens of the homo sapian, automation and hybrid (cyborg) minds.

Human Knowledge

The view that technology has shaped the way we ‘know’.

Knowledge is information we store in our heads, then apply to situations (exams, solving problems or a pub quiz). As a species, what we know grows in relation to the ever changing world around us. Knowledge is universal and doesn’t belong to individuals but to society and groups of people. This knowledge can be expressed through culture and is passed down from generations. Robots and specifically AI (Artificial Intelligence) accumulate knowledge in a similar way but yet many of us remain cautious about the role AI could play in occupying knowledge in the future. This seems hypocritical given that most of us more than happy to let our personal devices store personal data that we would rather offset.

Our education systems are built on knowledge.

Sugar Mitra through his work on SOLE (Self Organised Learning Environments) popularised the idea that knowing is obsolete. He justifies this bold claim by reminding us that facts have no purpose when we live in a world where we can just ‘look it up’. He offered an interesting belief that ‘every switched off device is a switched off child’.

So what is the real value of knowledge anyway?

E.D Hirsch (1988) put forward the idea that educators should not treat reading and writing as empty skills independent of specific knowledge. Furthermore, Hirsch believed the documented decline in shared knowledge was fundamentally attributed to the popular view that progressive ideologies outweighed the importance of teaching knowledge through facts.

Christodoulou (2013) a modern advocate of Hirsch’s work and an influential author in UK education arenas supports the view that students require knowledge of a specific field before they can create and innovate in it. To extend and deepen this idea, she points to evidence-based practice in the fields of educational neuroscience, specifically long term memory (LTM) retention. On the question of how facts as forms of knowledge strengthen the LTM, her rationale centres on the notion that knowledge stored in the LTM frees up the pressures and limited space in the working memory (Kirschner et al 2006 and Willingham 2019). Christodoulou also offers a fresh stance on the knowledge through facts argument by highlighting the role deliberate practice of factual retention (Ericsson, 1993) plays in developing LTM as a knowledge resource – in other words that rote learning of facts contributes to the store of what we can call ‘knowledge’ as something which is somehow greater and more cohesive than the facts themselves.

In her latest book ‘Teachers vs Tech’, Christodoulou illustrates why we need real knowledge to think. Her example below shows us that facts are ways to store and articulate knowledge and help us solve problems. Have a go:

I believed him when he said he had a lake house, until he said it’s only fourty feet from the water at high tide (Willingham, D.T., 2009 P.24)

To the knowing, we instantly recognise that lakes don’t have tides, so the statement must be false. Then as the mind conceptualises you use facts to make inferences, perhaps he is boastful, partial to hyperbole and clearly trying to impress others with his statement. If you have the knowledge of tides, you can do this and power to you. If you don’t make the tide connection and don’t posses this knowledge, then the statement would be taken at face value.

Perhaps we should place more value on how we use knowledge, rather than how we store it. Technology allows us to offset knowledge increasing our ‘usage capacity’. This is an interesting concept, one which separates us from our ancestors but connects us closer to a new threat, or opportunity: Robots.

Offsetting knowledge to the Robots

Pepper the Robot

We don’t need to store knowledge exclusively in our heads, devices can take some of the load. For example, think about how mobile phones store contacts and phone numbers. One could argue that speed dials and storing these digits remotely replaces the need to remember them. The same could be said for SatNavs to take over directions, calendars to store meetings, birthdays and events. We have come to rely on offloading this knowledge as a convenient truth, something we don’t need to afford mental capacity to anymore. As a result, we have external devices to store the knowledge our long term memories would rather not.

What separates us from the bots is our biological brains, specifically our ability to be empathetic. Things are changing fast in the AI world and a recent Google Translate development is worthy of a mention here. Google Translate is getting brainier. The online translation tool recently started using a neural networks to translate between some of its most popular languages – and the system is now so clever it can do this for language pairs on which it has not been explicitly trained. To do this, it seems to have created its own artificial language.

