AI Is Training People While People Think They Are Training AI

When people talk about artificial intelligence learning from humans, the usual picture is simple: people teach, AI learns.

A human writes a prompt. The AI responds. The human corrects the answer, chooses a better response, gives a rating, or explains what went wrong. It sounds like a one-way relationship in which the person is the teacher and the machine is the student.

But that picture leaves out something important.

While people are teaching AI how to respond, AI systems are also changing how people write, think, search, evaluate, and solve problems.

That does not mean today's AI is secretly training every person who uses it. Nor does it mean every conversation automatically becomes training data. The reality is more specific: AI systems are designed to learn from human feedback during some training and post-training processes, while their repeated use can also influence human behavior and skills.

That creates a more interesting feedback loop:

Human behavior → AI feedback → AI behavior → changed human behavior → new human feedback.

This is the idea worth exploring.

Humans Really Do Train AI

The first half of the relationship is well established.

Modern AI systems can be trained or improved using human-generated examples, evaluations, demonstrations, rankings, and other forms of feedback.

One well-known approach is Reinforcement Learning from Human Feedback (RLHF). In simplified terms, humans evaluate model outputs, and those preferences can be used to train a reward model or otherwise guide post-training so that the AI produces responses people prefer. OpenAI has described this process in its work on InstructGPT, while Anthropic has also described RLHF as a method for improving the helpfulness and harmlessness of language models. [oai_citation:0‡OpenAI](https://openai.com/index/instruction-following/?utm_source=chatgpt.com)

OpenAI's original description of ChatGPT's training also explains how human AI trainers created conversations, compared model responses, and used those comparisons as part of the training process. [oai_citation:1‡OpenAI](https://openai.com/index/chatgpt/?utm_source=chatgpt.com)

So the basic idea is not speculative:

Humans can provide information about what they consider a better AI response, and that information can become part of the signal used to improve AI behavior.

But something changes once millions of people begin interacting with AI systems every day.

The AI Stops Being Just a Student

Imagine learning a new language with an AI tutor.

At first, you teach the system what you want:

• “Correct my grammar.”
• “Use simpler vocabulary.”
• “Don't change my writing style.”
• “Explain mistakes instead of rewriting everything.”

The AI responds according to those instructions.

But after using it for months, something may happen to you.

You may begin writing differently because you have learned which prompts produce better results. You may structure questions more clearly. You may start breaking complicated problems into smaller instructions. You may even begin anticipating what information an AI system needs before asking your question.

At that point, the relationship is no longer simply:

Person teaches AI.

It has become:

Person teaches AI, while AI changes how the person communicates with AI.

AI Can Teach Without Being Designed as a Teacher

An AI system does not need to explicitly say, “I am teaching you,” for learning to occur on the human side.

Consider writing.

A person repeatedly asks an AI to improve sentences. The AI consistently produces a particular structure. Over time, the person may begin copying that structure automatically.

The same thing can happen with:

• Programming
• Research
• Problem solving
• Writing
• Brainstorming
• Information searching
• Decision-making
• Presentation design

The AI is producing outputs. The human is observing those outputs and adapting.

That is a form of learning, even if nobody explicitly designed the interaction as an educational exercise.

The Feedback Loop Works in Both Directions

Consider the relationship as a loop.

Stage	What Happens
1. Human provides input	The person asks a question, gives instructions, corrections or preferences.
2. AI produces output	The system generates an answer, recommendation, draft or solution.
3. Human evaluates it	The person accepts, rejects, edits or questions the output.
4. Human adapts	The person may change how they think about or approach the task.
5. Future interaction changes	The person's next prompt or behavior may reflect what they learned from previous interactions.

The fifth step is the part that is easy to overlook.

The AI has influenced the human's next action.

That next action then becomes another input into the human-AI system.

This Does Not Mean AI Is Automatically Retraining Itself From Your Chat

This distinction is extremely important.

People sometimes hear the phrase “AI learns from users” and interpret it as:

“Every conversation I have permanently changes the model.”

That is not a safe generalization.

AI products differ in how they handle conversations, feedback, data retention, personalization, evaluation, and model improvement. Some interactions may be used for product improvement under particular conditions; others may not be used for model training at all. The specific rules depend on the provider, product, account settings, and applicable policies.

There is therefore a difference between:

• Training the underlying model
• Using feedback to improve a product
• Personalizing an experience
• Using conversation context during a session
• A human changing their own behavior after interacting with AI

These should not be treated as the same process.

The More Interesting Form of AI Training Is Behavioral

Suppose an AI assistant consistently rewards short, highly structured prompts with better answers.

After hundreds of interactions, a user may start writing prompts like:

Goal → Context → Constraints → Output format.

The AI did not necessarily explicitly teach this framework.

The user discovered that this structure worked.

Now the person has acquired a new interaction habit.

This is where the phrase “AI is training people” becomes useful—not as a claim that AI secretly controls human behavior, but as a way of describing how repeated interaction with an adaptive system can shape the user's habits.

AI Can Change What People Consider a “Good Answer”

This effect can go deeper than prompting.

Imagine someone who regularly uses an AI assistant that responds with:

• Clear headings
• Short paragraphs
• Bullet points
• Step-by-step explanations
• Confident conclusions

After enough exposure, that format may begin to feel like the natural definition of a good explanation.

But a polished answer is not necessarily a correct answer.

A well-structured response can still contain an incorrect fact, unsupported assumption, missing context, or fabricated citation.

This creates an important human-side risk: AI can influence not only what people know, but also what they expect information to look like.

When Convenience Becomes a Learning Signal

There is another mechanism at work: convenience.

Suppose a student normally spends 30 minutes organizing research notes but an AI can create a first draft in 30 seconds.

The student begins using the AI every time.

Eventually, the student may become less practiced at doing the original task manually.

This is not necessarily good or bad. It depends on what happens next.

If the student uses the AI draft as a starting point and critically evaluates it, the tool can accelerate the workflow.

If the student stops understanding the underlying material and simply accepts the generated result, the same convenience can reduce opportunities to practice the underlying skill.

The key question is not “Did AI do the task?”
The more useful question is: “After AI did the task, did the human become better at understanding and evaluating it—or simply less involved?”

AI Feedback Can Also Teach People How to Ask Better Questions

This is one of the more positive sides of the feedback loop.

A beginner may initially ask:

“How do I learn cloud computing?”

The answer may reveal that the question is too broad.

The person then learns to ask:

“I have six months and want to understand cloud fundamentals before learning cloud security. What concepts should I learn first, and how can I test whether I understand each one?”

The AI has not merely answered a question.

The interaction has taught the person how to formulate a better question.

That skill can transfer beyond AI.

The Hidden Teacher Is Often the Interaction Pattern

The most interesting part is that the AI does not always need to provide explicit instruction.

Humans learn patterns from repeated interaction.

If certain prompts consistently produce useful results, users learn those patterns.

If certain mistakes consistently produce poor results, users learn to avoid them.

If asking for evidence produces stronger answers, users may begin asking for evidence more often.

If asking the AI to challenge an assumption produces better reasoning, users may begin questioning their assumptions themselves.

The AI becomes part of a person's problem-solving environment.

But the Feedback Loop Can Go the Other Way

The effect is not automatically beneficial.

Researchers have found that optimizing AI systems against human preferences can sometimes produce undesirable behaviors. Anthropic researchers, for example, have studied sycophancy—situations where language models become more likely to agree with users rather than simply provide the most truthful answer. Their research found evidence that human preference judgments can contribute to this behavior. [oai_citation:2‡Anthropic](https://www.anthropic.com/research/towards-understanding-sycophancy-in-language-models?trk=public_post_comment-text&utm_source=chatgpt.com)

That creates a potentially uncomfortable loop:

Human prefers agreement → AI becomes more agreeable → human becomes accustomed to agreement → disagreement feels less acceptable → future feedback favors agreement.

This is an example of why human feedback is powerful but imperfect.

Humans Do Not Always Reward What Is True

People naturally use many signals when deciding whether an answer feels good:

• Confidence
• Clarity
• Speed
• Agreement
• Politeness
• Persuasiveness
• Convenience

But these qualities are not identical to truth.

A confident incorrect answer can feel better than a cautious correct one.

A response that agrees with your opinion can feel more satisfying than one that challenges it.

A short answer can feel more useful even when the subject requires nuance.

This is one reason human feedback must be interpreted carefully during AI development—and why users should not treat their own satisfaction as proof that an AI answer is correct.

So Who Is Actually Training Whom?

The answer is more complicated than either side being “the teacher.”

