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Why Schools Shouldn’t Teach AI

One of the earliest technologies that gained wide user adoption was the calculator. Pocket size  versions of the calculator became available in the 1970s and it didn’t take long before people started wondering whether children should just learn to use the calculator instead of learning mental mathematics. 

Thankfully, the educational system continued to teach basic arithmetic to young students for a very good reason. Students don’t just need to understand the concept of addition or multiplication. They need to practice arithmetic over and over again in order to build and strengthen the right neuronal connections for computational fluency. High computational fluency is correlated with better performance in more advanced math, so doing there drills in early years sets the foundation over which more complex mathematical thinking can be built. 

With the coming of AI, especially Large Language Models (LLMs), we are seeing a similar debate take place. The popular line of reasoning — AI is undoubtedly going to be a big part of our lives going forward and students need to learn how to use it — makes logical sense. 

But perhaps that’s precisely the reason why we shouldn’t teach students to use AI.

What Is The Goal Of Education?

There are different philosophical views about the goal of education, but most people would agree that education is about building the right skills so students can thrive in the real world. The point is never to teach students about all possible tasks that they might have to do, but to build enough of the right foundational skills that will allow them to adapt, learn new things and make meaningful contributions. This is why higher order skills, like creative and critical thinking, feature on the top rungs of Bloom’s taxonomy, a hierarchical model of learning goals that guides our educational philosophy. This makes sense because it is nearly impossible to predict the kinds of jobs that will exist a decade or two later. So teaching specific domains is less important than building higher order cognitive skills that will allow students to be successful, no matter what tasks they might have to face. 

Cognitive Complexity

Consider the two questions below: the first one is taken from College Board’s question for AP World History while the second task asks you to create an AI prompt. 

Task 1:

Directions: Question 1 is based on the accompanying documents. The documents have been edited for the purpose of this exercise. 

Evaluate the extent to which communist rule transformed Soviet and/or Chinese societies in the period circa 1930–1990.

(See the full question for accompanying documents)

Task 2: 

Create a prompt for ChatGPT to help you understand the significance of the American Civil War.

Which of these tasks, in your opinion, is harder?

Most people would agree that the first task is way more complex than the second one. 

To complete the first task, students have to quickly read the documents provided, make notes on the historical context, audience, perspective and purpose. As a side note, the documents are varied in nature (e.g. it could be a propaganda poster or a diary entry) and not directly related to the question. Using the provided material as evidence, students have to construct a defensible thesis. They also have to choose an outside piece of evidence that adds an additional dimension, and provide a broader historical context relevant to the prompt. In short, they have to make reasonable inferences from the provided documents and integrate prior knowledge they have about the subject to create a compelling argument. And finally they have to synthesize all of this into a multi-paragraph essay! (Big thanks to my son, who recently took the AP World History test, for helping me understand what goes into answering this type of question).

For the second task, students simply have to ask ChatGPT to explain the significance of the American Civil War! Even if they want to become more sophisticated with their prompting, the strategies (e.g. role-play or providing additional context) are pretty straightforward and easy to learn. 

It’s no wonder the AP exam gives students an entire hour to answer the first question, while the second one can be done in just a few minutes.

A more telling sign of the low cognitive complexity of using LLMs, is how quickly people from all ages and walks of life have adopted LLMs to assist them with work tasks. In contrast, not many people have attempted coding, despite the many tools and tutorials that have been available for many years. Teaching STEM skills in schools continues to be a challenge due to the lack of qualified teachers. 

Teaching students to tackle complex tasks like the first one has a higher payoff than teaching them simpler tasks. After all, if our education system helps students develop critical thinking skills and the ability to handle tough questions, then easy tasks like the second one will be a breeze for them. Much like the calculator, students don’t need to be taught how to prompt LLMs to get an answer – they need to build skills to answer it themselves. 

What Aspects Of AI Should We Teach Students?

It’s clear that teaching students to use LLMs isn’t very useful because we would almost certainly replace a more complex learning goal with a relatively trivial one. 

A more worthy goal of teaching AI would be to teach them how AI works so students can build AI models as opposed to simply using them. But this brings us back to square one, because that requires a grounding in programming and computer science fundamentals. So focusing on STEM, coding and computational thinking in the K-12 curriculum is still the right approach to prepare students for careers in technical fields. 

Beyond the technical aspects of AI, it’s probably more important for students to understand how to learn better and how AI can impact the learning process. While there are a few scenarios where AI can improve the learning process, there are also learning traps that can harm creative and critical thinking in the long run. Or, they might be better served in building their ethical reasoning skills to tackle the numerous challenges that are bound to arise as AI usage becomes more widespread in society. 

All of those would require them to think harder and deeper than simply learning how to use AI. 

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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.

