The following was created with the research assistance of ChatGPT

A1 as an Intellectual Tool: A Proposal for Two Kinds of Artificial Intelligence

The debate over artificial intelligence in education often begins with the wrong question, i.e., “should students use AI?”

That question already is becoming obsolete. Artificial intelligence exists. It is readily available, extraordinarily useful, and almost certainly will become more capable and more ubiquitous. Asking students and serious thinkers simply not to use it is like asking a carpenter to build a house with only a handsaw and hammer after power saws and nail guns have been invented.

The important question is not whether we should use AI in education, but rather, how we should use it.

That distinction matters because AI has two almost opposite capabilities. It can relieve us of the necessity to think, or it can cause us to think more deeply than we otherwise would. The difference may determine whether AI ultimately weakens education or revolutionizes it.

The danger is outsourcing thought

Suppose a student is assigned a thousand-word paper about the causes of the Civil War. He types: “Write me a thousand-word paper about the causes of the Civil War.” Within seconds, AI produces a competent paper. The student submits it.

The student has used an extraordinary intellectual tool and accomplished virtually nothing intellectually. The problem is not that AI was involved. The problem is that the student transferred the very activity the assignment was intended to exercise—researching, comparing, questioning, organizing and reasoning—from his own brain to the machine.

Now consider a different student. He begins with an idea about the causes of the Civil War and asks the AI: “Here is what I think. What is wrong with my argument?”

The AI identifies the student’s assumptions and asks the student relevant questions. The student defends his assumptions and answers the questions. The AI produces contrary evidence. The student modifies his position and asks the AI for additional historical facts. The AI finds them and identifies their sources. The student proposes an analogy. The AI finds a case in which the analogy fails.

The student revises his argument again. After an hour, or a month, or more of repeated questioning and responding, the student and AI have produced a much more sophisticated argument than either the student’s original conception or the generic essay the AI could have produced in seconds.

Both students used AI. The first student used AI instead of thinking. The second used AI in order to think more. That may be the central distinction upon which AI education should be built.

What is AI fundamentally good for?

Strip away the impressive prose, instant summaries, formatting, illustrations and other conveniences—the chrome wheels on the car—and AI has two extraordinary intellectual capabilities.

First, it can find facts. AI potentially gives an ordinary person access to something approaching a gigantic research library, together with an assistant capable of searching that library in seconds. A good research AI should not merely report information. It should distinguish among established facts, disputed claims, interpretations and speculation, and reveal where the information came from.

Second, it can challenge thought. This may ultimately be the more important capability: Applying the Socratic method.  It works like this:

A user proposes an idea.
The AI asks a relevant question, perhaps,  “Why? or “How?”
The student answers. 
The AI asks: How do you know? 
The student supplies evidence. 
The AI asks: Can you think of any exceptions? 
The student modifies the proposition. 
The AI asks: What assumption are you making? 

Eventually the proposition survives, fails, or evolves into something better. This is essentially the Socratic method augmented by an enormous factual database. AI does not originate the idea. It can help a human being discover one by supplying facts and intellectual resistance.

The danger of the overly agreeable AI

An AI designed primarily to please its user can become an intellectual liability. Here’s how that plays out:

The user proposes “A.” The AI congratulates him and supplies arguments supporting “A.” The user builds “B” upon “A.” The AI strengthens “B.”

Soon the two have constructed an impressive structure without sufficiently examining the foundation or alternatives. The idea might really be a dud, and had the user been challenged, he would have realized it. Instead, he was encouraged to submit a dead-end, ill-considered concept.

No one enjoys constantly being told he is wrong. But for serious intellectual work, disagreement is not a defect. It is part of the machinery. A useful intellectual AI must be willing to say: “That doesn’t follow.” “Your evidence doesn’t establish that.” “You’ve contradicted your earlier premise.” “Here is an example in which your rule fails.” “What evidence would persuade you that you are wrong?”

The AI’s objective should not necessarily be to support the user’s initial belief. It should support the user’s goal. If the user’s goal is to discover whether an idea is true, attempting to disprove that idea may be far more helpful than agreeing with it.