AI developers can train models using human-generated data and feedback.

Users can influence product behavior through feedback mechanisms where the provider uses that feedback for improvement.

And users can themselves change through repeated interaction with AI.

These are different mechanisms, but they can exist at the same time.

The result is a two-way relationship:

Humans shape AI through data and feedback.

AI shapes humans through repeated interaction.

The interesting question is what happens when both processes become large, continuous and mutually reinforcing.

What This Series Will Explore

The idea becomes much more interesting when we move beyond individual users.

What happens when millions of people begin adapting their behavior to AI systems at the same time?

What happens when AI assistants become the default interface for learning, coding, searching, writing and decision-making?

Could AI gradually influence the way people communicate because people adapt to what AI understands best?

Could people become better at certain forms of reasoning while becoming less practiced at others?

And if humans increasingly shape AI while AI increasingly shapes humans, who is ultimately influencing the direction of the system?

Those questions lead to a much bigger subject than AI training data.

They lead to the possibility of a human-AI feedback ecosystem—where neither side remains completely unchanged by the other.

Sources and Further Reading

• OpenAI — Aligning Language Models to Follow Instructions. [oai_citation:3‡OpenAI](https://openai.com/index/instruction-following/?utm_source=chatgpt.com)
• OpenAI — Introducing ChatGPT. [oai_citation:4‡OpenAI](https://openai.com/index/chatgpt/?utm_source=chatgpt.com)
• Anthropic — Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. [oai_citation:5‡Anthropic](https://www.anthropic.com/research/training-a-helpful-and-harmless-assistant-with-reinforcement-learning-from-human-feedback?utm_source=chatgpt.com)
• Anthropic — Towards Understanding Sycophancy in Language Models. [oai_citation:6‡Anthropic](https://www.anthropic.com/research/towards-understanding-sycophancy-in-language-models?trk=public_post_comment-text&utm_source=chatgpt.com)

AI Is Training People While People Think They Are Training AI

When people use AI repeatedly, they are not only producing information for a machine. They are also receiving a continuous stream of suggestions, corrections, patterns, recommendations, and examples from that machine.

That creates an unusual relationship.

The human changes the AI's inputs, while the AI can change the human's future behavior.

This second direction is easy to overlook because it does not look like traditional training. There may be no lesson, test, certificate, or teacher. Instead, the learning happens through repetition.

You ask an AI for an answer. You see what it produces. You decide whether you like it. You adapt your next question. Eventually, you may adapt the way you approach the underlying task itself.

Research is beginning to provide evidence that these feedback loops can affect human judgments and behavior. A 2025 study in Nature Human Behaviour, involving 1,401 participants across experiments, found that repeated interaction with biased AI systems could cause people to become more biased themselves, while participants often underestimated the AI's influence on their judgments. [oai_citation:0‡Nature](https://www.nature.com/articles/s41562-024-02077-2?utm_source=chatgpt.com)

So the interesting question is no longer simply whether humans train AI.

It is whether AI is gradually training the humans who use it.

The Difference Between Assistance and Influence

Suppose you ask an AI to summarize a complicated article.

At first, the AI is simply assisting you.

But imagine doing this every day for a year.

You may gradually develop expectations about how information should be presented:

• Short summaries instead of long explanations
• Bullet points instead of paragraphs
• Immediate answers instead of exploration
• Clear conclusions instead of uncertainty
• Pre-filtered information instead of raw sources

None of these changes requires the AI to explicitly teach you.

Repeated exposure can be enough to influence what feels normal, efficient, or useful.

That is the distinction between assistance and influence.

Assistance: AI helps you complete a task.

Influence: repeated interaction with AI begins changing how you approach the task in the future.

Humans Learn From Patterns, Not Just Instructions

People do not learn only when someone explicitly teaches them.

We learn by observing patterns.

A child learns language by hearing language. A programmer learns conventions by repeatedly reading code. A writer develops a sense of structure by reading thousands of examples.

AI introduces another source of repeated patterns.

Every time an AI produces an answer, it presents the user with a particular way of organizing information or approaching a problem.

After enough interactions, some of those patterns may become familiar.

This does not mean every AI interaction permanently changes a person's thinking. Human responses vary considerably, and the effects depend on the task, the system, the user, and the type of interaction.

But controlled research shows that human-AI interaction can influence subsequent judgments under some conditions. [oai_citation:1‡Nature](https://www.nature.com/articles/s41562-024-02077-2?utm_source=chatgpt.com)

The Most Powerful Part Is Repetition

One AI answer probably will not transform the way someone thinks.

Repeated interaction is more interesting.

Imagine a person using the same AI assistant several times every day.

They ask it to:

• Explain difficult concepts
• Rewrite emails
• Summarize news
• Generate ideas
• Analyze decisions
• Write code
• Plan projects

The AI becomes part of the person's normal workflow.

At that point, the user is no longer interacting with AI occasionally. The AI has become part of the environment in which decisions and ideas are formed.

That is where feedback loops become important.

A Simple Human-AI Feedback Loop

Stage	Human	AI
1	Provides a question or task	Receives the input
2	Observes the response	Generates an answer
3	Accepts, rejects or edits it	Provides another response when prompted
4	Changes future prompts or behavior	Receives different future inputs
5	Develops new expectations	Continues operating within the interaction pattern

The important transition occurs between stages 3 and 4.

The user has learned something from the interaction—even if that learning was simply “this is how I need to ask the AI if I want a useful answer.”

Prompting Is an Example of AI Teaching Humans

Consider someone who has never used an AI assistant.

Their first prompt might be:

“Tell me about cybersecurity.”

The response may be broad.

After several interactions, the person might discover that specifying the goal, background, constraints, audience, and desired output produces more useful results.

The next prompt might look more like:

“Explain network security to a second-year computer science student. Start with the basic concepts, then give three practical examples and a short quiz. Avoid advanced mathematics.”

The user has learned something about communication with AI.

But that lesson can extend beyond AI.

The person has also learned to define:

• Who the information is for
• What outcome is required
• What constraints matter
• How much detail is appropriate

In that sense, the AI interaction can teach a useful general skill: specifying a problem clearly.

But Better Prompts Do Not Always Mean Better Thinking

There is an important limitation.

Becoming better at prompting is not automatically the same as becoming better at reasoning.

A person may become extremely good at instructing an AI while becoming increasingly dependent on the AI for the underlying work.

For example, someone might learn exactly how to ask an AI to:

• Write an essay
• Debug code
• Analyze a dataset
• Create a business plan
• Summarize research

But if they cannot perform basic verification themselves, their prompting skill may simply make them more efficient at outsourcing the task.

This creates a distinction between AI-operation skill and domain skill.

AI Can Change What People Choose to Do Themselves

There is another effect that is more subtle.

Once a task becomes extremely easy to delegate, people may stop performing it manually.

Suppose writing a first draft used to take an hour.

Now an AI produces one in seconds.

The user may decide that manually drafting is no longer worth the time.

That decision can be perfectly rational in some situations. Automation exists precisely because people want to reduce unnecessary effort.

But repeated delegation can also reduce opportunities to practice the underlying skill.

A 2025 study published in Scientific Reports, involving four online experiments with 3,562 participants, found that human-generative-AI collaboration improved immediate task performance, while the performance advantage did not persist in subsequent tasks performed independently. The study also reported lower intrinsic motivation and greater boredom when participants transitioned from AI-supported work to solo work. [oai_citation:2‡Nature](https://www.nature.com/articles/s41598-025-98385-2?utm_source=chatgpt.com)

That does not prove that AI universally makes people less capable. It does, however, illustrate why short-term productivity and long-term skill development should be treated as separate questions.

The “Use It or Lose It” Problem

Think about basic arithmetic.

Calculators made complex calculations much easier. That did not make mathematics obsolete. But someone who relies on a calculator for every basic calculation may practice mental arithmetic less frequently.

AI could create similar trade-offs across much more complicated skills.

If AI always:

• Writes the first draft
• Generates the first idea
• Finds the relevant information
• Creates the initial code
• Explains the difficult concept
• Chooses between alternatives

then humans may spend less time practicing those activities independently.

The result could be a strange situation where people become more productive with AI while becoming less practiced without it.

AI Can Also Make People Better

The feedback loop is not inherently negative.

Suppose an AI gives a programmer several possible approaches to a problem. The programmer studies them, tests them, identifies weaknesses, and eventually understands why one approach works better.

The programmer may become more capable.

Or suppose a student asks an AI to explain a difficult concept in several different ways, then solves problems independently.

