AI for Students
How to use these tools and still end up knowing things.
- Level
- Nothing assumed
- Lessons
- 73
- Reading time
- 597 min
- Price
- Free, no sign-up to read
A course for school and university students on using AI without losing the ability to think. It covers what actually happens to memory when the model does the work, how to get unstuck rather than finished, how to make it quiz you, how to check a citation exists, writing with it without losing your voice, why detectors do not work, and revision that survives an exam hall with no AI.
Opens after the AI at Work exam
Sign in, finish that course, and pass its exam. You can read this syllabus meanwhile.
Go to AI at WorkModule 1
What the machine is doing when it helps you
Before any study technique, the mechanism: what a language model computes, why it sounds certain when it is wrong, what that does to your memory, and which tool actually belongs to which job.
By the end you can
Explain what a language model computes when it answers you, predict from that mechanism which study tasks it will help with and which it will quietly damage, and pick the right free tool for a given piece of schoolwork instead of defaulting to a chatbot
- 1Two Kinds of HelpAfter a study session, ask what changed: the document, or you.
- 2What Happens To Your MemoryThe tutor that gave answers raised practice scores and lowered exam scores; the one that gave hints did neither harm.
- 3What it is actually doingA language model predicts the next token from a compressed statistical impression of its training text, so it is strong on repeated general material, weak on specific rare facts, and blind to the letters inside words.
- 4Why it invents things, and whenFabrication is the model doing its job — producing plausible text — so risk concentrates in names, numbers, dates and references, not in the general explanation around them.
- 5The fluency trapClarity produces the feeling of understanding before any of the work that earns it, so the only honest test of an AI explanation is whether you can reproduce it from a blank page afterwards.
- 6Context, limits, and why it forgetsEverything the model can use must fit in a fixed token window, it attends least to the middle of a long one, and quotas are measured in the same tokens — so pasting less and starting fresh chats improves answers and stretches a free tier at once.
- 7Which tool for which jobMatch the tool to the failure you can afford: deterministic tools for arithmetic, existence and data, and the model for explanation, phrasing, and deciding which deterministic tool to reach for.
- 8Getting set up for nothingFree tiers, Hugging Face Spaces, Colab, and a small local model on Ollama cover almost everything in this course, so the real skill is spending a limited allowance on the questions that are actually hard.
- 9A working agreement with yourselfWrite down in advance the five or six capabilities you will never outsource, your disclosure sentence, your verification rule and your evidence habit, because the decision made calmly is the one that holds at midnight.
Module 2
Asking well
The difference between a useless answer and a useful one is usually the question. Task, level, constraint, form; the source supplied rather than remembered; the follow-up that finds the weak joint.
By the end you can
Write a study prompt that states the task, the level, the constraints and the required form, ground it in a source you supply rather than the model's memory, and diagnose from a bad answer whether the fault was the model's or your instruction's
- 10The anatomy of a study promptState level, task, constraints and form, include your current wrong understanding, and when an answer disappoints work out which of those four you left out.
- 11Give it the sourcePaste the smallest span of the real source rather than relying on the model's memory, then check the summary against the passage instead of checking whether the fact exists.
- 12Ask for the working, then check itAsk for steps because they improve accuracy and give you something auditable, then audit with units, magnitude and the one difficult joint — never treating the stated reasoning as proof that it is what produced the answer.
- 13Follow-ups that find the weak jointReplace "are you sure" with follow-ups that produce checkable material — what would make this wrong, argue the opposite, what would a marker criticise — and test stability by re-asking cold in a fresh chat.
- 14Explain it at three levelsAsk for the same idea below your level, at your level, and above it — the third pass names which parts of what you were taught are teaching approximations, and that is usually what separates a good answer from an excellent one.
- 15Conversations have a shapeA conversation is a growing block of text the model re-reads every turn, so drift, anchoring and agreeableness accumulate — carry a short summary into a fresh chat instead of arguing with a long one.
- 16What not to pasteYou can consent to a service handling your own data but never somebody else's, so placement, participant and classmate material stays out of the window regardless of privacy settings.
- 17Diagnosing a bad answerBad answers have five distinguishable causes — underspecified question, missing material, genuine error, degraded conversation, wrong tool — and each has its own fix, so diagnose before you reword.
- 18Prompts worth keepingKeep six to ten prompts you actually reuse in one plain file, and note that the clauses doing the real work are the restrictions — one at a time, do not rewrite, do not help me.
Module 3
Study that raises the difficulty
The techniques with the strongest evidence behind them all feel worse than the ones that do not work. Hint ladders, retrieval, spacing, interleaving and self-explanation, rebuilt around a tool that will otherwise hand you the answer.
By the end you can
Run a study session in which the assistant raises the difficulty rather than lowering it — a hint ladder instead of a solution, retrieval before review, spaced and interleaved practice, explanation produced by you — and demonstrate afterwards what you can do with the window closed
- 19Getting Unstuck, Not Getting FinishedAsk for the smallest hint that lets you continue, then finish it yourself.
