We should not fear AI
The promise of technology transforming teaching is almost here, with the winds of change set to create conditions for a sustained and systematic shift
By Phil Richards, Jisc’s Chief Innovation Officer
Sceptics may say they have heard such claims before; this time three new and formidable forces are driving the revolution:
– The internet of things – interconnected physical objects that can exchange data
– Big data – large sets of data that can be digitally analysed to identify patterns
– Artificial intelligence – intelligent, cognitive behaviour by machines
Just as these three modern titans are hitting us in our everyday lives, they are starting to play out in education. As the UK’s digital body for education, we are helping the sector understand and exploit them as our aim is for the UK to be the most digitally-advanced nation in the world – an ambition that is within our grasp.
Learning analytics is gaining traction and moving from hype to reality. We can see universities beginning to equip campuses and lecture theatres with the latest technology and explore the potential of the big data captured for learning to help improve teaching.
The next step is the use of AI in teaching – predictive analytics and adaptive learning to personalise the students’ learning journey. While we are seeing some initial examples, the real promise and capability still lies in the future.
We are all seeing the hype about AI, with machines taking over the world. For some years, future-gazers have talked about the singularity, where machines become more intelligent than us, putting us out to pasture (or worse).
‘I am optimistic that the time is right for technology to play its part in improving teaching in our universities.’
Personally, I am optimistic about the future of humankind and I see AI as just another tool to improve our lot once we have moved through the revolution. It is another step on freeing humans up to be what we are best at.
I’m saying we need not fear AI, we can embrace it – there are many opportunities there, particularly for teaching.
1. Personalising learning
There are already examples of adaptive learning. For example, the virtual learning environment tailoring according to the way students use it, recognising what they are good at and where they need more support or different techniques, and adapting the material and mechanisms to them. So personalised and fitting to the student, rather than shoehorning them to a lecturer’s style.
2. Detecting plagiarism
There are many opportunities for machines to take on the burdensome aspects of teaching. For example, we all know of the plagiarism detection services; it’s only a small step for systems to do a first assessment of assignments, allowing subsequent human review to focus on the inspirational and/or creative elements – and being able truly to delve into them, to be inspired, and to spot the world-changing ones. These aspects will surely remain the human preserve for some time to come.
3. Intervening to help at-risk students
Learning analytics works well on its own. Pairing it with AI could forge a dream combination. A learning analytics system might flag a student who hasn’t attended a lecture or signed in to IT systems for a while as being at-risk. In a blog earlier this year, my colleague Martin Hamilton noted that by adding AI into the mix it becomes possible to make predictions about learning outcomes, retention and attainment, and also to suggest interventions that may help individuals overcome those risks.
4. Are you happy or unhappy about your lecturer
A number of universities are already looking at the possibilities of using video monitoring and webcams along with emotion recognition software to improve the performance of tutors and lecturers.
In learning spaces, disengaged or struggling students could be identified and feedback provided to their tutor or lecturer, possibly in real time. In libraries and learning resource centres systems might recognise confused or distressed students allowing appropriate action to be taken.
So we have the technology – or just about – but, how do we help our lecturers understand it? With all the scare stories about robots taking over the world, they would be forgiven for being wary. But even the willing sometimes need a helping hand.
‘Our aim is for the UK to be the most digitally-advanced nation in the world – an ambition that is within our grasp.’
A key strand of our work in Jisc is to promulgate technology in higher education, ensuring it is done ethically. How do we help our academic colleagues change? Also, where is the drive for new ways of doing things and the courage to make it happen going to come from? Is this a role that we can play?
I haven’t even touched on the capabilities of augmented (AR) and virtual (VR) reality, but we know that these technologies have great applicability in teaching, bringing the theoretical to life. If we embrace these technologies, it can only be for the good of the education sector.
We are seeing teaching gaining an unusual exposure prompted by the debate about value for money from fees. I am optimistic that the time is right for technology to play its part in improving teaching in our universities.
There are many exciting opportunities open to us to give our students the teaching they deserve – digitally enabled, enticing and wisdom-driven.