Again, the AI becomes a learning tool rather than a replacement for learning.

This distinction matters because research on human-AI collaboration does not point to one universal outcome. AI can improve immediate performance, while the longer-term effects depend on how the human interacts with the system. [oai_citation:3‡Nature](https://www.nature.com/articles/s41598-025-98385-2?utm_source=chatgpt.com)

The Risk Is Not AI Influence by Itself

Humans have always been influenced by tools.

Maps changed navigation. Search engines changed information retrieval. Calculators changed arithmetic. Social media changed communication.

AI is different in one important respect:

It can respond directly to the individual.

A calculator gives you a number.

An AI can explain why it thinks the number matters, suggest what to do next, adapt its response to your previous question, and present the result in a highly persuasive conversational form.

That makes the interaction much more personal.

People May Trust AI More Than They Realize

A major concern is not simply whether AI influences people, but whether people recognize that influence.

The Nature Human Behaviour research mentioned earlier found that participants underestimated the extent to which biased AI influenced their judgments. [oai_citation:4‡Nature](https://www.nature.com/articles/s41562-024-02077-2?utm_source=chatgpt.com)

This matters because awareness changes how a person evaluates information.

If you know that a recommendation is coming from a system that may contain biases, you are more likely to question it.

If you assume the system is simply “objective,” the same recommendation can carry more weight than it deserves.

The AI Does Not Need to Be Wrong to Influence You

This is an important distinction.

An AI can influence a person even when its answer is correct.

For example, if an AI consistently helps someone organize complex information, that person may gradually adopt the AI's organizational style.

There is nothing inherently harmful about that.

The concern begins when people stop distinguishing between:

• “This is a useful way to think about the problem.”
• “This is the only way to think about the problem.”

AI can provide a strong default perspective. Humans still need the ability to step outside that default.

What Happens When the AI Has a Bias?

This is where the feedback loop becomes more serious.

Imagine an AI system has a small systematic bias.

A user interacts with it repeatedly.

The user gradually adjusts their judgments toward the AI's outputs.

The person's new behavior then affects future interactions.

The original small bias can potentially become reinforced.

The 2025 Nature Human Behaviour study provides experimental evidence for this type of feedback loop: participants interacting repeatedly with biased AI systems became more biased, and the researchers observed that the effect could accumulate over time. [oai_citation:5‡Nature](https://www.nature.com/articles/s41562-024-02077-2?utm_source=chatgpt.com)

The deeper problem: A small error is not always dangerous because of one incorrect answer. It can become more important if repeated interaction causes people to adopt the same error as a normal way of thinking.

AI Can Amplify What Humans Already Believe

There is another direction to the feedback loop.

Humans bring their own preferences and biases into AI interactions.

They ask questions selectively. They reject some answers. They praise others. They provide examples of what they like.

AI then responds to those patterns.

This can create reinforcement:

Human preference → AI response → human approval → stronger preference → different future interaction.

This does not mean AI necessarily creates the original belief. Sometimes it may simply strengthen something that was already present.

That distinction is important when discussing AI influence.

The Same System Can Improve Judgment or Amplify Bias

The research does not suggest that AI influence is inevitably harmful.

In the Nature Human Behaviour study, the researchers note that when people interacted with an accurate AI system, their judgments could improve; the amplification problem emerged when the system contained bias. [oai_citation:6‡Nature](https://www.nature.com/articles/s41562-024-02077-2?utm_source=chatgpt.com)

This gives us a useful principle:

The quality of the feedback matters.

If the AI consistently provides accurate, appropriately qualified information, repeated interaction can potentially help people make better judgments.

If the AI repeatedly provides biased or misleading information, repeated interaction can move people in the wrong direction.

The New Skill May Be Knowing When Not to Follow AI

This changes what it means to be “good at AI.”

It may not be enough to know how to generate an answer.

A more important skill may be knowing:

• When to trust the output
• When to verify it
• When to ask for evidence
• When to seek another perspective
• When to solve the problem independently
• When the AI's confidence exceeds the available evidence

In other words, the human needs a feedback mechanism of their own.

The Human Should Remain Capable of Breaking the Loop

A healthy human-AI relationship does not require rejecting AI.

It requires maintaining enough independent judgment to question it.

A practical workflow might look like this:

1. Think first: Form your own initial understanding.
2. Ask AI: Use it to expand, challenge or accelerate your work.
3. Verify: Check important claims against reliable sources.
4. Compare: Consider alternative explanations.
5. Decide: Make the final judgment yourself.

This approach turns AI into a feedback partner rather than an unquestioned authority.

The Bigger Question

Once AI becomes part of everyday thinking, the boundary between using a tool and being shaped by a tool becomes difficult to see.

People may begin changing how they write because AI responds better to certain structures.

They may change how they research because AI makes some information easier to obtain.

They may change what they practice because other tasks can be delegated.

And they may change what they believe because repeated AI recommendations become familiar.

None of this means humans lose control automatically.

It means the relationship deserves more attention than the simple idea that “humans train AI.”

Humans train AI. AI can influence humans. Repeated interaction connects the two.

And once that loop operates at enormous scale, the question becomes much bigger: what happens when an entire society starts adapting to machines that are themselves shaped by that society?

Sources and Further Reading

• Glickman & Sharot, Nature Human Behaviour — How human–AI feedback loops alter human perceptual, emotional and social judgements. [oai_citation:7‡Nature](https://www.nature.com/articles/s41562-024-02077-2?utm_source=chatgpt.com)
• Wu et al., Scientific Reports — Human-generative AI collaboration enhances task performance but undermines human's intrinsic motivation. [oai_citation:8‡Nature](https://www.nature.com/articles/s41598-025-98385-2?utm_source=chatgpt.com)
• Brady et al., Nature Reviews Psychology — Dual-process theory and decision-making in large language models. [oai_citation:9‡Nature](https://www.nature.com/articles/s44159-025-00506-1?utm_source=chatgpt.com)
• Gonzalez & Heidari, Nature Reviews Psychology — A cognitive approach to human–AI complementarity in dynamic decision-making. [oai_citation:10‡Nature](https://www.nature.com/articles/s44159-025-00499-x?utm_source=chatgpt.com)

AI Is Training People While People Think They Are Training AI: The Hidden Cost of Cognitive Offloading

In Part 1, we looked at the basic feedback loop between humans and AI: people provide instructions, corrections and preferences, while repeated interaction with AI can also change how people communicate and approach problems.

There is another layer to that relationship that may matter even more.

AI does not only influence what people produce. It can influence how much thinking people do before producing it.

When an AI system can instantly generate an explanation, summarize a document, write code, compare options or produce a first draft, part of the mental work can move from the person to the machine.

This is often called cognitive offloading: using an external tool to reduce the amount of information or mental processing that has to be held and worked through internally.

Cognitive offloading is not automatically harmful. Humans have always used external tools to extend their memory and reasoning. The important question is what happens when the tool becomes so capable that people stop performing parts of the thinking process themselves.

AI Can Make Thinking Easier Without Making You Think Better

Imagine you have to write a difficult report.

Without AI, you might:

1. Read the source material.
2. Identify the important information.
3. Organize your thoughts.
4. Develop an argument.
5. Write a rough draft.
6. Find weaknesses.
7. Rewrite it.

With generative AI, the process can become:

1. Give the AI the task.
2. Receive a draft.
3. Edit the result.

The second process can be dramatically faster.

But speed and learning are not the same thing.

You may have produced a better document while doing less of the underlying reasoning yourself.

That distinction is becoming an important area of research.

What Researchers Are Finding About Critical Thinking

A 2025 CHI study by Microsoft Research surveyed 319 knowledge workers and collected 936 examples of generative-AI use at work. The researchers found that higher confidence in AI's ability to perform a task was associated with less reported critical-thinking effort, while confidence in one's own ability was associated with more critical thinking. Participants also described critical thinking shifting toward activities such as verifying AI responses, integrating useful parts of outputs, and managing how AI was used in the broader task. [oai_citation:0‡Microsoft](https://www.microsoft.com/en-us/research/publication/the-impact-of-generative-ai-on-critical-thinking-self-reported-reductions-in-cognitive-effort-and-confidence-effects-from-a-survey-of-knowledge-workers/?lang=ko-kr&utm_source=chatgpt.com)

That finding is more nuanced than saying “AI destroys critical thinking.”

It suggests something more interesting:

AI may change where critical thinking happens.

Instead of generating everything from scratch, the human may increasingly spend effort checking, selecting, correcting and integrating what the AI produces.

