Over time, animals that were capable of learning had a better chance of surviving than those with fixed behaviors. They were also more likely to pass on their genome — containing genetically driven algorithms — to the next generation. This way, natural selection favored the emergence of learning and so does modern society today.

Not only does the immensity of knowledge continuously redefine what it means to be human, but literacy and numeracy are imperative of modern wealth creation.

Yet, the numerous effects of education go well beyond practical know-how and economic growth. As pointed out by Steven Pinker in his book Enlightenment Now, “better education today makes a country more democratic and peaceful tomorrow”.

Educated people are less racist, sexist, xenophobic, homophobic, and authoritarian. It is indeed fascinating to become conscious of the progress we have achieved so far in basic education, years of schooling, and female literacy (which remains a problem in a few countries such as Afganistan).

It is also clear that we are going through a revolution in the dissemination of knowledge — powered by technology. Most of the world’s knowledge is now online and much of it is free.

Everyone with a smartphone can enroll in massive open online courses (MOOCs) or learn anything with a quick Google search.

As amazing as it might seem, we are also becoming smarter and smarter. Intelligence Quotient (IQ) scores have been rising for more than a century — the famously known Flynn effect.

At the epicenter of such effect is the increase in health, better nutrition, rising standards of living, and — of course — more and better education.

However, despite the global progress on education and its evident importance, we are still facing major challenges in the existing models and professional mindsets.

In today’s world, higher education serves primarily as a seal of approval, signaling that these particular professionals have met some sort of minimum standards.

Nevertheless, evidence suggests that we forget academic content at a rate of approximately 50% every two years, and given the exponential change rate of technology and knowledge, a significant part of that content is outdated by the time we need it in a professional setting.

So how does memory work and what can we learn from neuroscience?

Researchers distinguish memory into at least four different categories:

• Working memory: typically what allows us to keep a phone number in mind during the time it takes to type it;
• Episodic memory: episodes from our daily lives recorded in the hippocampus;
• Semantic memory: episodic memories do not seem to stay in the hippocampus forever. At night, during a good night of sleep, the brain plays them back and moves them to a new location in the cortex. From episodic, the memory has now become semantic and transformed into “permanent” knowledge;
• Procedural memory: when we repeat the same activity over and over again, neurons in the cortex modify themselves so that the information flows better in the future.

Sleep is then foundational for learning. Vital. Yet, schools tend to start quite early and graduate students, during exams season, typically do not get a lot of sleep (at least I didn’t!). So, I wonder what the impact would be if we decided to slightly postpone the time start of school?

Interestingly, the experiment has been done in Singapore, and with promising results:

By delaying start of school half an hour to one hour, teenagers got more sleep, school attendance increased, attention in class improved and grades shoot up.

This could be a simple adaptation of the educational system to the constraints of brain biology that are now widely researched and known. Yet, and despite the strong recommendation of the American Academy of Pediatrics to delay schools starting time, there has been little or no change in this regard.

What about the graduate illusion that cramming for an exam is the best learning strategy?

Is the problem that students were not thought how to learn, or is the system cramming all the information that was taught in 6 months into one single exam that may define your future?

Part of the problem is that we are unable to differentiate the different compartments of our memory. When we learn something, and it is fresh in our memory, it makes us believe we succeeded because the information is fully present in our minds.

We feel we know since it is present in our short-term storage space. But unless we retest our knowledge in a low-dose-high-frequency fashion, the memory will end up vanishing.

Ref. Osmosis.org

To get information into long-term memory, we need to study the material and test ourselves constantly — as well as receiving continuous and specific feedback.

But is grading the best we can do?

The quality, accuracy, and frequency of the feedback we receive determine how quickly we learn. Drawing on Edward Thorndike’s Law of Effect, immediate feedback is essential for effective learning — which is not possible when tests are marked by hand and grades are given weeks later.

Grades are also typically a simple sum — it summarizes different errors without distinguishing them.

