🧠 How Instant AI Answers Rewire Learning

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Welcome back AI prodigies!

In today’s Sunday Special:

• 📜The Prelude

• 💪To Learn, You Must Struggle.

• 🤖Does AI Make Learning Too Easy?

• 🔑Key Takeaway

Read Time: 7 minutes

🎓Key Terms

• Generative AI (GenAI): Creates entirely new content that mimics human-like creativity.

• Large Language Models (LLMs): AI Models pre-trained on vast amounts of high-quality datasets to generate human-like text.

• Rehearsal Schedules: The different ways in which people practice to remember information better. It’s all about how often and in what order we review things.

🩺 PULSE CHECK

📜THE PRELUDE

You ask ChatGPT: “How can I reply to this email in a professional tone?”

In seconds, it generates a polite, polished, and professional reply. It strikes just the right tone: firm yet friendly, sincere yet strategic, courteous yet confident. Just like that, you copy it, send it, and move on.

But here’s what didn’t happen: You didn’t consider your relationship with the recipient, explore different ways to express your thoughts, or make deliberate choices about style, emphasis, and intent.

LLMs stand to significantly improve, streamline, or accelerate our productivity. On average, U.S. workers who leverage GenAI are 33% more productive in each hour they use it. Among those who use GenAI daily, 33.5% report saving four or more hours during the workweek.

This surge in productivity raises two fundamental questions:

1. Are we accidentally shifting from active problem-solvers to passive information consumers dependent on AI?

2. What happens when AI replaces the fundamental struggle that builds genuine expertise?

As we embrace AI, it’s crucial to not only understand what it helps us do, but also what we may unintentionally lose along the way. So, let’s dive in!

💪TO LEARN, YOU MUST STRUGGLE.

⦿ 1️⃣ Struggle Enables Memorization.

In 1994, American cognitive psychologist Robert A. Bjork introduced the concept of Desirable Difficulty, which argues that learning becomes more effective when it’s more challenging. His concept relied on a key experiment conducted 16 years prior.

In 1978, American cognitive scientist Thomas K. Landauer was curious about how rehearsal patterns affect our ability to recall information. In simpler terms, he wanted to know whether how, when, or where we practice remembering something makes a difference in how well we remember it.

He conducted an experiment with roughly 468 college students that involved showing them faces paired with assigned names. Later, he asked the college students to recall the assigned names associated with the faces under various different Rehearsal Schedules, including:

• Uniform-Short Spacing: The same face-name pair was tested repeatedly in quick succession. It’s like cramming right before a final exam.

• Uniform-Moderate Spacing: The same face-name pair was tested at uniform intervals (e.g., every few minutes). It’s like reviewing flashcards regularly during a study session.

• Expanding Spacing: The same face-name pair was tested at gradually increasing intervals (e.g., every 5 seconds, then 15 seconds, then 30 seconds). It’s like spacing out study sessions over a few days.

Expanding Spacing led to the best mastery and long-term memory because gradually increasing the time between tests made the college students work harder to remember the same face-name pair.

While this method effectively supports remembering information, learning goes beyond just remembering; it involves deeply understanding the material.

⦿ 2️⃣ Struggle Enhances Understanding.

In 2010, Singaporean learning scientist Manu Kapur proposed a theory called Productive Failures, which encourages you to struggle through complex problems before receiving help on how to solve them. The theory suggests that it makes you activate prior knowledge, generate novel ideas, and develop a deeper understanding of the complex problem before ultimately receiving the correct explanation.

To validate his theory, he conducted an experiment with 71 high school students learning basic algebra, dividing them into two groups:

1. Problem-First Group: They were given basic algebra problems to solve before receiving any explanation on how to work through them.

2. Explanation-First Group: They received direct instructions on how to solve basic algebra problems before attempting them.

Despite performing worse during the initial problem-solving phase, the Problem-First Group scored up to 35% better when given basic algebra problems later. The early struggle helped them develop stronger Mental Models (MMs): internal frameworks that support deeper understanding and more effective problem-solving over time.

🤖DOES AI MAKE LEARNING TOO EASY?

⦿ 3️⃣ LLMs vs. Traditional Search?

Today, the best AI-powered tools systemically eliminate struggle.

For over two decades, we’ve turned to search engines like Google Search, typed in a search query, and received a list of blue links. It required us to sift through websites, evaluate sources, and synthesize content.

But LLMs like ChatGPT eliminate the hassle of sifting through endless websites by providing direct answers to your questions. For example, let’s say you want to learn how to build better sleep habits. Instead of spending 10 minutes skimming through blogs, articles, and forums, ChatGPT can instantly generate 10 science-backed, practical, and actionable tips to improve your sleep habits.

The entire research process is compressed. What once involved searching, evaluating, comparing, and synthesizing has been reduced to an immediate answer. In other words, it’s now a straight line from questions to answers.

You can see this change happening right now. Google Search traffic has declined from 2.3 billion daily visits to 1.7 billion daily visits since Spring 2024. Over that same period, ChatGPT has grown to process over 2.5 billion user queries (i.e., “prompts”) per day.

⦿ 4️⃣ The Illusion of Cognitive Offloading.

In theory, if AI-powered tools handle these simple questions, we can devote more time, energy, and effort toward Higher-Order Thinking (HOT), which allows us to focus on analyzing information, synthesizing ideas, and evaluating arguments. For example, instead of struggling to interpret sleep research studies, we can theoretically ponder bigger, more personal questions: “Is it worth sleeping 2 fewer hours to spend more time with friends and family?”

But in practice, that’s not how we usually spend the time we save. When ChatGPT provides instant access to an immediate answer, we rarely use those extra minutes for deeper understanding or meaningful exploration. Instead, we tend to accept the answer and move on. If a question truly matters to us, we’ll wrestle with it regardless of how quickly LLMs can answer it.

LLMs won’t drive us toward deeper reflection on their own. But do they quietly dull the mental friction that once helped us learn?!

⦿ 5️⃣ Learning With ChatGPT.

When you ask ChatGPT: “What are the best habits to improve sleep?” It offers 10 science-backed, practical, and actionable tips, like: “establish a consistent sleep schedule by going to bed and waking up at the same time each day.”

With ChatGPT, learning new sleep habits feels almost effortless and immediate, becoming a passive exercise. In other words, the convenience of instant answers can lull us into a state of cognitive laziness, where we absorb information without questioning, experimenting, or integrating it into our daily lives. This convenience can unintentionally encourage cognitive shortcuts, reducing the mental effort that solidifies learning and fuels personal growth.

To truly harness ChatGPT’s potential without losing the depth of learning, we must intentionally design moments of productive struggle into our interactions with the conversational chatbot. Rather than passively accepting the first answer it provides, challenge yourself to reflect critically on it: “Why does this answer work?” Then, experiment by applying the answer in real-world contexts to test how effective it is. This way, ChatGPT doesn’t eliminate the struggle of learning; it supplements it.

After experimenting with the answer, return to ChatGPT and share your experience by explaining what worked, what didn’t, and any new questions that arose. This feedback loop not only reinforces your learning but also allows ChatGPT to help you refine, deepen, and personalize your understanding.

🔑KEY TAKEAWAY

LLMs can inhibit learning, but they don’t have to. When used thoughtfully, they can serve as powerful learning aids that accelerate how we acquire knowledge while preserving the essential struggle that nurtures deep understanding and genuine expertise.

📒FINAL NOTE

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