Google translate, like Google maps and Earth is an amazing technological advancement. I was lucky to meet ‘Pepper’ the robot last year at a STEM Fair.

Standing 4ft tall this prototype bot could speak several languages fluently, answer most question asked of it (akin to ‘hey Google’) and it even gave me a hug. Perhaps the bots are learning how to be human after all. I have to confess the hug felt robotic and programmed but a sign that robot brains are being wired to connect on a human level perhaps?

The new digital assistant?

Alex Beard specialises in the role AI plays in education and society. In his recent BBC 4 radio series The Learning Revolution, Beard discusses the role digital assistants will play in our future. He proposes that most children will have digital assistants like Pepper and gives a vivid example of a young girl living in the year 20100. Her digital assistant is used to offset knowledge, help her make decisions and to check the validity of data she encounters whilst learning.

How would you feel about your kids having access to a Pepper in their lives?


Cyborgs are defined as a fictional or hypothetical person whose physical abilities are extended beyond normal human limitations by mechanical elements built into the body.

It’s easy to associate cyborgs with images from the Terminator as terror yielding threats to humanity, but in doing so we overlook the potential they might have. Furthermore, A cybernetic organism or ‘cybor’ in IT is defined as an organism with both biological and technological components. In a technical sense, humans can be seen as cyborgs in various types of situations, including the use of artificial implants.

Cyborgs go beyond even postmodern thought to what Garoian (2001) calls posthuman. Post-human thinks of the body as the original prosthesis we all learn to manipulate. Garoian (2001) in response to Prenky’s digital natives paper, says in effect that human identity is not replaced by technology but rather that technology has become a further extension of human expression just as our physical body expresses our mind. Dallas McPheeters (2010) introduces the notion of a Cyborg Learning Theory. Can humans learn to think like machines? Can we use algorithms to enhance our existing knowledge?

Derek Haoyang Li, founder of Squirrel AI promotes the idea that AI can help us develop our capacity to use knowledge effectively. Squirrel AI’s ‘adaptive learning platform’ powered by its proprietary AI-driven adaptive engine and custom-built courseware does just this. The technology now exists! The Squirrel AI Learning platform provides students with a supervised adaptive learning experience that has been proven to improve both student efficacy and engagement across its online learning platform and offline learning centres.

The relationship between knowledge components is connected into a graph structure. Then according to the real learning data of the users, the relationship between the knowledge components in the knowledge graph is iterated until the knowledge graph becomes stable (Li, 2020).

Final thoughts

To conclude this post, technology is the road map to an uncertain future. Knowledge can be human, non-human (artificial) and cyborg. Technology has the potential to unite us or be decisive. Access to devices and the technology accessible is getting easier and cheaper. Technology is shaping the way to view and store knowledge.

Are we a step closer to accepting robots as legitimate members of society?

If so, will robots be able to vote? Be in a relationship? Should we tax them?

A blog is a platform from which ideas can catch the train of sustained conversation to the collective educational space of the world’s classrooms and find new life in other people’s hands.  So please participate:  bring your ideas to the blog, and/or elaborate on or give further substance to the ideas that are already here. Please engage via the comments section below or via our social media pages.

Next time

In the next post I will explore the Digital Natives vs Digital Immigrants debate and explore what is meant by the terms warm and disruptive technologies.

References & further reading

Beard, A., 2020. The Learning Revolution. BBC Radio 4

Christodoulou, D., 2020. Teachers vs Tech: The case for an ed tech revolution. Oxford University Press

Hirsch, E.D., 1988. Cultural Literacy: What every American needs to know

McPhetters, D., 2010. Cyborg Learning Theory: Technology in Education and the blurring of boundaries. World Futures Review. Vol.2(6)

Mitra, S., 2017. Child Driven Education and SOLE (Self Organised Learning Environments).

Presnky, M., 2001. Digital Natives, Digital Immigrants. On the Horizon. Vol.9(5)

Willingham, W., 2019. Ask the Cognitive Scientist: Should Teachers Know the basic Science of How Students learn? Blog post:

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