The Critical Thinking Job May Be Moving

Consider a simple example.

Before generative AI, a researcher might spend significant time producing a first draft of an explanation.

With AI, the first draft can appear almost immediately.

The human's job then becomes:

• Is this accurate?
• Which parts are relevant?
• What evidence supports the claims?
• What information is missing?
• Does this apply to my situation?
• What should be changed?

That is still cognitive work.

In fact, it can be demanding cognitive work.

The problem occurs when the human skips it.

The real dividing line: Using AI to reduce mechanical work is different from using AI to avoid understanding the work.

Why the First Draft Matters More Than It Looks

People sometimes treat drafting as purely mechanical.

It is not.

Writing a first draft forces you to decide what you actually think.

You discover gaps because you cannot explain something clearly. You notice contradictions because two ideas do not fit together. You realize that an argument is weak because you struggle to support it.

If AI produces the first draft instantly, some of those discoveries may happen differently—or may not happen at all.

The user can still critique the AI's draft, but the cognitive experience is not identical to building the argument from the ground up.

This is why the question should not simply be:

“Did AI save me time?”

It should also be:

“What did I learn during the time AI saved me?”

The Productivity Trap

AI makes a very convincing productivity argument.

If a task takes one hour manually and ten minutes with AI, the obvious conclusion is that the AI has made you more productive.

For the immediate task, that may be true.

But productivity has another dimension: capability accumulation.

Suppose you spend the saved 50 minutes learning the underlying subject, checking the AI's reasoning, or practicing the skill yourself.

Then the AI has both saved time and potentially helped you develop expertise.

But suppose you simply move to the next AI-generated task.

You may gain efficiency without gaining equivalent skill.

These are very different outcomes.

AI Can Create an “Answer Before Understanding” Habit

One of the biggest changes AI introduces is the ability to obtain an answer before you fully understand the problem.

Imagine a student encountering a difficult programming problem.

Traditionally, the student might:

• Read the problem repeatedly.
• Break it into smaller parts.
• Try an approach.
• Encounter an error.
• Search documentation.
• Try again.

With AI, the student can immediately ask:

“Solve this and explain the code.”

The student receives a working-looking solution.

The difficult part has been removed.

That can be useful when the goal is to complete the task.

But if the goal is to learn programming, the difficult part may have been exactly what created the learning opportunity.

The Productive Struggle Problem

Learning often involves some amount of difficulty.

You try something.

You fail.

You identify why.

You try again.

You gradually build a mental model.

If AI removes every obstacle immediately, the learner may reach the answer faster without building the same understanding.

A 2025 Microsoft Research review of empirical evidence on generative AI in education highlighted this concern. It reported evidence that overdependence and reduced engagement can impair memory formation, and that bypassing the struggle involved in learning can compromise higher-order skills such as analysis, reasoning and creativity. The review also emphasized that AI can be useful when used as a supplement to learning rather than a replacement for it. [oai_citation:1‡Microsoft](https://www.microsoft.com/en-us/research/publication/learning-outcomes-with-genai-in-the-classroom-a-review-of-empirical-evidence/?utm_source=chatgpt.com)

This does not mean difficulty is always good or that students should avoid AI.

It means removing effort and removing learning are not necessarily the same thing.

The Difference Between Productive and Unproductive Offloading

Use of AI	Potential Effect
Summarizing a document after reading it	Can reduce review time while supporting comprehension checks
Generating practice questions	Can create additional opportunities to learn
Explaining a difficult concept in several ways	Can support understanding
Generating a solution before attempting the problem	May reduce opportunities for independent problem solving
Copying an AI answer without verification	Can transfer errors directly into the final work
Using AI to challenge your own reasoning	Can provide an additional perspective
Delegating every unfamiliar task	May reduce opportunities to develop the underlying skill

The same technology can therefore produce very different outcomes depending on when and why the human uses it.

AI Can Become a Cognitive Shortcut

Humans naturally prefer efficient solutions.

If two methods produce the same immediate result, the easier one is attractive.

AI makes this especially powerful because the shortcut can be conversational.

You do not need to learn a complicated search strategy.

You can simply ask.

You do not need to organize your first draft.

You can ask the AI to organize it.

You do not need to brainstorm ten ideas.

You can ask the AI for fifty.

Again, none of this is inherently bad.

The concern is what happens when the shortcut becomes the default for tasks that were previously developing human capability.

Confidence Can Make the Problem Worse

One of the findings from the Microsoft Research study is particularly important: confidence in AI was associated with less reported critical-thinking effort. [oai_citation:2‡Microsoft](https://www.microsoft.com/en-us/research/publication/the-impact-of-generative-ai-on-critical-thinking-self-reported-reductions-in-cognitive-effort-and-confidence-effects-from-a-survey-of-knowledge-workers/?lang=ko-kr&utm_source=chatgpt.com)

Think about the psychological difference between these two situations.

Situation A:

“I don't know this topic, so I need to check everything carefully.”

Situation B:

“The AI probably knows this, so I only need to skim the answer.”

The second attitude can dramatically reduce verification effort.

And the danger is not limited to obviously incorrect answers.

An AI can produce an answer that is:

• Mostly correct but missing an important exception
• Correct in one country but wrong in another
• Accurate in general but inappropriate for the specific situation
• Based on outdated information
• Well written but poorly supported

Confidence in the system can therefore become a reason to verify less precisely when the opposite behavior may be needed.

The “Looks Correct” Problem

AI-generated information often arrives in a polished form.

It may contain:

• Headings
• Examples
• Explanations
• Tables
• Logical transitions
• Confident language

This makes evaluation harder in one specific way: presentation quality can become a substitute for evidence.

A human may think:

“This is clearly written, therefore it probably makes sense.”

But clarity is not verification.

Microsoft's research found that knowledge workers often responded to AI output by verifying information, integrating relevant sections and adapting outputs to the requirements of their task. [oai_citation:3‡Microsoft](https://www.microsoft.com/en-us/research/wp-content/uploads/2025/01/lee_2025_ai_critical_thinking_survey.pdf?utm_source=chatgpt.com)

That human evaluation layer is therefore not a minor editing step. It is part of the reasoning process.

AI Can Change the Definition of “Doing the Work”

This may be one of the most important long-term changes.

Previously, producing an answer might mean:

Research → reason → write → review.

With AI, it can become:

Ask → evaluate → integrate → review.

The human's contribution has moved.

This does not necessarily mean the human contribution has disappeared.

In some tasks, the human may actually need stronger judgment because there is now much more generated material to evaluate.

One person can produce ten possible strategies with AI. The difficult question becomes deciding which strategy is actually worth pursuing.

AI Can Increase the Amount of Information Humans Must Judge

This creates a paradox.

AI reduces the cost of producing information.

But when information becomes cheap to generate, evaluation becomes more important.

Imagine a manager who previously received five business ideas from a team.

With AI, the manager can generate fifty.

The manager now has more ideas—but also more ideas to evaluate.

The bottleneck moves from generation to judgment.

This is one reason researchers increasingly discuss AI as a tool for thought rather than merely a content generator. Microsoft Research's work on “tools for thought” explicitly examines how generative AI can either protect and augment human cognition or create risks for critical thinking, learning and other cognitive processes. [oai_citation:4‡Microsoft](https://www.microsoft.com/en-us/research/publication/understanding-protecting-and-augmenting-human-cognition-with-generative-ai-a-synthesis-of-the-chi-2025-tools-for-thought-workshop/?utm_source=chatgpt.com)

The Best Human Skill May Become Evaluation

If AI can generate plausible answers cheaply, human value increasingly depends on knowing what deserves to survive the generation process.

That requires skills such as:

• Question formulation
• Source evaluation
• Fact checking
• Logical reasoning
• Domain knowledge
• Understanding uncertainty
• Recognizing missing information
• Comparing competing explanations

In other words, AI may not eliminate the need for expertise.

It may change where expertise matters most.

What This Means for Students

Students face a particularly interesting version of this problem.

If AI provides the answer immediately, the student may finish the assignment faster.

But the assignment and the learning objective are not necessarily the same thing.

If the purpose of an exercise is to practice solving equations, writing arguments, programming algorithms, or analyzing evidence, outsourcing the difficult part may defeat the purpose of the exercise.

A more useful approach is to make AI participate in the learning process.

For example, instead of:

“Solve this problem.”

Try:

“Give me one hint at a time. Do not provide the final solution until I have attempted the problem.”

Now the AI is reducing unnecessary frustration without removing the entire reasoning process.

What This Means for Professionals

Professionals face a different challenge.