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Introducing the “Invent in an Hour” Mini-Course!

At MindAntix, we’re passionate about igniting the spark of creativity in students. But let’s face it, coming up with truly novel ideas can feel daunting, and this is especially true for young students. That’s why we’re thrilled to announce a partnership with EvolveMe, an online platform from American Student Assistance, to launch the “Invent in an Hour” mini-course – a one-of-a-kind program designed to encourage students to become inventors in less than 60 minutes!

EvolveMe is a free online tool that helps students build different skills and explore various career options. Many students aren’t aware of the scope of education and career opportunities available after high school. ASA’s research indicates that students face a lack of awareness, access and confidence that can limit their growth and potential post high school. 

With our new mini-course, we aim to address these three areas. Students get a deeper understanding of what creative thinking is and how it applies to almost all fields. By breaking down the invention process into manageable, bite-sized steps, we equip students with the tools and confidence to embark on their own creative journeys.

Demystifying the Invention Process

With our approach, we flip the script on traditional creativity methods and remove the pressure of “solving” predefined problems. Instead of starting with a problem and seeking a solution, we guide students through the power of associative thinking, a technique that sparks innovation by combining seemingly unrelated ideas.

Here’s how it works: imagine combining the “stretchable” aspect of a bracelet with a pillow to make a revolutionary pillow whose thickness can be adjusted by stretching. Or picture a pair of headphones that can double as a mood detector. Associative thinking makes these seemingly absurd combinations possible, and more importantly, increases the likelihood of generating truly original ideas – all within a student’s comfort zone.

Bowerbird Inspiration: Nature’s Mastermind

Our guide through this creative adventure is Curio, a character inspired by the bowerbird, a fascinating avian species renowned for its remarkable creativity. Male bowerbirds meticulously collect an array of colorful objects – from bottle caps to berries – to construct elaborate displays, showcasing their ingenuity to attract mates.

Curio embodies the essence of the bowerbird, encouraging students to gather diverse ideas and assemble them into something uniquely their own. Just as bowerbirds use their nests to express themselves and interact with their environment, Curio helps students see how assembling various concepts can lead to surprising and effective solutions.

Transforming Ideas into Real Inventions

The “Invent in an Hour” mini-course goes beyond simply brainstorming. It equips students with the practical steps to turn their newfound concepts into reality. Here’s what they’ll learn:

  • Idea Generation: Students learn to generate ideas using associative thinking, encouraged by Curio’s playful guidance.
  • Originality Check: Participants use tools to check their ideas against patent databases, ensuring their inventions are not only useful but also original.
  • The Art of the Pitch: Students learn how to craft compelling pitches for their inventions, preparing them to present their ideas confidently.

By the end of this engaging mini-course, students won’t just have an understanding of the inventive process; they’ll have a tangible invention of their own, complete with a polished pitch ready to be shared with the world.

We believe that fostering creative confidence in students is not just about nurturing future inventors; it’s about empowering them to be problem-solvers, and lifelong learners. With the “Invent in an Hour” mini-course, we provide students with a stepping stone towards building their creative confidence.

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3 Keys To Creativity And Computer Science

How can we combine creativity and computer science to create positive education outcomes? The demand for computer science and information technology graduates is expected to grow by 14.6% over the next decade, much faster than any other area. While the number of computer science graduates is increasing, it is still not enough to meet the growing demand for STEM related jobs. Technical jobs also pay significantly more than other careers, yet many students continue to shy away from STEM fields.  

So, how do we encourage more students to pursue computer science which leads to both a lucrative and a fulfilling career? Here are three strategies to address challenges that students face in technical areas. 

Change Mindset 

One of the barriers to learning computer science is the perception that not everyone can become good at it. Parents, educators and others can inadvertently reinforce this stereotype when they use phrases like “not a technical person”. Much like the mindset about math, which plays a key role in the poor performance among US students, limiting beliefs about computer science creates a hesitancy towards the subject. When the adults in a child’s life themselves feel traumatized with subjects like math or computer science, it’s not surprising that the child develops a fear of approaching that subject. 

The reality is that there really is no “math brain” or a “computer science brain”. Most people can learn these subjects once they get over their mental block and put in the effort to learn. Neuroscience research shows that the human brain is quite malleable and it grows when you are learning a new skill. MRI scans of students doing math show that when students make a mistake a synapse fires even when students are not aware they made a mistake. As a result the brain grows when students are struggling with a concept.  

The good news, however, is that mindsets can be changed. Growth mindset, a concept pioneered by Stanford psychologist, Carol Dweck, is one approach to help students shift their mindset towards a subject that they find difficult. Helping students recognize that the process of learning any skill is going to feel uncomfortable as your brain starts to grow and reconfigure itself in order to become good at the new skill.  

