Most schools are having the wrong argument about AI. They are debating how to catch it — detectors, lockdown browsers, honor pledges — as though the goal were to preserve a world in which take-home essays and lab reports still measure something. That world is gone. A student can now generate a polished, correctly formatted, mechanistically plausible writeup of an experiment they slept through. If your assessment can be defeated by a chatbot, the chatbot is not the problem. The assessment is.
This course takes the opposite posture, and it has two halves that only work together.
Half one: teach students to use AI well
We do not ban the tool. Banning it is both unenforceable and dishonest — the chemists and engineers these students will become are already using AI every day, and a school that pretends otherwise is preparing them for a world that no longer exists. So we teach AI as an explicit skill:
- How to ask it to explain a mechanism three different ways until one clicks — and how to check the explanation against the textbook, because it will confidently invent things.
- How to use it as a tireless practice partner: "give me ten stoichiometry problems at increasing difficulty, then check my work and tell me where my reasoning broke."
- How to spot when it is wrong — an unbalanced equation, an impossible product, a number with the wrong units — which is itself a stiff test of whether the student actually understands the chemistry.
Used this way, AI becomes an accelerant for the Learn and Master stages. A student who can interrogate a model, catch its errors, and use it to drill themselves is learning faster than any previous generation could. We want that.
Half two: assess where AI cannot reach
And then — this is the part that makes the first half safe — the grade does not come from anything a student does alone with a screen. It comes from three in-person demonstrations a chatbot is structurally incapable of doing for them. Each maps onto one of the course's lab-notes essays.
- The titration defense. The student performs an acid–base titration at the bench, then defends it on the spot — why did you choose that indicator? what does the equivalence point mean? where did your error come from and which direction did it push your result? AI can describe a titration. It cannot make this student's hands steady or answer for this student's data.
- Timed qualitative analysis. Handed a set of unknown solutions and a finite amount of time, the student must identify them through their own reactions — precipitates, flame colors, gas tests. It is reasoning under a clock, with real reagents, where a wrong call is visible in the test tube. There is no prompt to type.
- The oral lab-notebook defense. The student sits across from the teacher and walks through their own notebook — this entry, this crossed-out number, this anomaly — and explains the thinking behind it. Generated text has no memory of a Tuesday afternoon. The student's own notebook does, and so does the student.
We are not trying to build an assessment AI can't help with. We are building one AI can't replace the student in — because the thing being measured is whether this person can actually do the chemistry.
Why this is the honest answer
The two halves need each other. Teaching AI without changing assessment just hands students a faster way to cheat. Changing assessment without teaching AI leaves them unprepared for the tools their field already runs on. Together, they resolve the tension completely: a student is encouraged to use every tool available to learn, precisely because the moment of accounting is a live one — their hands, their reagents, their notebook, their voice.
This is what we mean when we call the course AI-proof by design. Not walled off from the future, but built so that the future's most powerful tool makes our students more capable rather than less — and so that, when it comes time to show what they know, there is no screen to hide behind. Only the bench, and a person who can stand at it.