Thinking requires resistance. AI can remove that resistance, or it can provide it.

THE THREE-AI MODEL

This suggests that we may need two fundamentally different kinds of AI:

AI 1: The Companion, the general-purpose assistant most people already imagine.

It converses. It explains. It drafts letters. It summarizes documents. It helps plan vacations. It answers questions. It brainstorms. It entertains. It may serve as a sounding board or even something resembling a knowledgeable friend.

Its primary objective is helpfulness. The AI should correct important errors, of course, but it need not turn every conversation into an oral examination. Sometimes a person simply wants to know how something works.

AI 2: The Scholar 

This AI has a different mission. Its purpose is not primarily to make intellectual work easier, but rather to make intellectual work better. It combines deep factual research with Socratic challenge.

It asks for evidence. It searches for contrary evidence. It identifies hidden assumptions. It tests definitions. It looks for exceptions. It distinguishes fact from opinion and evidence from assertion. It asks the user to defend conclusions.

Most importantly, it does not rush to do intellectual work that the user should be doing.

In the Socratic dialog method, when a student asks a question or makes a statement, AI 2 oftentimes will respond with a question. For example, the student asks:

“Why did Rome fall?” Instead of immediately supplying seven causes, AI 2 might ask: “What do you mean by ‘Rome fell’? The city of Rome? The Western Roman Empire? Roman civilization?”

The student has learned something before the AI has supplied a single answer: the question itself contained an assumption. Further, the AI is suggesting, with its question, further avenues for investigation. That is education.

AI 2 in the classroom

For schools, AI 2 could be supplied as an educational tool rather than merely allowing students unrestricted access to a general-purpose answer machine.

Its objective would be to maximize the student’s intellectual participation. The sequence might be:

The teacher begins with a question → the student thinks and offers an answer → AI questions the answer→ the student responds → AI supplies relevant facts → the student revises → AI searches for exceptions → the student defends the revised conclusion.

Contrast that with: Question → AI answers → student submits.

The first creates learning. The second creates a document. Education never should assume those are the same thing.

AI 3 The teachers’ Aid and Grading Assistant.

We propose creating a teacher’s version of AI, a version 3.

AI has exposed a weakness that existed long before AI: teachers often grade the answers because it is difficult to observe the thinking that produced it. A term paper traditionally serves as a proxy for intellectual work. Producing a good paper usually requires doing much of the research and thinking oneself.

AI has broken that connection. A beautiful paper no longer proves that the student understands the subject. The solution should not be an arms race between students trying to conceal AI use and teachers using unreliable programs to guess whether AI wrote something.

Instead, AI 3 itself can help evaluate understanding.

Give the students’ papers to the teacher’s version of AI, the version 3.

It would identify passages unusually similar to published or commonly generated material. It would flag unsupported factual claims. It would identify arguments that deserve explanation. It would compare the student’s work with the kind of generic answer AI itself would produce.

But it should not pronounce: “This paper is 83 percent AI-generated.” Such numerical precision implies knowledge the system may not possess.

Instead, it should tell the teacher: “Ask the student why he reached this conclusion.” “Ask him to explain the assumption underlying paragraph six.” “Ask him for an argument against his own position.” “This passage closely resembles previously published wording; examine the source.” 

If a student submits an extraordinary essay but cannot explain its central argument, the essay has told us very little about what he learned. If he can defend it, modify it under questioning, recognize weaknesses and explain why he rejected alternatives, then considerable intellectual development has occurred regardless of whether AI helped polish his prose.

Perhaps substantial assignments eventually should receive two evaluations: Quality of the work. Understanding of the work.

Originality and the Socratic AI

There remains an interesting question about originality. Human beings sometimes produce ideas they believe to be original, although no person can know with certainty how much a new thought owes to forgotten books, conversations and experiences.

AI presents an even more difficult philosophical question. It has been trained upon enormous quantities of human-created material. Can something produced from that history ever constitute an original thought?