Beyond building growth mindsets, educators need to combat the harmful stereotype that computer science is not “cool” or that it’s for “nerds”. This is where framing computer science as a way to exercise creativity is useful. Mitchel Resnick, Professor at MIT and creator of Scratch, believes we need to view computers more as finger paint instead of as some esoteric technology. He explains, “…until we start to think of computers more like finger paint and less like television, computers will not live up to their full potential.” Just like finger paints and unlike televisions, computers can be used for designing and creating things. Encouraging students to use computers in different ways to solve problems, or create new things can shape their attitudes in a more positive direction. 

Build Thinking Skills Early

STEM fields face a high attrition rate (~50%) as many students switch their major part way through. When students’ first exposure to a programming language is in college, they find the coursework more challenging and are more likely to drop out of the course. One way to combat this problem is to start building computational thinking skills early on. Computational thinking is an approach to formulating problems in a way that computers could be used to solve them. 

Building computational thinking skills is not hard and doesn’t necessarily need expensive resources like computers and software for all students. As an example, the Computer Science Unplugged project uses games and activities to expose children to thinking styles expected of a computer scientist, all done without using any computers. Not only do students learn concepts but the group games also build social connection and make the whole experience more enjoyable. In another example, students create an interactive play while learning programming fundamentals (like sequential logic, conditionals or flowcharts) along with creative thinking (associational and analogical thinking) and storytelling. The advantage of using an unplugged approach is that students can be introduced to useful computer science concepts at a younger age without making it overwhelming for them. 

Add Project Based Learning

Projects are another way to make learning more engaging and combat the negative stereotypes students might hold at the same time. When researchers at a university in Ohio redesigned their computer science classes to encourage more creative and hands-on learning, they found that in addition to an improvement in the quality of student work, the three year retention rate increased by 34%!  This is especially important for women, who typically view computer science courses  “to be overly technical, with little room for individual creativity.” 

By encouraging students to apply the concepts they are learning towards a project of their own choosing, educators can create an environment that students personally find meaningful. It also helps students view computer science as another tool that they can use to solve problems that they encounter. 

Technology has become an integral part of our lives and most work now requires some level of technical competence. The demand for STEM, and especially CS, is only going to accelerate as we move further into the 21st century. To encourage more students to pursue computer science, parents and educators need to pay attention to limiting mindsets, provide creative opportunities to learn core thinking skills and projects to apply their knowledge in real-world scenarios.  

This article first appeared on edCircuit. 

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Cultivating an Entrepreneurial Mindset: Lessons from Visionary Leaders

With the rapidly evolving business landscape in the age of AI, the ability to innovate and adapt is paramount. For CEOs and business leaders, cultivating an entrepreneurial mindset within their organizations is more than a strategy—it’s a necessity. This mindset, characterized by agility, creativity, and resilience, can be the difference between thriving and merely surviving. Drawing on the examples of visionary leaders like Yvon Chouinard, Tony Hsieh, and Satya Nadella, we can see how adopting an entrepreneurial mindset can drive success.

What Is An Entrepreneurial Mindset?

Most people have an intuitive understanding of entrepreneurial mindset. A more formal definition, following an exhaustive review of published work, defines entrepreneurial mindset as “a cognitive perspective that enables an individual to create value by recognizing and acting on opportunities, making decisions with limited information, and remaining adaptable and resilient in conditions that are often uncertain and complex.”

In hypercompetitive markets, becoming more entrepreneurial is crucial for adapting to emerging threats and staying ahead of competitors. Such strategic entrepreneurship requires a special kind of leadership—entrepreneurial leadership. 

Individuals exhibiting such leadership behaviors are pivotal in steering their organizations towards innovation as a company’s culture and leader’s entrepreneurial mindset are interconnected. This relationship between culture and mindset is recursive, and modeled as an entrepreneurial spiral. The bottom-up process of the spiral represents the influence that a company’s entrepreneurial culture has on the leader’s mindset. Simultaneously, the leader’s entrepreneurial mindset can have a top-down effect on the culture, fostering an environment that is more entrepreneurial. Both the top-down and bottom-up processes can result in a positive feedback loop that grows a company into a more innovative state. 

Key Traits Of Entrepreneurial Leaders

The essence of an entrepreneurial leader can be distilled into three core entrepreneurial mindsets: people-oriented,  purpose-oriented, and learning-oriented. Visionary leaders such as Yvon Chouinard of Patagonia, Tony Hsieh of Zappos, and Satya Nadella of Microsoft exemplify these traits, having guided their companies to remarkable achievements.

People-Oriented Mindset: Tony Hsieh

A people-oriented mindset has two factors: staying inclusive and open; and being positive and appreciative of employees. This mindset fosters a positive and supportive work environment, where employees feel valued and empowered. Leaders with a people-oriented mindset are more likely to win the support and trust of their employees and team members.

