🧠 Is GenAI Making Us Smarter or More Dependent?

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Welcome back AI prodigies!

In today’s Sunday Special:

• 📜The Prelude

• ⚔️Automation: A Double-Edged Sword

• ✈️Aviation Tragedy From Automation

• 🤖How to Minimize GenAI Dependence

• 🔑Key Takeaway

Read Time: 7 minutes

🎓Key Terms

• Generative AI (GenAI): When AI models create new content such as text, images, audio, video, or code.

• Maneuvering Characteristics Augmentation System (MCAS): Software that automatically adjusts the angle of a plane’s nose to stabilize it.

🩺 PULSE CHECK

📜THE PRELUDE

The Walton Family Foundation (WFF) recently found that nearly half of U.S. K-12 Students rely on OpenAI’s ChatGPT to finish homework, write essays, or study for exams. In response, educators are embracing GenAI in the classroom by teaching Prompt Engineering Techniques and showing how to use Conversational Chatbots. While this approach is reasonable, it overlooks a more fundamental issue.

Critical thinking is essential to education: it comes from the Greek word “Κριτικός (i.e., Kritikos),” which means “able to judge or discern.” Knowing how to judge is important because it requires Higher-Order Thinking: the ability to analyze situations and evaluate decisions. It’s important in the classroom and crucial in everyday choices. For instance, deciding which career path to pursue or who to spend the rest of your life with. So, learning to be a critical thinker is vital to making the right choices in your life.

U.S. K-12 Students frequently use GenAI to generate ideas, summarize texts, and solve problems. However, this reliance on GenAI risks shifting them from active critical thinkers to passive information consumers. As GenAI becomes more prevalent in the classroom, we must ask ourselves, “How does it impact a student’s capacity to practice critical thinking?”

⚔️AUTOMATION: A DOUBLE-EDGED SWORD

After observing this reliance on GenAI in classrooms, research associate and experienced educator Nicolas Spatola established the Efficiency-Accountability Tradeoff. It posits that the more we rely on GenAI to streamline tasks, the less capable we become of performing those tasks ourselves.

Unlike Search Engines like Google Search or Microsoft Bing, which require sifting through websites, evaluating sources, and synthesizing content, GenAI generates content within seconds. When we reduce the need to read and analyze information, we risk outsourcing critical thinking to GenAI. This tradeoff isn’t unique to GenAI; new technology often outsources mental power or physical effort to machines. For example, railroads allowed us to forget how to operate horse-drawn carriages, and matches enabled us to forget how to start fires from scratch.

But GenAI introduces a new paradigm. Unlike most new technology, which automates tasks or simplifies procedural knowledge, GenAI often works alongside us as we tackle cognitively demanding work. That’s why we must be careful not to let GenAI think on our behalf, as the cost of cognitive outsourcing can be detrimental to critical thinking.

✈️AVIATION TRAGEDY FROM AUTOMATION

Meredith Carroll, a professor of Aviation Human Factors at the Florida Institute of Technology (FIT), outlined how Human-Computer Interaction on the Modern Flight Deck can lead to a pilot’s increased reliance on automation, resulting in catastrophic consequences.

On October 29th, 2018, at 7:20 AM Local Time, Lion Air Flight 610 departed from Soekarno-Hatta International Airport in Indonesia. Just 13 minutes into the flight, the MCAS falsely indicated that the plane’s nose was too high and repeatedly pushed it down. The pilots were unaware of how MCAS functioned and lacked training to disable it. So, they struggled to regain control of the plane as it went into an unrecoverable dive and crashed into the Java Sea, killing all 189 passengers.

This incident underscores a broader issue in modern aviation: automation dependency. The increased reliance on automation has reduced a pilot’s manual flying proficiency. As a result, pilots are ill-equipped to handle sudden automation malfunctions. In other words, neglecting manual skills can leave pilots less prepared when automation fails. Pilots must remain proficient at flying manually.

Similarly, excessive dependency on GenAI in the classroom can weaken a student’s ability to analyze situations and evaluate decisions when solving problems. Students must use GenAI to augment, not replace, their problem-solving process.

🤖HOW TO MINIMIZE GENAI DEPENDENCE

To minimize your reliance on GenAI, you can take the following steps:

1. Challenge Yourself First: Before prompting GenAI, ask yourself: “Why is the problem relevant?” and “Why does it exist?”

2. Reverse Engineer: When you receive a GenAI response, identify the assumptions behind it. Ask yourself: “What does this assumption rely on?” Once you’ve mapped out these assumptions, logically trace them backward, questioning how each assumption leads to a conclusion and whether an alternate assumption could lead to a different outcome.

3. Correct GenAI: Instead of just correcting a GenAI response, build an intentionally flawed version of it first. Let it be full of gaps, errors, and contradictions. Then, critique it.

4. In-Depth Analogy: Transform the complex idea you’re inquiring about into an in-depth analogy from a different field or domain. If you’re learning how the economy works, compare it to an ecological framework like the food chain. Then, ask GenAI for feedback on your analogy.

5. Change the Time Frame: Ask GenAI to answer problems as if it were 10, 50, or 100 years in the past or into the future. Consider how past strategies could be modernized or how the future world might reframe present-day problems.

🔑KEY TAKEAWAY

Overreliance on GenAI in the classroom can hinder a student’s ability to think critically. While GenAI can be helpful, it’s crucial to avoid outsourcing cognitively demanding work to it. Like how pilots need to maintain manual flying skills, students must continue to practice being active critical thinkers even with the aid of GenAI by challenging the technology’s assumptions and reverse-engineering prompts.

📒FINAL NOTE

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