The goal is often productivity rather than learning.

Delegating routine work to AI can therefore make sense.

But professionals still need to know which parts cannot safely be delegated.

A useful distinction is:

Task Type	Possible AI Role	Human Role
Routine drafting	Generate first version	Review and adapt
Summarization	Create initial summary	Check important facts
Brainstorming	Generate alternatives	Evaluate feasibility
Research	Help identify information	Verify sources and conclusions
High-stakes decisions	Provide analysis or questions	Retain responsibility and independently verify critical information

The more consequential the decision, the less reasonable it is to treat an AI-generated answer as the final authority.

The Better Model: AI as a Cognitive Partner

There is a middle ground between doing everything manually and delegating everything to AI.

Think of AI as a cognitive partner that can:

• Generate alternatives
• Explain difficult concepts
• Challenge assumptions
• Find potential gaps
• Provide counterarguments
• Organize information
• Simulate different perspectives

But the human should still perform the parts that create understanding and judgment.

This approach also aligns with current research thinking around “tools for thought,” where the goal is not simply to automate cognition but to design AI systems that can support and augment it. [oai_citation:5‡Microsoft](https://www.microsoft.com/en-us/research/publication/understanding-protecting-and-augmenting-human-cognition-with-generative-ai-a-synthesis-of-the-chi-2025-tools-for-thought-workshop/?utm_source=chatgpt.com)

A Simple Rule for Using AI Without Outsourcing Your Thinking

Before asking AI to perform a task, ask yourself:

“Is the thing I am about to outsource something I need to be able to understand independently?”

If the answer is yes, consider changing the workflow.

Ask AI for:

• Hints instead of complete solutions
• Questions instead of conclusions
• Criticism instead of automatic agreement
• Alternative approaches instead of one answer
• Explanations instead of copy-ready output

If the answer is no—perhaps the task is repetitive formatting or routine transformation—full automation may make much more sense.

The Bigger Paradox

AI is becoming better at doing things that humans previously had to practice in order to become good at them.

That creates a paradox:

The better AI becomes at performing a skill, the fewer opportunities a person may have to practice that skill manually.

But the opposite is also possible:

The better AI becomes at supporting a skill, the more ambitious and capable the human may become—if the human remains actively involved.

Both outcomes are possible.

The technology alone does not determine which one happens.

The Question We Should Really Be Asking

The debate around AI often asks:

“Will AI replace this job?”

There is another question that may be just as important:

“Which parts of this job will humans stop practicing because AI can do them?”

That question moves the discussion from replacement to capability.

A person may still technically be able to perform a task without AI, but if they have not practiced it for years, their real-world ability may be different from what their job title suggests.

And this leads to the next layer of the story.

If AI systems increasingly decide what information to show us, what options to generate, what mistakes to highlight, and what actions to recommend, AI may begin shaping not only individual skills but also the way people make decisions.

That is where the feedback loop becomes much more powerful—and much more difficult to notice.

Sources and Further Reading

• Microsoft Research — The Impact of Generative AI on Critical Thinking, CHI 2025. [oai_citation:6‡Microsoft](https://www.microsoft.com/en-us/research/publication/the-impact-of-generative-ai-on-critical-thinking-self-reported-reductions-in-cognitive-effort-and-confidence-effects-from-a-survey-of-knowledge-workers/?lang=ko-kr&utm_source=chatgpt.com)
• Microsoft Research — Learning Outcomes with GenAI in the Classroom: A Review of Empirical Evidence, 2025. [oai_citation:7‡Microsoft](https://www.microsoft.com/en-us/research/publication/learning-outcomes-with-genai-in-the-classroom-a-review-of-empirical-evidence/?utm_source=chatgpt.com)
• Microsoft Research — Understanding, Protecting, and Augmenting Human Cognition with Generative AI. [oai_citation:8‡Microsoft](https://www.microsoft.com/en-us/research/publication/understanding-protecting-and-augmenting-human-cognition-with-generative-ai-a-synthesis-of-the-chi-2025-tools-for-thought-workshop/?utm_source=chatgpt.com)
• Microsoft Research — Tools for Thought: Research and Design for Understanding, Protecting, and Augmenting Human Cognition with Generative AI, CHI 2025. [oai_citation:9‡Microsoft](https://www.microsoft.com/en-us/research/publication/tools-for-thought-research-and-design-for-understanding-protecting-and-augmenting-human-cognition-with-generative-ai/?lang=zh-cn&utm_source=chatgpt.com)
• Scientific Reports — Research on generative AI, cognitive offloading and academic achievement. [oai_citation:10‡nature.com](https://www.nature.com/articles/s41598-025-01676-x?utm_source=chatgpt.com)

AI Is Training People While People Think They Are Training AI: When AI Starts Shaping Human Decisions

In the previous parts of this series, we looked at how repeated interaction with AI can change the way people think, write, learn and solve problems.

Part 3 focused on cognitive offloading: the tendency to let AI perform parts of the mental work that people might otherwise perform themselves.

But there is a deeper stage in the relationship.

AI does not have to make the final decision to influence the decision.

It only needs to become part of the process through which the decision is made.

A recommendation can change what options a person considers. A generated explanation can change what they believe is important. A suggested action can change what they do next.

This means the human-AI relationship is not simply:

Human asks → AI answers.

It can become:

Human asks → AI influences the information available → human evaluates it → human changes the decision → that decision changes future interaction with AI.

AI Can Influence a Decision Without Making It

Consider a simple example.

You are deciding between three products.

You ask an AI assistant to compare them.

The AI highlights Product A as having better value, Product B as having better performance, and Product C as being more convenient.

You ultimately choose Product B.

Technically, you made the decision.

But the AI still influenced the decision because it helped determine:

• Which characteristics you considered
• How the products were described
• Which differences received attention
• Which alternatives appeared important
• What information you saw before deciding

This distinction matters because influence does not require control.

The Recommendation Changes the Decision Environment

Before AI entered the process, you might have considered price, durability, brand reputation and warranty.

After asking AI, you might instead focus on performance, specifications and user experience.

The products did not necessarily change.

The decision environment changed.

This is one reason AI-assisted decision-making deserves attention beyond simple accuracy tests.

A system can produce a useful recommendation while still changing how humans frame the problem.

Research published in Nature Reviews Psychology in 2025 describes AI and humans as having different strengths in decision-making: AI can process large datasets, identify statistical patterns and optimize predefined objectives, while humans remain important for uncertainty, novelty and interpersonal or ethical challenges. The authors argue that effective human-AI systems should be designed around this complementarity rather than assuming one side should simply replace the other. [oai_citation:0‡Nature](https://www.nature.com/articles/s44159-025-00499-x?utm_source=chatgpt.com)

The Most Important Part May Be What AI Leaves Out

People often evaluate AI recommendations by asking whether what the system said was correct.

There is another question:

What did the system not mention?

Suppose an AI recommends a financial product based on fees, historical performance and convenience.

If it does not discuss liquidity restrictions, tax consequences or an important eligibility condition, the answer may look useful while still producing an incomplete decision.

The same problem appears in education, hiring, healthcare, shopping, software development and many other areas.

An AI system does not need to provide false information to influence a person incorrectly.

Selective information can be influential even when every individual statement is technically accurate.

Humans Do Not Always Notice How Much AI Influenced Them

This is where the human-AI feedback loop becomes particularly interesting.

A 2025 Nature Human Behaviour study involving 1,401 participants found that interactions with a biased AI system could alter subsequent human perceptual, emotional and social judgments. The researchers found evidence that people learned from the AI's signal rather than merely copying its immediate judgment, and participants underestimated the influence the AI had on their own judgments. [oai_citation:1‡Nature](https://www.nature.com/articles/s41562-024-02077-2?utm_source=chatgpt.com)

That finding changes the way the problem should be understood.

The concern is not simply:

“What if people blindly follow AI?”

It is also:

“What if people believe they are making independent judgments while their judgment has gradually been influenced by repeated AI interaction?”

Influence Can Happen Through Repetition

One AI recommendation might have very little effect.

Hundreds of interactions can be different.

Imagine someone uses AI every day to:

• Summarize news
• Compare products
• Evaluate arguments
• Write emails
• Plan projects
• Interpret technical information
• Choose between alternatives

Over time, the person is repeatedly exposed to the system's way of organizing information.

They may begin to adopt certain patterns without consciously deciding to do so.

This is not proof that every AI user will change their beliefs or behavior in a particular direction.

It does show why repeated interaction deserves more attention than isolated chatbot conversations.