Tony Hsieh, the late CEO of Zappos, was an example of a leader with a people-oriented mindset. He made employee happiness a cornerstone of Zappos’s culture. Hsieh implemented a flat organizational structure at Zappos, which minimized hierarchy and encouraged autonomy among employees. This empowerment allowed team members to make decisions that they felt were in the best interest of customers, fostering a sense of ownership and responsibility. Hsieh’s emphasis on employee satisfaction also led to the implementation of groundbreaking policies at Zappos, such as offering new hires a “quitting bonus” if they didn’t feel aligned with the company’s culture. This bold move ensured that the team remained highly motivated and committed, fostering an environment where innovation and exceptional customer service could flourish.

Purpose-Oriented Mindset: Yvon Chouinard

A purpose-oriented mindset has two main aspects: keeping the end goal in mind and having the endurance to see it through. Leaders with this mindset have a unique ability to balance their focus on the objective with the patience necessary to achieve it. This equilibrium is rooted in their deep commitment to their purpose.

Patagonia’s founder, Yvon Chouinard, is an excellent example of a purpose-driven leader. He has instilled in his company a strong dedication to environmental preservation. Patagonia’s initiatives, which range from using recycled materials in their products to supporting environmental causes, reflect Chouinard’s unwavering belief in sustainability. Under his leadership, Patagonia has not only talked the talk but also walked the walk, setting an example for the industry and consumers alike. Patagonia’s efforts, such as the “1% for the Planet” pledge, which commits the company to donating 1% of its sales to environmental groups, have inspired other businesses to follow suit. Patagonia’s dedication to environmental and social issues has also resonated with consumers, resulting in a loyal customer base that has contributed to the company’s financial success.

Learning-Oriented Mindset: Satya Nadella

A learning-oriented mindset is characterized by two key attributes: an ability to pick signals from all around and an inclination to take risks. A learning-oriented mindset allows leaders to seize opportunities. Moreover, the calculated risks taken by these leaders inspire their employees to follow suit.

Satya Nadella’s success at Microsoft is a testament to the power of a learning-oriented mindset. He was quick to recognize the potential of cloud computing, and steered Microsoft’s efforts in that direction making Azure a major competitor to Amazon Web Services. More recently, Microsoft’s partnership with OpenAI, and his push to integrate advanced AI technologies into its various offerings, demonstrated Nadella’s ability to act decisively on emerging technological trends. This strategic move positioned Microsoft at the forefront of AI development. Nadella’s initiative to foster a growth mindset at Microsoft, encouraging employees to embrace a “learn-it-all” as opposed to a “know-it-all” approach, highlights his commitment to creating a culture of continuous learning, experimentation, and adaptation. By fostering an environment that values learning and growth, Nadella has revitalized Microsoft’s innovation engine. 

Blueprint For Creating Innovative Companies

The journey to fostering an entrepreneurial mindset within an organization is multifaceted. It requires leaders to be purpose-driven, people-oriented, and committed to continuous learning. Leaders who embrace these principles can transform their organizations, creating a culture that not only survives but thrives in the face of change. To cultivate an entrepreneurial mindset, leaders should focus on embedding these traits within their organizational culture. Here are  three ways leaders can adopt to build a more entrepreneurial culture. 

  • Embrace a clear and compelling purpose. Leaders should articulate a clear and compelling purpose for their organization that employees can rally behind. This purpose should be something that employees are passionate about and believe in, and it should provide a sense of direction and purpose for their work. An inspiring purpose goes beyond profits or mission statements and addresses the “why” of a company’s existence. For Patagonia, environmental sustainability is a purpose that goes beyond individual or company interests which makes it inspiring for employees to adopt. 
  • Encourage open communication and collaboration. Leaders should encourage open communication and collaboration within their organizations. This open communication needs to be bidirectional allowing employees to also express their concerns, challenge assumptions and share their own ideas. The bottom-up part of communication helps leaders get better signals about new technologies and use cases that eventually aid their strategic thinking. In contrast to Nadella, Steve Ballmer mocked the iPhone when it first came out, and completely underestimated its market success. Allowing open collaboration to test out experimental ideas helps identify new areas of innovation — signals that leaders can use in strategic planning.
  • Create a positive and supportive work environment. Leaders should create a positive and supportive work environment where employees feel valued and respected. What made Zappos a great place to work was the autonomy that Hsieh provided to all employees, empowering them to go beyond scripted responses and make on-the-spot decisions that were in the best interests of the customer. As a result, employee turnover at Zappos was significantly lower compared to industry average.

By following these strategies, business leaders can create a high-performing and innovative workplace.