Will AI Make Us Dumber?

Back in the early 1980s, a researcher named James Flynn was studying old IQ tests when he noticed something strange. Between 1932 and 1978, IQ scores had shot up by almost 14 points! This became known as the “Flynn Effect,” and researchers kept seeing IQ scores rise by about 3 points every ten years. But then, in the early 2000s, things took a turn. Studies in a few Scandinavian countries found that IQ scores were actually dropping. And get this – a recent US study confirmed the trend, with the biggest decline in young adults aged 18-22.

This drop in critical thinking skills is like what happened to creative thinking scores over a decade ago. Professor Kyung Hee Kim discovered that student creativity, as measured by the Torrance Test of Creative Thinking (TTCT), has been tanking since the 1990s. Her analysis even led to a super popular Newsweek article called The Creativity Crisis. She found that important aspects of creativity, like coming up with original ideas and being able to brainstorm lots of ideas, have seriously declined over the years. And the decline has gotten even worse in the last ten years.

So, what’s changed that’s causing this decline in higher-order thinking skills? It’s not a huge leap to think that the rise of technology over the past couple of decades might have something to do with it. Both studies suggest that certain aspects of technology can actually hinder the development of creative and critical thinking skills.

With AI rapidly gaining adoption — over 45% of students in high school already use AI to help with their assignments — what are the possible implications on cognitive development of children. 

How We Learn

To understand how thinking is essential for learning, let’s take a closer look at what happens in our brains when we encounter new information.  Learning can be seen as a three-step process. First, our brain encodes new information and stores it in our working memory. It’s like translating a high-resolution image of an apple into a simpler icon or symbol. Next, this information might need to be moved to our long-term memory. Imagine you see a purple apple for the first time. Your brain needs to pull up your existing mental image of an apple, update it, and then store it back. This shows that working memory isn’t just storage – it’s more like a mini-computer with multiple functions. The latest model of working memory includes three specialized memory subunits and an executive controller that manages attention and communication with other parts of our brain.

One memory subunit is the visuo-spatial sketchpad, which lets you hold and modify visual concepts. For example, try picturing a red apple, then change its color to green and add some leaves to the stem. If you could do this, it all happened in your visuo-spatial sketchpad. Another subunit, the phonological loop, processes auditory information. It has a small temporary memory that needs constant refreshing.  When someone tells you their phone number and you repeat it to remember it, you’re using the phonological loop.  Working memory is where the magic of creativity and learning happens. It creates specialized models to speed up future processing. By comparing, contrasting, and finding connections, our working memory helps us understand the world better. It’s also where new ideas are born.

The challenge is that deep thinking takes time. Learning something new or creating something original means changing your internal mental models, and that requires serious brainpower.

Biological Bandwidth of Learning

The late MIT professor, Patrick Winston, was renowned for his lectures, especially his super popular annual talk, “How to Speak.” One of his top tips? Ditch the slides and use the board. Slides are good for exposing people to a topic, but the board is better for informing them. Why? Because writing on the board forces you to slow down, giving your audience more time to process what you’re saying. As Winston puts it, “The speed with which you write on the blackboard is approximately the speed at which people can absorb ideas.” 

This simple trick perfectly illustrates the concept of “biological bandwidth” when it comes to learning. Your brain needs time to absorb and store new information—kind of like how long it takes to physically write stuff down.

So, what happens when we try to cram too much information into our brains too quickly? Our working memory doesn’t have time to process everything properly. Instead of analyzing, comparing, and updating our mental models, it takes shortcuts, relying on things like stereotypes or gut feelings. This leads to quick decisions, but they’re often wrong because they’re not based on solid reasoning.

How AI Can Make Thinking Skills Worse

The problems with AI are similar to the problems of relying on too much tech, but way worse. It all boils down to “cognitive offloading,” which is when we let AI or other tech do the thinking for us. If we keep this up, our own critical and creative thinking skills will get rusty, and could even disappear for good. It’s not hard to imagine how bad that would be for society.

Students are hit even harder. During the teen years, our brains are growing and rewiring big time, especially in the prefrontal cortex. If we don’t use certain abilities, our brains just prune those connections away. So, if you’re not flexing those creative and critical thinking muscles, you might not be able to think as deeply as an adult.

Lots of schools and students are already using AI, and while it can be helpful in many scenarios, there are also some traps that are easy to fall into.

The Effort Trap

It’s obvious that using AI to write an essay without putting in any effort is bad for learning. But there are also sneakier ways that AI can trick us into thinking less. This is the effort trap: when you think you’re being critical and analyzing AI’s output, but you’re actually using less brainpower, or even thinking in a totally different way.

