
The original Blade Runner was released in 1982. It depicts a future in which synthetic humans known as replicants are bioengineered by a powerful Corporation to work on off-world colonies. The final scene stands out because of the “tears in rain” speech given by Roy, the dying replicant.
I’ve seen things you people wouldn’t believe. Attack ships on fire off the shoulder of Orion. I watched C-beams glitter in the dark near the Tannhäuser Gate. All those moments will be lost in time, like tears in rain. Time to die.
This was the moment in which the artificial human had begun to think for himself. But what makes this so relevant is that the film is predicting what life will be like in 2019. And with 2018 only a few days away, 2019 is no longer science fiction, and neither is Artificial Intelligence (AI).
Artificial Intelligence and machine learning
There is no one single agreed upon definition for AI, “machine learning” on the other hand is a field of computer science that enables computers to learn without being explicitly programmed. The way it does this is by analysing large amounts of data in order to make accurate predictions, for example regression analysis does something very similar when using data to produce a line of best fit.
The problem with the term artificial intelligence is the word intelligence, defining this is key. If intelligence is, the ability to learn, understand, and make judgments or have opinions based on reason, then you can see how difficult deciding if a computer has intelligence might be. So, for the time being think of it like this:
AI is the intelligence; machine learning is the enabler making the machine smarter i.e. it helps the computer behave as if it is making intelligent decisions.
AI in education
As with many industries AI is already having an impact in education but given the right amount of investment it could do much more, for example
Teaching – Freeing teachers from routine and time-consuming tasks like marking and basic content delivery. This will give them time to develop greater class engagement and address behavioural issues and higher-level skill development. These being far more valued by employers, as industries themselves become less reliant on knowledge but dependant on those who can apply it to solve real word problems. In some ways AI could be thought of as a technological teaching assistant. In addition the quality and quantity of feedback the teacher will have available to them will not only be greatly improved with AI but be far more detailed and personalised.
Learning – Personalised learning can become a reality by using AI to deliver a truly adaptive experience. AI will be able to present the student with a personalised pathway based on data gathered from their past activities and those of other students. It can scaffold the learning, allowing the students to make mistakes sufficient that they will gain a better understanding. AI is also an incredibly patient teacher, helping the student learn from constant repetition, trial and error.
Assessment and feedback – The feedback can also become rich, personalised and most importantly timely. Offering commentary as to what the individual student should do to improve rather than the bland comments often left on scripts e.g. “see model answer” and “must try harder.” Although some teachers will almost certainly mark “better” than an AI driven system would be capable of, the consistency of marking for ALL students would be considerably improved.
Chatbots are a relatively new development that use AI. In the Autumn of 2015 Professor Ashok Goel built an AI teaching assistant called Jill Watson using IBM’s Watson platform. Jill was developed specifically to handle the high number of forum posts, over 10,000 by students enrolled on an online course. The students were unable to tell the difference between Jill and a “real” teacher. Watch and listen to Professor Goel talk about how Jill Watson was built.
Pearson has produced an excellent report on AIEd – click to download.
Back on earth
AI still has some way to go, and as with many technologies although there is much talk, getting it into the mainstream takes time and most importantly money. Although investors will happily finance driverless cars, they are less likely to do the same to improve education.
The good news is that Los Angeles is still more like La La Land than the dystopian vision created by Ridely Scott, and although we have embraced many new technologies, we have avoided many of the pitfalls predicated by the sci-fi writers of the past, so far at least.
But we have to be careful watch this, it’s a robot developed by AI specialist David Hanson named “Sophia” and has made history by becoming the first ever robot to be granted a full Saudi Arabian citizenship, honestly…..

The title of this month’s blog is not mine but taken from what many would consider a classic book about what can realistically be achieved by someone stood at the front of a classroom or lecture theatre, simply talking. Written some 25 years ago but updated recently Donald A. Bligh’s book takes 346 pages to answer the question, 

This month’s blog is coming from Malaysia, I have been presenting at the ICAEW learning conference in KL. The only relevance of this, is that as with any lecture/presentation or lesson you have to put yourself in the shoes of your audience and ask, what do they want to get out of this, why are they giving up their valuable time and in many instances money to listen to what you have to say?
I have to admit in the last few months I have spent a fair bit of time looking into the facts behind the EU and checking on some of the statements made by both the remain and leave sides, attempting to discover truths or otherwise so that I could make a more informed decision. It proved difficult; much was opinion dressed up as fact by using numbers open to interpretation. Another technique used on the face of it to offer clarity, but in reality did just the opposite, was to state the “facts” forcefully, with conviction and repeat them often, giving the impression that what was being said was not only true but believed to be true.
Step one in Kolb’s learning cycle is to have the experience. Step two, reflect, think back on what we have experienced. Step three, conceptualise, generate a hypothesis about the meaning of the experience, what is it we have learned, and step four, test that the hypothesis is supported by the experience, does it confirm that what we have learned is correct.




