We don’t get the why, nor how to correct them instantly. In the most extreme but rather common cases, an F remains as such — providing no additional information, only the social stigma of incompetence.

What is more interesting is that numerous studies confirm that stress and anxiety can dramatically hinder the ability to learn. And when bad grades are presented as punishment (negative reinforcement), this is how you tend to feel.

How can we then stimulate curiosity making us want to learn more whilst receiving constant and personalized feedback (positive reinforcement)?

I believe we can achieve this at scale with the help of Education Technology (EdTech) and Artificial Intelligence in Education (AIED) — making learning more efficient, cost-time effective, and fun.

I think intelligent learning systems should not be designed to entirely replace teachers, but instead to augment them where it makes sense from a learners’ experience and a business perspective.

The so-called Intelligent Tutoring Systems (ITS) are among the most common applications of AI in education. In a simplified way, this is how some ITS models work:

An example of typical ITS architecture (ref. Holmes et al. 2019)

While the student engages with an adaptive learning activity selected by the system, thousands of data points are being collected about each interaction, such as what is clicked and what is typed, common misconceptions, how rapidly students move the mouse around the screen, students’ speech and psychological response (in some advanced ITS), etc.

Then, all this data is analyzed (possibly using machine learning or a Bayesian network) both to provide the student with individualized formative feedback and to update the system’s Learner Model. The analysis might also update the Pedagogy Model to make sure the model is optimized continuously, based on the approaches that made learning more effective.

Over time, this ITS cycle of data collection, data analysis, and model optimization aims to deliver each student unique personalized learning experiences that only gets better and better.

These and other AI-driven learning systems can also be designed to boost curiosity and deliver instant (and delayed) gratification.

This is important as memory and curiosity are linked: the more curious you are about something, the more likely you are to remember it.

The neuroscience of motivation is also quite simple: the desire to do action X must be associated with an expected reward.

Although not necessarily instantly when you’re learning, our world is pretty good at rewarding people who have the required skills at the right time.

So, if we can educate people to continuously reskill or upskill themselves, I think we have a much better chance of progressing towards a more equal society. Scalable online and free education will be key in this transformation.

However, our attitude toward learning also has to change.

Today, most people follow a sequential model of learning and working. We spend a few years of our lives studying and at some point, we say: “OK, I’m done studying now, I’m ready to start working”.

Considering the exponential growth of technology and scientific research, we need to rethink this model: we need to follow a more parallel approach towards learning and working, where we are open and able to acquire new skills whilst applying them, as a lifelong learning process.

Indeed, the question of managing this transition in terms of skilling, educating, and on-the-job (re)training is far more concerning than the tragic popular question of “Will there be enough work?”. When discussing automation in general, people tend to focus solely on “jobs lost”, and that is awfully incomplete. We also need to add “jobs changed” and “jobs gained” into the equation — as a result of technology.

By looking at the data at the Bureau of Labor Statistics, if we look at 10-year periods in the United States, at least 8% to 9% of jobs are jobs that didn’t exist in the previous period — because we have created and invented them. This is the case for Web design, Social Media Marketing, etc. It’s not that difficult to create jobs.

The challenge is to create entire new industries that will employ a large number of people in an era where you can build massive valuable companies like Whatsapp with only 18 people. To support this, innovation in other areas will also need to happen, such as governmental public programs like Universal Basic Income (UBI).

The reason why we use technology and invest in automation is so that we don’t need to put in so much effort to achieve a certain outcome. We achieve more with less.

Thanks to recent advances in neuroscience, cognitive psychology, education sciences, and artificial intelligence we now have a detailed understanding of how our brain learns.

Just like medicine is based on biology, the field of education must be grounded in systematic research that brings forth evidence-based learning strategies, as well as scalable and affordable technology that enables us to go beyond our human limitations.

I look forward to seeing — and take an active part of — the progress and impact of EdTech and AIED in society. Amazing times ahead for the future of education, and ultimately, for humanity!