Here’s an example that can mess with your creativity in the long run. People often use AI to generate ideas, which they then refine. But if they had taken the time to think for themselves first, they might have come up with totally different ideas. Coming up with that initial spark is an important skill in itself, especially in ambiguous situations. By relying on AI, we’re short-circuiting our own creativity.

The Competence Trap

The competence trap is when you think you’re a pro at something, but you’re really just leaning on AI as a crutch. This can trip up both students and teachers. Teachers might think their students are killing it based on the AI-polished work they see, and move on to harder stuff before the students are actually ready.

The Capacity Trap

When information is super easy to get, it’s tempting to consume way more than our brains can handle. We’ve all been in lectures where we thought we understood everything, but then got totally lost when it was time to do the work. This is the capacity trap: it’s easy to keep chugging along without stopping to reflect, but that can lead to a big crash later on.

Making AI Work For You

AI can definitely help us be more productive and learn new things. For example, getting instant feedback from AI is very impactful because you can make changes while everything is still fresh in your mind. Waiting a week for feedback on an essay isn’t as effective because you have to switch gears and get back into that mindset. Also, teachers can use AI to personalize lessons for each student, which helps them stay engaged and understand the material better.

So, how can we use AI in a way that actually helps our thinking skills instead of hurting them?

The key is to be mindful of how much thinking you’re doing yourself and how much you’re leaving to AI. A good rule of thumb is to avoid using AI as a crutch right from the start. Instead, think about the problem first and try to come up with a solution on your own. Then, you can use AI to help you improve your work or get unstuck if you need to.

Can Creativity Show the Flynn Effect?

In the 1920s, Alexander Luria, a Russian psychologist, interviewed a group of rural Russians who had been untouched with the scientific advancement of the 20th century. His goal was to understand the influence of social environment on cognitive development. What kind of thought patterns would dominate in societies where life revolved around handling tangible objects, as opposed to technical societies that induce more abstract reasoning?

A sample interview (see picture above) shows the questions and responses to a logical syllogism, posed to a rural adult. Nowadays, these kinds of deductive reasoning puzzles can be easily answered by a typical 7-8 yr old. But based on the response, it is clear that abstract, hypothetical reasoning is an alien concept to the interviewee. As Prof. James Flynn points out, “Today, we are accustomed to detaching logic from the concrete, and say “of course there would be no camels in this hypothetical German city.” The person whose life is grounded in concrete reality rather than in a world of symbols is baffled.

The Flynn Effect, which was first documented by Prof. Flynn, is the observation that IQ has been rising steadily from one generation to the next over the last century. The reason fueling this phenomenon has been a matter of debate. While Flynn initially attributed the rise in IQ to improved nutrition, affluence and other factors, in his recent book, “Are We Getting Smarter?”, he proposes a new theory.

He now believes that the Industrial Revolution and its subsequent social changes led to the rising IQ trend. He explains in the book, “The ultimate cause of IQ gains is the Industrial Revolution. The intermediate causes are probably its social consequences, such as more formal schooling, more cognitively demanding jobs, cognitively challenging leisure, a better ratio of adults to children, richer interaction between parent and child.

At the same time that IQ scores have been rising, another set of scores have been changing – in the opposite direction, unfortunately. Prof. Kyung Hee Kim, has been studying Creativity and its trends for several years. In a meta-analysis of creativity scores since the 1990s, Kim found that Creativity scores have been declining over the last two decades. The decline in creative thinking has affected Americans of all ages, but is especially pronounced for elementary age kids. Our focus on logical and analytical thinking certainly helped raise IQ scores, but it might have come at the expense of other thinking patterns. 

In his book, A Whole New Mind, Daniel Pink outlines why the 21st century will be the “Conceptual Age”, dominated by creators and empathizers. He uses three prevailing trends – Abundance (most things are not scarce anymore), Asia (work that can be outsourced, is) and Automation (linear, logical work is increasingly being handled by automation) – to make his argument that non-linear and creative thinking will become essential in the 21st century.

So, this brings us to an interesting state. On the one hand, the 21st century will place more demands on creative thinking, and on the other hand, creativity is currently on the decline. Would the cognitive demands of the Conceptual Age spur a trend of rising Creativity?

An optimistic view would be that just like Industrial Age triggered a rise in IQ, the new Conceptual age will set off a rise in creative thinking. However, the current declining trend of Creativity, should give reason to pause. Maybe the transition towards more creative work will be more challenging, than the adaptation to the industrial and scientific age.   