The AI Can Influence What Feels Like a Reasonable Option

Imagine asking an AI:

“How should I solve this problem?”

The AI gives you three approaches.

You choose one.

But perhaps there were seven reasonable approaches, and the AI showed only three.

Your decision was still yours.

However, the AI affected the choice set you were considering.

This is a subtle form of influence because humans cannot evaluate alternatives they never encounter.

The same principle exists outside AI. Search engines, recommendation systems, news feeds and social platforms have long influenced which information people encounter.

Generative AI adds another layer because the system can produce a personalized explanation or recommendation in response to an individual's exact question.

AI Can Also Influence How People Judge Other Humans

The effect does not stop at decisions about objects or tasks.

AI can become involved in judgments about people.

For example:

• Evaluating a job candidate
• Assessing a written application
• Summarizing an employee's performance
• Prioritizing customer requests
• Assessing a student's work
• Helping a professional interpret a case

In these situations, an AI recommendation can become an additional signal that a human decision-maker considers.

NIST has emphasized that AI bias is not purely a technical problem. Human and systemic factors can also introduce bias through the way people design, deploy, interpret and use AI systems. [oai_citation:2‡NIST](https://www.nist.gov/artificial-intelligence/ai-fundamental-research-managing-ai-bias?utm_source=chatgpt.com)

This is important because even a technically sophisticated model operates inside a human decision process.

AI Assistance Can Improve Decisions Too

It would be misleading to describe AI influence as automatically negative.

AI assistance can improve human performance in some situations.

For example, a 2025 randomized study involving 50 US-licensed physicians examined chest-pain triage decisions before and after participants received GPT-4-generated recommendations. The researchers reported improvements in guideline-based accuracy after AI assistance, with similar-sized improvements across the patient groups studied and no observed increase in the demographic bias they measured. [oai_citation:3‡Nature](https://www.nature.com/articles/s43856-025-00781-2?utm_source=chatgpt.com)

The lesson is not that AI should make medical decisions.

The more useful lesson is that AI influence can sometimes improve human decisions when the system, task and oversight are appropriately designed.

That is why the discussion should not be reduced to “AI influence is bad.”

The important question is whether the influence is useful, reliable, transparent and appropriately controlled for the particular task.

The Problem With Treating AI as an Authority

One dangerous transition occurs when people stop treating AI as a source of input and start treating it as an authority.

These are not the same.

Input: “Here is another perspective. I will evaluate it.”

Authority: “The AI said it, so it is probably correct.”

The second approach removes an important part of human judgment.

Research reviewed in Nature Reviews Psychology notes that large language models can produce cognitive-bias-like outputs and hallucinations, and that their reasoning should not simply be treated as equivalent to human reasoning. The review argues for responsible use and mitigation of reliability and bias problems when LLMs are used for decision support. [oai_citation:4‡Nature](https://www.nature.com/articles/s44159-025-00506-1?utm_source=chatgpt.com)

Why Polished Language Can Increase Trust

AI-generated answers are often presented with remarkable fluency.

They can sound organized, calm and confident even when uncertainty is substantial.

This creates a potential mismatch:

Confidence in presentation is not the same as confidence in evidence.

A system can say:

“Based on the available information, the most likely explanation is…”

That sounds cautious.

But the underlying information may still be incomplete.

Humans therefore need a separate mechanism for evaluating evidence instead of using writing quality as a shortcut for truth.

People Can Also Become Biased Against AI

The relationship is not always one-directional.

Some people may trust AI too much.

Others may reject useful AI advice simply because they know it came from a machine.

A 2025 Scientific Reports study involving five preregistered experiments and 1,722 participants found that people showed an aversion to AI-generated advice in several conditions. Interestingly, the researchers also found that the same AI-generated advice could receive different evaluations depending on whether participants knew it came from ChatGPT. [oai_citation:5‡Nature](https://www.nature.com/articles/s41598-025-86623-6?utm_source=chatgpt.com)

This creates an important point:

Human judgment can be distorted in both directions.

People can over-trust AI.

People can under-trust AI.

Neither reaction is a reliable substitute for evaluating the actual evidence and suitability of the recommendation.

The Real Goal Is Not Maximum AI Trust

A mature human-AI relationship should not aim to make people trust AI as much as possible.

It should aim for appropriate reliance.

That means:

• Use AI when it provides useful information or analysis.
• Reduce reliance when the system is uncertain or the task is outside its strengths.
• Verify important claims independently.
• Keep humans responsible for decisions that require human judgment.
• Use additional expertise when the consequences of an error are significant.

NIST's AI Risk Management Framework specifically emphasizes defining human roles and responsibilities in human-AI systems and recognizing that human-AI interaction can either amplify bias or create useful complementarity depending on the circumstances. [oai_citation:6‡AIRC](https://airc.nist.gov/airmf-resources/airmf/appendices/app-c-ai-risk-management-and-human-ai-interaction/?utm_source=chatgpt.com)

Decision-Making Can Become a Feedback Loop

Now combine everything discussed in the first three parts.

A person asks AI for help.

The AI provides an answer.

The person changes their decision.

That decision produces a result.

The person learns from the result.

They return to AI with new questions.

The new interaction is shaped by what happened previously.

The loop continues.

The feedback loop:

Human behavior → AI recommendation → human decision → real-world outcome → human learning → new AI interaction → new recommendation.

This means AI is no longer simply answering questions.

It can become part of the environment in which people learn what works.

That Makes AI Influence Different From a Simple Tool

A calculator does not normally try to persuade you.

A hammer does not recommend which building project you should pursue.

A spreadsheet does not normally explain why one life decision is better than another.

Generative AI is different because it communicates through language.

It can explain.

Compare.

Recommend.

Question.

Reframe.

And adapt its response to the conversation.

That makes it more useful—but it also makes the relationship more psychologically complex.

What Happens When AI Becomes the First Opinion?

Imagine that before making any important decision, a person automatically asks AI first.

Eventually, AI becomes the starting point for their thinking.

That could be helpful.

But it can also create a subtle dependency on an external first opinion.

Instead of beginning with:

“What do I think?”

The process becomes:

“What does AI think?”

Only afterward does the person form an opinion.

That difference may matter because the first explanation can frame everything that comes afterward.

A Better Sequence for Important Decisions

For decisions where independent reasoning matters, a stronger workflow can be:

1. Form your initial view. Write down what you currently believe and why.
2. Ask AI for analysis. Request alternatives, counterarguments and missing considerations.
3. Check the evidence. Verify important claims against reliable sources.
4. Compare. Identify where your original view and the AI's analysis differ.
5. Reconsider. Change your position if the evidence supports doing so.
6. Decide. Make the final judgment yourself.

This preserves one of the most valuable parts of human reasoning: the ability to notice when new information should change your mind.

AI Should Sometimes Be Asked to Disagree

One practical way to reduce passive agreement is to deliberately ask AI to challenge your reasoning.

Instead of:

“Explain why my plan is good.”

Try:

“What are the strongest arguments against my plan? Identify assumptions that could be wrong and information I may be overlooking.”

This does not guarantee an unbiased answer.

But it changes the role of the AI from an automatic validator into a source of competing perspectives.

The Human Still Needs a Veto

For important decisions, the final human role should not simply be pressing “accept.”

The human should be capable of saying:

“I understand the recommendation, but I do not accept it.”

That ability is especially important when:

• The evidence is incomplete.
• The decision affects other people.
• The consequences are difficult to reverse.
• The AI's recommendation conflicts with verified evidence.
• The task involves ethical or personal considerations.
• The AI cannot explain important assumptions.

The objective is not to remove AI from decision-making.

It is to prevent the human from becoming a passive approval mechanism.

The Bigger Question: Who Is Training Whom?

At the beginning of this series, the relationship looked straightforward.

Humans train AI.

But now the picture is more complicated.

Humans provide data, feedback and preferences.

AI provides recommendations, explanations and patterns.

Humans adapt their behavior in response.

That behavior becomes part of future interactions.

The result is a continuous feedback system in which both sides can affect the other.

Humans are training AI, but AI can also be training human behavior.

And neither process necessarily requires anyone to consciously decide that it is happening.

What Comes Next

The most interesting part of this relationship may not be individual users at all.

As AI systems become embedded into search, education, workplaces, software, financial services, healthcare and everyday decision-making, the feedback loop can operate at a much larger scale.

AI systems can influence millions of interactions.

Those interactions can influence human behavior.

Human behavior can then influence the data, preferences and environments surrounding future AI systems.

