Unleashing Creativity with Generative Learning: The Future of AI

In recent years, AI has rapidly evolved and transformed industries and generative learning is at the forefront of this revolution. This cutting-edge approach has shown great potential in various fields such as natural language processing, computer vision, and more. In this article, we'll explore the principles behind generative learning and how they're changing the game for AI. Whether you're an expert in AI or a newcomer to the field, we invite you to join us on this exciting journey to discover the future of AI!

What is generative learning?

It’s easy, in learning, to put the onus on the teacher to drive home a concept to their student, but we often disregard how the engagement of the student with their education is equally important when it comes to making their new knowledge stick as a permanent boon, rather than just content they’ll remember briefly and forget later. This is where generative learning comes in; generative learning is a type of learning whereby the learner is expected to produce something independent of the educator. This can be represented in many different ways, but to give a quick example, if someone was learning from a video, rather than just watching the content, moving on and hoping to remember, using the generative learning model, they might instead watch the video and write their own summary, draw a diagram or visual aid… Essentially, a productive engagement that the user “generates” themselves can be considered the use of the generative learning process, which is a tested and proven way of consolidating knowledge and new information.

We can think of generative learning as the equivalent of other philosophies in situations where we need a background before diving right in, such as “try before you buy” in the case of marketing. Whether we’re putting an emphasis on using it or not, it’s always going to be present in learning to some extent; it’s like the liminality of learning or the transitional stage of a journey. To give yet another example, it’s a bit like the vehicle you use in a journey, the journey itself being learning in this case, which can be accomplished on foot but is much easier with a faster vehicle. Without using generative learning, we can think of ourselves as travelling without such a vehicle.

Examples of generative learning?

Let’s go into more detail about the different ways of using generative learning that we know of. If you’ve been in the education system in recent years, you’ll likely remember doing these things in your classrooms, or as assignments and homework. A prime example of generative learning is brainstorming before diving into a new concept. A teacher might introduce a new topic or idea to their class, but before explaining any further, they will ask something like this, “What do you already know about [topic]?” or, “What do you think of when you hear the word [topic]”? These simple questions will get the learner thinking about their own experience with the topic and allow them to figuratively prime their canvas upon which they will paint their new learning experience. A common follow for this task is asking the class to turn and discuss their ideas with a partner, which allows the learners to build upon each other's ideas and also provides an instant conversational use for any information they already have, which will help anything they might learn stick with them.

Another popular and common example of generative learning is the good old mind map. When we only rely on our thoughts, information, especially information we are unfamiliar with, might seem disjointed, dissonant and hard to piece together in a meaningful way. But by writing down our thoughts and drawing visible connections we can make between them, it can be very insightful as to how different aspects and components of a given subject interact with each other to form our understanding of the subject as a whole. Let’s imagine we were learning about a complicated gestalt like inflation in economics, it’s hard to visualise using our minds alone how such a situation could arise in the economy, but if we take to paper and draw out the contributing factors in a mind map, we might be able to better understand the conditions which create inflation, how they might contribute to each other in ways we didn’t notice before. Using inflation as an example, we might even identify a cycle or pattern to inflation that we wouldn’t have noticed without gathering our thoughts into a diagram. I think it's clear to see why this process of creation and discovery from the learner themselves might provide more value than a bland explanation from someone who already has the foundations to understand the topics that their students might not.

Where did generative learning come from?

The coinage and proposal of the term “generative learning” are attributed to Merlin Wittrock, an American psychologist, who is known for his work on this theory which he published in 1974. He was the first to properly identify that learning was a generative process, where the learner themselves was building something, and therefore just as much of the process had to be undertaken by them as their educator if they were to genuinely benefit from it. He called the relationships between the learner and the material they were studying: “semantic and distinctive idiosyncratic associations between stimuli and stored information”, which is to say that each learner learns in their own way and that the connections they make and therefore their understanding is unique to them. This further implies that knowledge is something that the learner is building, rather than something that can just be placed in their minds by someone else.   His ideas lead to the further exploration of educational psychology, some of which we discuss in the articles “how the brain thinks” and “how the brain learns”.

Generative learning theory

It's proven that generative learning boosts the rate of learning and perhaps just as importantly the retention of information, but why is this the case? The reasons are multitudinous. One of the most obvious reasons that this model is effective is because, unlike passive learning, at the very least, generative learning forces the learner to pay attention, since it’s not really possible to create a response without thinking about it first. Even for a student whose mind is usually elsewhere while listening, responding to a question, drawing a diagram, talking to a peer… All of these actions require the participant to focus their mind on a task.

The other key reason is that generative learning creates an investment from the learner as well as the teacher. That is to say that the student, who might otherwise have felt bored or uninterested in a subject that has little to do with them, would form an attachment to the subject matter by means of their own input, which can be looked at as the “investment” in this case. This is the reason that art courses, for example, aren’t just about learning the theory and history behind various art movements but involves the practice of the techniques by the students themselves, which will help them connect and empathise with the artists they are studying.

The world without generative learning would be quite tragic in actuality… Imagine for example if our sports teams never practised their games, but instead spend their time rehearsing for the big match by reading a textbook. Or if the pilot school was just an instruction manual finished with a written test. Obviously, these are big exaggerations, and examples where generative learning is completely necessary in order to produce satisfactory results, but the point is, adding generative learning is a surefire way to make things more interesting and get better results while doing it.

How to use generative learning

So if that’s the case, how can we as educators and students use generative learning to bolster our educational efficacy and stay committed to our learning goals and ambitions? It’s much easier than you might think. For almost any skill, we learn it for the sake of doing it, rather than just to know how to do it. It really is as simple as just doing it. As an educator, we should constantly be looking for ways to put into practice the words and anecdotes we tell our students. A language tutor can use generative learning by having the student repeat words and phrases they learn, to help them get used to the sounds when it comes from their own mouth, and familiarise themselves with the rules of the language by making the associations out loud. Even if your brain doesn’t remember a word or sound, your mouth will, and it will help you make similar words sound natural and native when you use them. In mathematics, a teacher can have their students try and prove a theory rather than just knowing it, perhaps letting them play around blind at first, and then giving them just enough tips for them to come to an answer themselves. As someone who has studied both language and maths, I can tell you that the added practice alongside the theory really helps to not just know, but understand the subject you’re learning.

So what now?

If you learned anything or simply enjoyed reading this article, remember that there’s much more right here on the Superprof blog. This article is from a series called “advice for tutors” so if you’re interested in learning more about how the brain works and learns, along with techniques that help us use this knowledge to become better educators, make sure to check out the rest of the articles in the series. Thanks for reading!