To address the declining creativity, Prof. Kim says, “Reversing the trend will be a process that will require patience and perseverance, because the results will not be immediate.” We will eventually find out the answer to our question, but in the meantime we can all start taking steps to influence the outcome.



Analogical Reasoning

In the early 1860s, when Leo Tolstoy was teaching writing to children of Russian peasants, he hit upon an interesting way to bring more creativity into the exercise. He asked his students to write a story on the proverb, “He eats with your spoon and then puts your eyes out with the handle.” The result of his exercise surprised even him.

After some initial hesitation, his students approached the challenge with an unexpected enthusiasm and produced a much better composition than the one Tolstoy had himself written. Tolstoy commented on the quality of his students’ work in an article with, “Every unprejudiced man with any feeling for art and nationality, on reading this first page written by me, and the following pages of the story written by the scholars themselves, will distinguish this page from all the others, like a fly in milk, it is so artificial, so false, and written in such a wretched style.

While Tolstoy was simply trying to motivate his students to write with more vigor and authenticity, he accidently introduced his students to a key creative thinking skill – analogical reasoning.

Analogical reasoning is the ability to find relational similarity between two situations or phenomena. Robert and Michele Root-Bernstein in their book, Sparks of Genius, consider analogical reasoning to lie at “the heart of what it means to think creatively” and a skill that many scientists rate as the most important one to possess.

In fact, several discoveries in science can be traced back to finding the right analogy. For instance, early geneticists likened genes to beads on a string to help them understand how traits are passed along. While this simple analogy couldn’t explain everything, it did suggest possible mechanisms for inherited traits. Making analogies is a fundamental way of thinking applicable not just in science, but in almost every field like mathematics, religion and literature. Robert Frost’s metaphor of life to a journey in “The Road Not Taken” is especially powerful because of the unique associations it invokes each time.

While it’s clear that analogical thinking plays an important role in creative thinking, what exactly does it involve? Underlying analogical thinking are three mental processesRetrieval (with a current topic in working memory, a person may be reminded of an analogous situation in long-term memory), Mapping (aligning the two situations on the relational structure and projecting inferences), and Evaluation (judging the analogy and inferences).  

The MindAntix brainteaser, Proverbial Tales, inspired by Tolstoy’s challenge to his students, aims to strengthen the mental processes used in analogical reasoning. Using proverbs from different cultures, users have to construct an original story that reflects the meaning of the proverb, forcing them to go through the different stages of retrieval, mapping and evaluation.

As Robert and Michele Root-Bernstein point out, “There is so much to be learned by analogizing that we must not neglect to learn how. Like every other tool for thinking, the capacity within ourselves and our children ought to be nurtured, exercised, trained.

3 Simple Ways To Be Creative in Science

Bernard Baruch, the American financier and political consultant, once commented that “Millions saw the apple fall, but Newton was the one who asked why.” While it’s hard to imagine that no one else asked why, it is still worth pondering on how Newton managed to solve the puzzle.

Newton did not arrive at the solution in a sudden flash of insight. Instead, the groundwork for reaching his conclusion had been laid over several years before that. Newton had been mulling over what force prevents the moon from shooting off in a straight line at a tangent to its orbit. His breakthrough came when he connected the dots between the force that holds the moon in it’s orbit and the force that causes an apple to fall to the ground. In other words, by using an analogy, Newton was able to create the right hypothesis that eventually led to his theory of universal gravity.

Contrast that kind of thinking with how science fair projects in most schools are approached today. Most teachers (helpfully) give out a list of ideas to base science projects on and the focus is almost entirely on following the scientific process to construct good experiments. However, just like Newton’s discovery, most scientific breakthroughs are the result of generating new and novel hypotheses – a skill that unfortunately, doesn’t get as much focus.  Prof. William McGuire, who proposed different techniques to help generate hypotheses, laments that “our methods courses and textbooks concentrate heavily on procedures for testing hypotheses (e.g. measurement, experimental design, manipulating and controlling variables, statistical analysis, etc) and they largely ignore procedures for generating them.

So how can you start to generate your own hypotheses? Let’s take an example. Suppose you wanted to do a science experiment that involves plants, but instead of the typical “how well do plants grown in different kinds of liquids?”, you wanted to use your own hypothesis. Here are three techniques that you could use to generate some interesting, fresh hypotheses.