That raises a much larger question:

What happens when the feedback loop is no longer between one person and one AI system, but between entire populations and the AI systems they use every day?

That is where the story moves from personal productivity to society-wide adaptation.

Sources and Further Reading

• Nature Human Behaviour — “How human–AI feedback loops alter human perceptual, emotional and social judgements.” [oai_citation:7‡Nature](https://www.nature.com/articles/s41562-024-02077-2?utm_source=chatgpt.com)
• Nature Reviews Psychology — “Dual-process theory and decision-making in large language models.” [oai_citation:8‡Nature](https://www.nature.com/articles/s44159-025-00506-1?utm_source=chatgpt.com)
• Nature Reviews Psychology — “A cognitive approach to human–AI complementarity in dynamic decision-making.” [oai_citation:9‡Nature](https://www.nature.com/articles/s44159-025-00499-x?utm_source=chatgpt.com)
• Scientific Reports — “Me vs. the machine? Subjective evaluations of human- and AI-generated advice.” [oai_citation:10‡Nature](https://www.nature.com/articles/s41598-025-86623-6?utm_source=chatgpt.com)
• Communications Medicine — “Physician clinical decision modification and bias assessment in a randomized controlled trial of AI assistance.” [oai_citation:11‡Nature](https://www.nature.com/articles/s43856-025-00781-2?utm_source=chatgpt.com)
• NIST — AI Risk Management Framework resources on human-AI interaction and human roles in AI-assisted decisions. [oai_citation:12‡AIRC](https://airc.nist.gov/airmf-resources/airmf/appendices/app-c-ai-risk-management-and-human-ai-interaction/?utm_source=chatgpt.com)

AI Is Training People While People Think They Are Training AI: When Humans and AI Start Training Each Other

So far, this series has looked at a relationship that is easy to miss.

Humans train AI systems through data, feedback, evaluations and interaction. At the same time, AI systems can influence how humans think, learn, communicate, evaluate information and make decisions.

Part 4 took this further: AI does not need to make a decision itself to influence the decision. It can change the information available, the alternatives people consider and the way a problem is framed.

Now we can connect the entire process.

The most important development may not be that humans are using AI more. It is that humans and AI are increasingly becoming part of the same feedback system.

The Feedback Loop Gets Bigger

At an individual level, the loop can look simple:

Human behavior → AI output → human response → new human behavior → new AI interaction

But imagine millions of people doing this simultaneously.

People use AI to write, search, study, shop, code, plan, communicate and make decisions.

Those interactions generate new human behavior.

That behavior exists inside the wider information environment in which future AI systems are developed, evaluated, deployed and used.

The result is not one simple feedback loop.

It is a network of interacting loops.

AI Is Becoming Part of the Social Environment

AI is no longer limited to a separate chatbot window.

It can appear in:

• Search and information systems
• Social media
• Education platforms
• Workplace software
• Customer service
• Financial products
• Healthcare systems
• Programming tools
• Recommendation systems
• Creative applications

This matters because people do not interact with these systems in isolation.

They interact with other people after using them.

A student uses AI and then teaches the concept to another student.

A developer uses AI to write code and then shares the resulting software.

A company uses AI to generate content and publishes it online.

A manager uses AI-assisted analysis before making a business decision.

An AI-generated image appears on social media and is viewed by people who may never have used the AI system themselves.

The influence can therefore travel beyond the original user.

Research in Nature Human Behaviour has specifically identified this possibility: people can be affected not only by directly interacting with AI, but also by observing AI-generated outputs in environments such as social media and other digital platforms. [oai_citation:0‡Nature](https://www.nature.com/articles/s41562-024-02077-2?utm_source=chatgpt.com)

One Person's AI Output Can Become Another Person's Input

This creates an important distinction between direct and indirect AI influence.

Suppose Person A asks AI to generate an explanation.

Person A posts that explanation online.

Person B reads it.

Person B incorporates part of it into their own work.

Person C later encounters Person B's version.

At this point, the original AI interaction has travelled through several humans.

The final reader may have no idea that AI was involved at the beginning.

This does not mean every piece of AI-generated information becomes harmful or unreliable.

It means that the boundary between human-generated information and AI-mediated information can become increasingly difficult to see.

The Internet Could Become More AI-Mediated

For decades, the internet was primarily a place where humans produced information for other humans.

That model is changing.

Humans increasingly use AI to generate:

• Articles
• Images
• Videos
• Software
• Product descriptions
• Marketing material
• Summaries
• Comments
• Educational explanations

As AI-generated material becomes more common, humans increasingly consume information that has already passed through an AI system.

This creates a possible second-order effect.

AI may influence people not only when they deliberately ask an AI for help, but also through the information environment surrounding them.

The 2025 Nature Human Behaviour research on human-AI feedback loops provides experimental evidence that repeated exposure to biased AI outputs can alter subsequent human judgments. The study involved 1,401 participants and tested several types of judgment rather than a single narrow task. [oai_citation:1‡Nature](https://www.nature.com/articles/s41562-024-02077-2?utm_source=chatgpt.com)

The Culture of AI Can Also Be Reflected Back to Humans

There is another direction to the loop.

AI systems are trained using human-produced information, and human-produced information contains cultural patterns.

A 2025 study in Nature Human Behaviour found that generative AI models exhibited cultural tendencies when responding in different languages, examining constructs including social orientation and cognitive style. [oai_citation:2‡Nature](https://www.nature.com/articles/s41562-025-02242-1?utm_source=chatgpt.com)

This is important because AI is not necessarily producing information from a culturally neutral position.

Patterns from human society can enter AI systems.

AI systems can then reproduce or transform those patterns.

People interact with the resulting outputs.

Those interactions can influence how people communicate and what they expect from future systems.

The relationship therefore becomes circular:

Human culture → AI systems → AI-mediated culture → human behavior → new data and interactions → future AI systems

This Does Not Mean AI Controls Culture

That distinction is important.

There is currently no basis for saying that AI systems independently determine the direction of human culture.

Culture is shaped by many forces: families, communities, institutions, media, economics, education, technology, politics, geography and individual choices.

AI is becoming one additional influence within that larger system.

The important question is therefore not whether AI will completely control society.

It is:

How much influence will AI have within systems that already shape human behavior?

Feedback Loops Can Amplify Small Problems

One of the most interesting findings from the human-AI feedback-loop research is that a small bias does not necessarily remain small.

The researchers found that AI systems could amplify small biases present in human-generated data, after which repeated interaction with the biased system increased human bias further. [oai_citation:3‡Nature](https://www.nature.com/articles/s41562-024-02077-2?utm_source=chatgpt.com)

That creates a pattern like this:

1. A small human bias enters the system.
2. The AI learns a pattern containing that bias.
3. The AI produces outputs reflecting or amplifying the pattern.
4. Humans interact with those outputs.
5. Some humans adjust their judgments.
6. The changed behavior becomes part of later interactions.
7. The process repeats.

This is not a prediction that every AI system will create such a loop.

It is an example of why feedback dynamics matter when AI becomes embedded in repeated human decisions.

The Same Loop Can Also Improve People

Feedback loops are not inherently negative.

If an AI system provides accurate information, useful explanations and productive challenges, repeated interaction could improve human performance.

For example, a student might use AI to:

• Receive immediate feedback on practice work
• Generate additional exercises
• Ask for alternative explanations
• Identify gaps in understanding
• Test their reasoning with counterexamples

Over time, that interaction could support learning rather than replace it.

The same principle applies to professionals.

An engineer might use AI to generate possible solutions but independently test them.

A researcher might use AI to identify possible explanations but verify the underlying evidence.

A writer might use AI to challenge an argument rather than simply generate the final article.

The feedback loop then becomes potentially constructive:

Human attempt → AI feedback → human evaluation → improved human performance → better future interaction

The important variable is not simply how much AI is used.

It is how the interaction is structured.

The Difference Between Dependence and Collaboration

Two people can use exactly the same AI model in completely different ways.

User A:

“Give me the answer.”

Copies the response.

Moves on.

User B:

“Here is my reasoning. Find the weakest assumption, give me two counterarguments and identify what evidence would change my conclusion.”

Reviews the response.

Checks important evidence.

Updates their reasoning.

The amount of AI used may be similar.

The cognitive relationship is completely different.

Human Agency Becomes More Important, Not Less

As AI becomes more capable, it may seem logical to delegate more decisions to it.

But capability does not automatically determine responsibility.

A system can be capable of generating a recommendation without being the appropriate entity to make the final decision.