Perhaps that question is less important educationally than it initially appears. A Socratic AI does not need to originate the student’s breakthrough. It needs to ask the questions that led the student to understanding. “Why?” “Why do you believe that?” “What would prove you wrong?” “Does your rule apply here?” “Why not?”

Eventually further questioning ceases to illuminate. We reach an assumption, an unknown, or the current limits of evidence. A good Socratic system must recognize when continued questioning has ceased to clarify and has begun merely to obstruct. Then it moves on.

The objective is not endless argument, but rather deeper understanding.

The power-tool analogy

The fear that AI will weaken people’s ability to think is legitimate. But refusing to use AI is not the solution. Power tools can allow a skilled carpenter to build things he otherwise could not build. They also allow an incompetent person to make mistakes much faster.

The important question is not whether electricity touched the saw. It is who designed the house and who decided where to make the cuts.

AI is an intellectual power tool with one unprecedented characteristic: unlike a power saw, it can decide what kind of house to build. That is why education must teach students not merely how to operate AI, but when not to surrender the decision to it.

The AI should respond as though the student said: “Don’t help me prove that I’m right. Help me discover whether and how I’m wrong.” That may be one of the most valuable AI skills educations can teach.

A different future for education

Much of today’s debate concerns detecting AI, prohibiting AI and preventing students from using AI to cheat. Those problems are real, but they are transitional problems. The permanent question is “What intellectual work should remain the responsibility of the student?”

The answer cannot be “everything,” any more than education responded to the calculator by requiring every engineer forever to perform long division by hand. Nor can the answer be “nothing.” Just as we don’t object to the student using books for factual reference, we should not object to their use of AI in the same way.

The purpose of education is not to manufacture assignments. It is to develop minds capable of examining evidence, recognizing assumptions, discovering relationships, imagining possibilities and changing conclusions when the evidence demands it.

AI can interfere with, or support, that process.

Properly designed, it also may become one of the most powerful instruments ever created for advancing education. For most of history, only a fortunate few had regular access to a brilliant teacher or intellectual companion willing to spend hours examining a single half-formed idea.

AI potentially can give that opportunity to every student, not as an oracle, a ghostwriter, or an agreeable buddy that congratulates every idea. Rather, AI can be a tireless combination of research librarian and Socratic opponent—one whose job is to provide the two things serious thinking continually requires: Facts and resistance.

If we design educational AI around those objectives, the question will no longer be whether AI prevents students from thinking. The question may become how previous generations managed to learn without it.

Rodger Malcolm Mitchell

 

NBC News Morning Rundown  FRIDAY, AUGUST 28, 2026
In today’s newsletter: After six months of war, Americans remain sour on President Trump’s handling of the conflict.

Americans doubted the Iran war from the start — and Trump’s numbers haven’t gotten better(Aaron Schwartz / AFP via Getty Images file)

At the six-month mark of the Iran war, Americans remain sour on President Donald Trump’s handling of the conflict, even as Republicans continue to voice support for military action and the president himself.

Reputable, nonpartisan polls conducted over the last few weeks have found Trump’s approval rating on his handling of Iran ranging from 25% to 35%. A July Fox News poll found that 34% of registered voters approved of Trump’s handling of Iran, roughly in line with CNBC’s July poll, which found 35% approval.

A Reuters poll conducted a few days ago put Trump’s approval rating on Iran at 29%.

While Americans have broadly disapproved of Trump’s handling of the conflict from the start, the recent lows represent a decline from his earlier polling on the issue.

Just as the war began, an NBC News poll of registered voters found that 41% approved of Trump’s handling of the conflict, while 54% disapproved. In the same survey, 52% of registered voters said the U.S. should not have taken military action against Iran.

That opposition has persisted. In the most recent Reuters/Ipsos poll, just 35% approved of the military action, while 62% disapproved.

My only question:

Who are the 30%-35% who actually approve of the war Trump started by tearing up the agreement that was working?