  • Use Analogies: Say you start with an analogy that plants are like humans. We know that humans grow faster when they are babies and then start slowing down. We can apply this fact to plants to build a hypothesis of  “Do plants grow faster when they are small?”
  • Stretch or Shrink a Variable: We know that leaves have chlorophyll that help in photosynthesis (converting light energy into chemical energy). So one hypothesis could be that If we were to shrink the chlorophyll (maybe by removing all the leaves) would the plant be able to survive?
  • Use Reversals: You can get additional insights by reversing the causality or taking the opposite of a hypothesis. For instance, if your hypothesis is that “nature lovers make better gardeners”, by reversing the causality, you get the hypothesis that “learning gardening can make you into a nature lover”. By examining and experimenting with the new hypothesis, you can potentially uncover some new insights.

As a side note, it’s worth noting that these different techniques fit well with the broader framework of creative problem solving. Using reversals or shrinking a variable are both different kinds of manipulations, while analogies use the associative process.

Every scientific advancement started with asking the right “why?” followed by the right “how?”. We can get a lot more from our science education if in addition to understanding the scientific process, we also start focusing on generating original hypotheses. As Sir Isaac Newton himself said, “No great discovery was ever made without a bold guess.

MindAntix Brainteaser: Make-it-Better

The Wright brothers, Orville and Wilbur, after experimenting with gliders for a couple years, built and tested their first powered plane in 1903. The flight lasted 59 seconds. The next year, after making some design improvements, the brothers managed to stay in air for more than 5 minutes. And finally in 1905, they broke all records by flying 24.5 miles in a little over 38 minutes and landing safely when the fuel ran out.

Interestingly, despite having witnesses and photographic evidence, people were skeptical that two bicycle repairmen, with no expertise in designing airplanes, would have beaten well-funded experts in the field who were actively building their own planes. In fact, a 1906 article on the Wright Brothers in the Paris edition of the Herald Tribune was captioned “FLYERS OR LIARS?”. It took another couple of years for people to finally accept that the Wright brothers had indeed managed to create a flying machine. So how did these two amateurs end up outthinking the experts?

To fully understand that, you have to look at what some educators believe our current education system lacks. Dr. Maureen Carroll, Director of Stanford University’s Research in Education & Design Laboratory, is an advocate for introducing Design Thinking into the K-12 classroom. Our educational focus, thus far, has been on building analytical thinking skills. But, as she explains, “While analytical thinking is critically important, design thinking blends in equally powerful creative thinking.” And, “It’s not that creative thinking is more important… a blend of both types of thinking are more productive for finding truly unique and transformative innovation.

So, what does the design thinking process look like? As Dr. Carroll and her colleagues describe, the design thinking process has six key components – Understand, Observe, Point of View, Ideate, Prototype and Test. This is an iterative process, and not a linear one. Making prototypes and testing helps in understanding what works and what doesn’t, and in modifying the point of view.

How does this all relate to the Wright Brothers? Essentially, what made the Wright brothers succeed, was their exceptional design thinking skills. In an analysis of the Wright brothers’ thinking, Johnson Laird proposes that the brothers superior reasoning skills gave them the edge over others. Wilbur first spent three months reading up about aeronautical history and recognizing some of the gaps in the knowledge (understanding). They also developed their own, unique point of view on what factors would be most important in designing airplanes. For instance, while Wrights’ contemporaries believed building a light but powerful motors was key, the brothers believed that the ability to control the the plane was more important. They also used analogies from bicycles and nature to design specific parts of the plane (ideation, creativity). And of course, they spent years observing, iterating and building prototypes to test out their ideas.

Encouraging design thinking is the goal behind the Make-It-Better category of MindAntix Brainteasers. The goal is to look at everyday objects – understand how they evolved the way they did, observe how people use them, develop a point of view about what could be improved about them and then come up with ideas on how to make them even better. Design thinking, like other creative problems, helps build both critical and creative thinking. And because of the focus on users, it also helps build empathy. As Carroll and colleagues explain, “Empathy develops through a process of ‘needfinding’ in which one focuses on discovering peoples’ explicit and implicit needs.

After my son had done a couple of these brainteasers, he identified his own problem –  he wanted to make stickers better. His problem was that stickers lose their stickiness quickly when you try to use them on different shirts (well, it was a problem for him). His solution was to attach the sticker to one magnet and use another magnet to hold it in place. Not bad for a six year old!

Developing basic design skills isn’t hard, even without having to prototype and test. There are hundreds of objects we interact with everyday that are waiting to be improved upon. All it needs is an inclination to pause, reflect and imagine.