Is it possible we’re about to see a big change in the way we approach curricula design? In a world where Artificial Intelligence (AI) machines can access, deliver and learn an almost infinite amount of information in a matter of milliseconds, does it really make sense to be teaching students facts and figures?
The ascent of AI in education is signalling a shift from knowledge-based curricula towards intelligence-based curricula. This post explores the differences between both perspectives and what this will mean for how we approach curriculum design in the future.
Knowledge vs intelligence
- facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
- the ability to acquire and apply knowledge and skills.
In traditional knowledge-based curricula, students are taught and tested on facts, figures, content and ideas. In an intelligence-based curriculum, however, the onus is on preparing students to think critically, creatively and inquisitively about the information they acquire.
A movement towards intelligence-based curricula makes sense in a world where AI is able to carry out an increasing number of repetitive and menial tasks, as well as process and provide us with an overwhelming amount of data. The ability to retain and access information becomes increasingly redundant as technology leaves us in its dust.
As a result, we should be looking at how we educate ourselves and our learners to contribute in an AI-rich economy. This mea
ns shifting the focus to intelligence – including emotional intelligence, creativity, and critical thinking skills.
But is a purely intelligence-based curriculum really a good idea? And how might it affect fundamentally knowledge-based subjects like ELT and other modern languages?
The big curricula debate
Dr. Rose Luckin, a professor of learner-centred design at UCL, argues that knowledge-based curricula taught in schools today are shallow and outdated.
Effectively, they teach students finite modules of information, leaving them to compete with machines that can find, learn and recall facts much faster than they can. This means that, if we value simple knowledge over the treatment of that knowledge and our abilities to analyse it, workplace AI machines will outrank us, outmanoeuvre us and eventually make us all but redundant.
The alternative, she says, is to teach how to be reflective about knowledge through a process she dubs “meta-knowing”. Instead of, for example, asking how plants convert light into food through photosynthesis, students should be taught to ask how we know it, and why it is important – something machines are less likely to be able to do.
On the other side of the debate, there are those that argue that AI will not make knowledge-based curricula obsolete. Carl Hendrick, Head of Research at Wellington College, argues that, while it is indeed important to teach students how to think about and question information, knowledge itself is a fundamental part of the learning process.
He cites a recent issue with a GCSE English exam where some students misunderstood the word “vocation”, thinking it meant “vacation”, rendering their answers to a particular question irrelevant and incorrect. A lack of concrete knowledge contributed directly to their failure.
Hendrick’s argument is that – for the most part – schools already successfully combine teaching knowledge alongside intelligence-based skills. He suggests that schools do not simply pump out facts for students to memorise, without giving them the critical thinking tools to use what they are learning.
Both Luckin and Henrick make important points. If people are to succeed in the workplace, they will almost certainly need to develop critical thinking skills based around AI-facilitated insights.
However, as Henrick shows, these skills are useless in isolation. In essence, he argues we need to be able to connect what we are experiencing to established knowledge in order to be able to ask the right questions, produce an effective analysis, or to understand and use it.
How to prepare for a world of work facilitated by AI
Perhaps a healthy approach is to think of AI as a means of providing a competitive advantage, rather than as the competition. People who have been taught how to work with advanced AI will not only have an incomprehensible volume of information at their fingertips, but they’ll know how to use it effectively.
AI will replace slow research tasks, take over mundane administration, allowing us to be more creative, more innovative and far more productive – if we use it correctly. That is, if it doesn’t annihilate us in the process.
How to bring this into ELT
Currently, ELT is primarily a knowledge-based subject, and until technology renders language learning obsolete (and we don’t foresee that happening anytime soon), students have to make the effort to build their understanding and use of a language.
Will they be able to compete with the speed of an AI translation machine? No.
But, they will be able to participate in culture, conversation and communication – and be richer for it.
Luckin’s notion of meta-knowing also comes into play here. Encouraging students to imagine situations in which they will need a particular language skill can help students understand why they need to learn it and why it is important.
By teaching emotional intelligence (EQ) in class, teachers can foster the skills students will need in the modern workforce. The ability to express emotion and empathise has always been important – but as jobs become less routine, more analytical, creative and team focused, it will become paramount. Those with strong interpersonal skills will thrive.
ELT learner experience designers might look at including activities that get students reflecting on their progress, or on their feelings and those of others. For example, product teams might consider the following aspects of EQ:
- Self-awareness, an intrapersonal intelligence that involves identifying and understanding our own feelings.
- Empathy, an interpersonal intelligence that involves understanding the feelings of others.
- Sensitivity to others, which is a step on from empathy, means being able to respond to others appropriately.
- Intrinsic motivation, which involves the ability to self-start and push on through adversity.
Do you think AI is going to affect your teaching for better or worse? Join the debate and leave us a comment below.
Read more about artificial intelligence in our post AI: a primer.