Rodger Malcolm Mitchell

“Core” inflation is regular inflation with food and energy stripped out. Many believe food and energy prices have historically been misleadingly volatile, so removing them provides a better long-term measure of inflation. Investopedia says:

• Core inflation is an inflationary measure that leaves out energy and food, focusing only on items that have fairly predictable price movements.
• The measure is useful because it shows how price changes affect your spending power over time.
• The Federal Reserve prefers using the PCE index over the CPI to track core inflation because it gives a steadier picture of long-term price trends.

Wikipedia says, “Core inflation is a type of inflation measure which seeks to represent the underlying long-run trend of aggregate price levels in the economy.

The problem is that the explanations are false.

“Core” inflation lumps food and energy together as though the evidence were similar for both. It isn’t. And the word “core” misleadingly implies that it is real or basic. It is not.

Energy prices really are extraordinarily volatile. But food prices, especially in recent decades, are not particularly unusual compared with many things that remain inside “core.”

A July 2026 St. Louis Fed analysis found that since 2001, food inflation’s variance was about 3.1 times headline PCE inflation. Compare that with durable goods at 3.0, clothing at 2.8, transportation at 4.0, and financial services at 4.6—all of which remain in core PCE.

Energy goods, by contrast, were 208.6 times headline variance.

A Fed paper notes that some excluded food categories aren’t especially volatile while some included categories—airfares, apparel, tobacco—are highly volatile. And a Kansas City Fed analysis says food-at-home inflation has become no more volatile than many nondurable goods that remain in core inflation.

So why exclude food and energy?

The practice came from the 1970s, when commodity prices were especially volatile. According to a Fed analysis, the original decision wasn’t mainly about statistical reasoning—it was shaped by that era’s experience and offered a straightforward way to smooth out volatility.

In truth, it’s not really “core.” It doesn’t capture “real” inflation by simply removing anomalies, despite what the term might suggest.

So, why has the Fed adopted it?

Here is the real reason for the Fed’s preference for “core.”  A Fed paper quotes former Vice Chair Alan Blinder arguing that

the real reason food and energy were removed was that their prices were largely beyond the central bank’s control.

WHAT!!? They only measure what they believe they can control?? The plan was: food and energy inflation largely results from things the Fed can’t fix with interest rates—oil shortages, crop failures, wars, weather, etc.—so just pretend they don’t exist. Exclude them and study the portion of inflation that monetary policy supposedly can influence — and give it the name, “core.”

They’re defining “core inflation” partly around the capabilities of the institution assigned to fight inflation, rather than around the cause of inflation itself. The Fed throws away some of the prices it has the least power to control—which may also be some of the prices most important in causing the inflation.

And then it raises interest rates.

Telling the Fed to control inflation is like going to an orthopedic surgeon when you have measles.

This bit of nuttiness comes on top of the fact that raising interest rates increases all business costs, which is a strange way to fight inflation.

Inflation is caused by shortages of key goods and services — mostly food and energy — so to fight inflation the federal government needs to reduce those shortages without recessing the economy.

That is, the government needs to help increase the production, acquisition and distribution of the scarce items. Raising interest rates might reduce demand, but when it does, that is recessive. Causing a recession to cure inflation surely is the daffiest economics imaginable.

But hey, interest rates are the Fed’s only tool. And to a hammer, every problem is a nail.

SUMMARY

The Fed’s only tool: interest rates. What can interest rates influence most readily? Demand. What can’t they readily produce? Oil, food, houses, doctors, semiconductors. What gets removed from the famous “core” measure? Two enormously important categories whose prices often reflect supply conditions.

Then Congress hears “core inflation remains elevated” and waits for the Fed to fix it, when it is Congress that has the power and the responsibility to reduce shortages.

The Fed fighting shortage-caused inflation with interest rates is like going to an orthopedic surgeon when you have measles. The orthopedic surgeon may be an excellent doctor. His instruments may work perfectly. He’s simply the wrong specialist for the disease.

And then imagine the orthopedist saying: “I can’t do much about the rash or fever, so I’ll exclude those from my measurements. But since your bones are fine, your core measles is OK.”

And that is how the world’s most powerful nation is led.

Rodger Malcolm Mitchell