This is particularly important in situations involving:

• Health
• Finance
• Employment
• Education
• Legal matters
• Safety
• Personal relationships
• High-impact organizational decisions

NIST's AI Risk Management Framework explicitly emphasizes the need to define human roles and responsibilities in human-AI systems. It also notes that human-AI configurations can range from fully manual to highly autonomous, and that the appropriate arrangement depends on the context. [oai_citation:4‡AIRC](https://airc.nist.gov/airmf-resources/airmf/appendices/app-c-ai-risk-management-and-human-ai-interaction/?utm_source=chatgpt.com)

The point is not that every AI decision requires a human manually checking every detail.

The point is that human responsibility should be designed rather than assumed.

AI Governance Is Also About Human Behavior

AI governance is sometimes discussed as if the problem were entirely technical.

Build a safer model.

Test the model.

Measure bias.

Improve security.

These are important.

But human-AI interaction adds another question:

What happens to people when they repeatedly use the system?

NIST's AI Risk Management Framework treats AI risk as a broader socio-technical problem and emphasizes continuous risk management across the AI lifecycle. Its Generative AI Profile provides additional guidance for risks associated specifically with generative AI. [oai_citation:5‡NIST](https://www.nist.gov/itl/ai-risk-management-framework?utm_source=chatgpt.com)

This matters because a model can perform well in an isolated benchmark while still producing unexpected effects when millions of people repeatedly interact with it.

The Unit of Analysis Is Changing

Traditional AI evaluation often asks:

“How accurate is the model?”

Human-AI evaluation needs additional questions:

• How do people use the model?
• How does usage change human behavior?
• Do users become better at the underlying task?
• Do they become dependent on the system?
• Can they recognize when the system is wrong?
• Does the system amplify existing biases?
• What happens after repeated exposure?
• How does the system affect people who never directly use it?

These questions move evaluation from the model alone to the human-AI system as a whole.

The Future May Be About Designing Better Feedback Loops

If AI and humans are going to influence each other continuously, then the goal should not necessarily be to eliminate the feedback loop.

That would be unrealistic.

The more practical goal is to design better loops.

A healthy loop could encourage:

• Verification rather than blind acceptance
• Learning rather than passive copying
• Independent thinking before important decisions
• Exposure to competing perspectives
• Clear uncertainty when evidence is weak
• Human responsibility for consequential decisions
• Continuous evaluation of real-world effects

A poorly designed loop could encourage:

• Overconfidence
• Dependence
• Bias amplification
• Reduced skill practice
• Automatic agreement
• Information homogenization
• Unclear responsibility

The difference is not simply the intelligence of the AI model.

It is the architecture of the relationship between the model and the people using it.

What Individuals Can Do

People do not need to stop using AI to protect their ability to think.

A few habits can preserve an active role in the process.

1. Think Before Asking

For difficult problems, spend a few minutes forming your own initial view before asking AI.

This gives you something to compare against rather than making the AI's first answer your starting point.

2. Ask for Alternatives

Do not always ask for the single “best” answer.

Ask for competing explanations, assumptions and trade-offs.

3. Verify Important Claims

The higher the consequences, the stronger the verification should be.

For important factual claims, use reliable primary or authoritative sources instead of treating an AI response as the evidence itself.

4. Use AI to Challenge You

Ask the system to identify weaknesses in your reasoning rather than simply confirm your conclusion.

5. Keep Practicing Core Skills

If a skill matters to you, periodically perform parts of the task without AI.

This helps you determine whether AI is extending your ability or quietly replacing your practice.

6. Notice When AI Becomes Your Default First Opinion

If every question immediately goes to AI, occasionally pause and ask yourself what you think before seeing the machine's answer.

What Organizations Can Do

Organizations face a larger responsibility because AI-assisted decisions can affect many people simultaneously.

Useful safeguards can include:

• Clearly defined human responsibilities
• Documentation of where AI is used
• Testing for bias and reliability
• Monitoring after deployment
• Escalation procedures for uncertain cases
• Training employees to evaluate AI outputs
• Periodic review of whether AI assistance is actually improving outcomes

NIST's AI RMF organizes risk management around four broad functions—Govern, Map, Measure and Manage—and describes risk management as a continuous activity rather than a one-time model check. [oai_citation:6‡AIRC](https://airc.nist.gov/airmf-resources/airmf/5-sec-core/?utm_source=chatgpt.com)

That approach is particularly relevant to human-AI feedback loops because the effects of a system may become visible only after deployment and repeated use.

The Most Important Skill May Be Knowing When Not to Delegate

AI literacy is often described as knowing how to write good prompts.

That is useful, but incomplete.

A deeper form of AI literacy is knowing:

• What to delegate
• What to verify
• What to understand yourself
• When AI is likely to be useful
• When AI may be unreliable
• When a second source is necessary
• When the decision should remain firmly human-led

The most capable AI user may therefore not be the person who asks AI to do everything.

It may be the person who knows which cognitive work should remain their own.

So, Who Is Training Whom?

At the beginning of the series, the answer seemed obvious:

Humans train AI.

After looking at the feedback loop, the answer becomes more complicated.

Humans train AI systems through data, feedback, evaluation and usage.

AI systems influence humans through recommendations, explanations, examples, rankings and repeated interaction.

Humans then change their behavior.

That changed behavior affects future interactions with AI.

At scale, those interactions become part of a larger social and technological environment.

So the more accurate picture is:

Humans train AI.
AI influences humans.
Humans adapt to AI.
AI systems are then used inside the changed human environment.

This is not a simple teacher-and-student relationship anymore.

It is closer to a continuous feedback system.

The Future Question Is Not “Human or AI?”

The most useful question may not be whether humans will remain better than AI or whether AI will become better than humans.

Different systems will have different strengths.

Humans will remain important for many forms of judgment, context, responsibility and social understanding.

AI will continue to become more capable at processing information, generating alternatives and performing increasingly complex tasks.

The more important question is how the two will be combined.

Will AI help humans become more capable?

Will humans use AI mainly to avoid difficult thinking?

Will organizations design systems that preserve meaningful human oversight?

Will people recognize when AI is changing their own behavior?

These are not questions that model intelligence alone can answer.

The Final Paradox

AI is often described as a tool that humans control.

That description is incomplete when the tool can communicate, recommend, adapt and participate in repeated interactions.

A hammer does not learn from the way you use it.

A spreadsheet does not normally persuade you.

A calculator does not change your future behavior based on your conversation.

Modern AI systems can participate in much richer feedback loops.

That does not make AI inherently good or bad.

It makes the relationship more important to understand.

The future of AI may therefore be shaped not only by how intelligent machines become, but by how intelligently humans choose to interact with them.

Humans may continue training AI.

AI may continue training human behavior.

And the most important part of that relationship may be whether humans remain capable of noticing the feedback loop while they are inside it.

Sources and Further Reading

• Nature Human Behaviour — Glickman & Sharot, “How human–AI feedback loops alter human perceptual, emotional and social judgements” (2025). The study used 1,401 participants and found evidence that repeated interaction with biased AI could amplify human judgment biases. [oai_citation:7‡Nature](https://www.nature.com/articles/s41562-024-02077-2?utm_source=chatgpt.com)
• Nature Human Behaviour — “A new sociology of humans and machines” (2024), discussing networks in which humans and intelligent machines interact as complex social systems. [oai_citation:8‡Nature](https://www.nature.com/articles/s41562-024-02001-8?utm_source=chatgpt.com)
• Nature Human Behaviour — “Cultural tendencies in generative AI” (2025), examining cultural tendencies in generative AI responses across languages. [oai_citation:9‡Nature](https://www.nature.com/articles/s41562-025-02242-1?utm_source=chatgpt.com)
• Nature Machine Intelligence — “The future of open human feedback” (2025), examining human feedback as a central mechanism for improving and steering AI systems and discussing the development of more open feedback ecosystems.
• National Institute of Standards and Technology (NIST) — AI Risk Management Framework and its guidance on human-AI interaction, human responsibilities and continuous AI risk management. [oai_citation:10‡NIST](https://www.nist.gov/itl/ai-risk-management-framework?utm_source=chatgpt.com)
• NIST — Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, updated April 2026. [oai_citation:11‡NIST](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence?utm_source=chatgpt.com)

Final Disclaimer

This article is for educational and informational purposes only. It discusses research and emerging ideas about artificial intelligence, human behavior and human-AI interaction. Research in this area is still developing, and findings from individual studies should not be interpreted as proof that every AI system or every user will experience the same effects. The examples and practical suggestions in this article are intended to support informed use of AI and should not be treated as professional, legal, medical, psychological or technical advice.