AI Tools + Econ Methods + WLSN Students = High-Quality Research

Good research produces the same answer when you ask the same question twice. That sounds obvious, but most work that involves human judgment fails this test quietly. Two people read the same document and code it differently. The same person codes it differently on a Tuesday than on a Friday. Nobody notices because nobody checks.

AI forces you to confront consistency directly. When you run a protocol, you get an output. When you run it again, you get the same output. When a second platform gives a different answer, you can’t shrug it off — you have to figure out why. The disagreement is visible, documented, and demands resolution.

Students trained in this environment develop an intolerance for sloppiness. They learn that “it seems right” is not a standard. They learn to build processes where consistency isn’t hoped for but engineered. That discipline transfers to any field, any job, any graduate program. A person who instinctively asks “would this produce the same result if I ran it again?” is a person whose work can be trusted.

Replicable means someone else can do what you did and verify that it works. Not someone on your team. Not someone who watched you do it. A stranger at another university, reading your documentation, following your steps, getting the same result.

This is a higher standard than most people ever encounter before graduate school — and many graduate students never encounter it either. But AI-driven research makes it natural. Every prompt is text. Every model version has a name. Every input and output can be logged. The infrastructure for replicability is built into the workflow if you’re disciplined enough to use it.

Students who learn to work this way internalize something important: your work should be able to stand without you in the room to explain it. Your documentation should be complete enough that it speaks for itself. That’s not just a research skill. It’s a professional skill. The person who writes code others can read, builds processes others can follow, and documents decisions others can understand — that person is valuable everywhere.

Elegant means simple enough to explain and powerful enough to matter. It means you solved the problem with the minimum necessary complexity. Nothing extra. Nothing decorative. Just the clean line between question and answer.

Elegance is hard because the temptation is always to add more. More variables, more steps, more qualifications. The undisciplined researcher builds a complicated system and calls it thorough. The disciplined one strips away everything that doesn’t contribute and calls what remains sufficient.

AI tempts you toward complexity — you can process more data, add more models, run more variations. Elegance is knowing when to stop. It’s the prompt that works in four sentences instead of forty. It’s the protocol that a new student can learn in a week instead of a semester. It’s the research design that someone outside your field can understand in one paragraph.

Students rarely encounter elegance as an explicit value. They’re taught to be thorough, comprehensive, detailed. Nobody tells them that the hardest and most respected thing is to make something simple. The lab does.

This is not about grades. It’s about the relationship you have with your own work.

A student who holds a high standard finishes something, looks at it, and asks whether it’s actually good — not whether it’s good enough to submit. The difference is internal. Nobody is checking over your shoulder at midnight. The question is whether you check yourself.

AI makes this both easier and harder. Easier because you can produce more output faster. Harder because quantity creates the illusion of quality. The student who generates ten pages of AI output and submits it without scrutiny has produced nothing of value. The student who generates the same ten pages, reads every line, catches the error on page seven, and fixes it — that student has done real work. The output might look identical. The difference is entirely in the person.

The lab asks you to hold a standard that most people won’t hold until someone forces them to — and then to hold it when nobody is forcing you. That’s a habit. Once you have it, you don’t lose it.

Curiosity is the willingness to follow a question past the point where it's required. You were asked to classify sentences. You did it. But then you noticed something odd — a pattern you weren't looking for, a result that doesn't fit your expectation, an edge case that the protocol doesn't address. The incurious student ignores it and moves on. The curious student stops and investigates.

Most of what matters in research — and in careers — comes from that pause. The assigned task gets you to the table. Curiosity is what you do once you're there.

AI amplifies curiosity in a specific way. The cost of testing an idea has collapsed. If you wonder whether something is true, you can often check in five minutes instead of five weeks. That means a curious person can follow more threads, test more hunches, and learn faster than at any previous point in history. But only if they have the instinct to ask in the first place. The technology doesn't generate curiosity. It rewards it.

Some things don't work the first time. Or the fifth time. Or the tenth time.

Perseverance is not stubbornness. Stubbornness is doing the same thing repeatedly and refusing to change. Perseverance is doing something, watching it fail, figuring out what went wrong, adjusting, and trying again. It's iteration with intelligence. It requires the emotional ability to absorb failure without being defeated by it and the intellectual ability to learn from failure without ignoring it.

AI work is relentlessly iterative. A prompt fails. You rewrite it. The rewrite handles most cases but breaks on a new edge case. You fix the edge case and introduce a different problem. This cycle is not a bug in the process — it is the process. The student who expects things to work immediately will be miserable. The student who finds satisfaction in the gradual narrowing of errors — who sees each failed attempt as the problem getting smaller — will thrive.

Perseverance is also the trait that separates people who have skills from people who have accomplishments. Plenty of people learn to use AI. Far fewer stick with a project long enough to produce something that matters. The lab gives you a place to practice finishing things, which is a rarer and more valuable ability than most people realize.

A tool does what you tell it to do. You learn the commands, you follow the steps, you get an output. This is where everyone begins and where most people stay.

Using AI as a tool means you can write a prompt that produces a useful result. You can format inputs, interpret outputs, and troubleshoot when something goes wrong. You understand API calls, token limits, model differences. You know which platform handles what well. You can sit down with a task and get it done.

This is real and it matters. Most people right now cannot do this competently. They copy and paste into a chatbox and accept whatever comes back. A student who can design a structured prompt, evaluate the quality of the response, and iterate toward a better result already has an advantage over the majority of professionals in any industry.

But a tool only extends what you were already going to do. It makes existing work faster. It doesn't change what work you attempt. If you stop here, you are a more efficient version of who you were before. You haven't changed the category of what's possible.

Leverage changes the scale of what one person can accomplish. It's not about doing the same thing faster — it's about doing things that were previously impossible for someone in your position.

An undergraduate at a teaching university, working ten hours a week, has no business producing a dataset that covers fifteen years of corporate filings for a hundred firms. That's a multi-year project for a team at a research university. But with the right AI protocol, the right mentorship, and enough discipline, that undergraduate can do it. Not because they're exceptional. Because the leverage changed.

This is what most people miss about AI. They think about productivity — saving twenty minutes on an email, generating a first draft faster. That's Level 1 thinking applied to Level 2 technology. Leverage means asking a different question entirely: what would I attempt if the constraint I've always accepted no longer existed?

A student who understands leverage stops measuring themselves against what's expected of someone at their level. They start asking what's possible. The answer, right now, is more than at any point in history — but only for people who recognize that the old constraints have moved. Most people keep operating within boundaries that no longer exist. The student who sees that the wall has come down, and walks through it, is the one who produces something that matters.

A mirror shows you what's actually there, not what you think is there.

When you give AI a vague prompt, you get a vague answer. When you give it a confused question, the confusion comes back amplified. The output is a reflection of the input — and the input is a reflection of how clearly you think. Most people, when they get a bad result from AI, blame the technology. The more honest response is to look at what you asked and ask whether the question was any good.

AI is the first tool in history that gives you immediate, unsparing feedback on the quality of your thinking. A spreadsheet doesn't care whether your categories make sense. A search engine doesn't tell you your question is poorly framed. But an AI that produces garbage output when given garbage input is telling you something about your input — if you're willing to listen.

Students who reach Level 3 stop seeing AI as something external. They start seeing it as a diagnostic instrument for their own reasoning. The prompt isn't just instructions for the machine. It's a test of whether you actually know what you want, what you mean, and what would count as a good answer. When the mirror shows something unclear, you don't fix the mirror. You fix your thinking.