- 20Make It Quiz YouA model that asks you questions is worth more than one that answers them.
- 21Building a hint ladderTake the smallest hint that lets you continue, and when checking work ask only for the number of the first wrong line — the gap between locating an error and being told the fix is where the learning is.
- 22Retrieval before reviewWrite from a blank page before you open anything, because the gap that matters is what you were confident about and had wrong, and no fluent explanation will ever show you that.
- 23Spacing and interleavingForgetting a little between sessions is the ingredient that makes spacing work, and mixing problem types without labelling them is what trains the recognition an exam actually tests.
- 24Explaining it backExplaining from memory to a listener who refuses to help produces the learning; the most useful feedback to ask for is which parts you have memorised correctly without understanding.
- 25Worked examples, and when to stop using themWorked examples help beginners and hinder people who already have the method, so fade from full example to blank problem by removing steps from the end — and cap how many examples you read before moving on.
- 26A study session that worksBookend every session with a blank page, cap the assisted block with a timer, and close with three lines saying what you can now do unaided and what you cannot.
- 27Study groups, with and without the machineAnswer alone, argue with someone who disagreed, then check the source — a group that consults the assistant first has an answer and stops producing the reasoning that the exercise exists to train.
- 28Proving it to yourselfPredict your score before you check it, and record specific failures rather than grades — the average gap between what you predict and what you get is the most useful number you can have about yourself.
Module 4
Subject by subject
The same tool behaves differently in maths, code, the lab sciences, the essay subjects and language learning. Each has a characteristic failure, and each has one check that catches it.
By the end you can
Name the characteristic failure mode of an AI assistant in a given subject — arithmetic in maths, plausible invalid steps in proof, code that runs and is wrong, invented quotations in history, fossilised errors in language learning — and apply the specific check that catches it, using a free tool where a deterministic one is required
- 29Maths, and the arithmetic problemDigits are tokenised without place value, so arithmetic degrades with length while looking perfectly normal — take the method from the model and the number from SymPy, Maxima or a code interpreter.
- 30Proofs, and the step that looks fineGenerated proofs fail by making one unjustified move inside otherwise correct working, so read upward from the conclusion asking what licenses each step, and check the seven standard illegal moves.
- 31Code that runs and is wrongGenerated code that runs is not code that is correct — read every line, test the boundary case, refuse rewrites when debugging, and write the tests yourself from the specification.
- 32The lab sciences, and the dimension checkCarry units through every line as algebra and the dimensions will catch most wrong formulae instantly — then apply significant figures and uncertainty yourself, because those are the parts a lab report is actually marked on.
- 33History, and the quotation that was never saidHistory runs on exactly the specifics a model is weakest at, so trace every quotation to a primary source — a phrase search in Google Books restricted by date will expose most invented attributions in two minutes.
- 34Literature, and the text it half remembersGenerated quotations from literary works are usually near-misses, and an argument built on words that are not in the book is worth nothing — read and notice first, consult the critical consensus only afterwards.
- 35Languages, where it is genuinely excellentThe tool is genuinely excellent for input, explanation and drilling, but an agreeable partner that understands your errors without flagging them will fossilise them — demand a correction list after every message.
- 36Statistics for courseworkGive the model the design, the measurement level, the sample sizes and the real question, then run the test in jamovi or R and plot the data before you trust any number, because most analysis errors are visible in the plot.
- 37The essay subjects, and what a marker is reading forGenerated essays fail identically across subjects — uncontestable thesis, generic evidence, straw objection, reorderable paragraphs — so own the outline, and check every case, statute or named source against a free primary database.
Module 5
Research, sources and evidence
How a citation gets invented, how to find the real paper for nothing, how to read one quickly, and how to say what level of evidence a claim actually rests on.
By the end you can
Take a claim from a model to a verifiable primary source using free tools, judge what strength of evidence that source carries, and build a bibliography in which you have opened every item
- 38Research, And Sources That ExistNever cite a source you have not opened. Asking the model if it is real does not count.
- 39How a citation gets inventedCitations are a rigid form that a predictor reproduces easily, so plausible fabrications and real-paper hybrids both occur — resolve the DOI to check existence, and read the page to check support.
- 40Finding the paper, for nothingLibrary proxy, Unpaywall, Scholar's all-versions, preprint servers, PMC and emailing the author will reach almost any paper free — and the page number you record while reading is what turns a citation into evidence.
- 41Reading a paper without reading all of itRead in the order abstract, figures, aim, discussion, limitations — and check any claim that matters against the results section rather than the abstract, because abstracts systematically overstate.
- 42What counts as evidenceMatch the claim to the design — causal language requires randomisation, and comparing response rates across two separate trials is invalid however natural it reads.
- 43When it searches the webRetrieval replaces invented sources with real sources attached to claims they do not make, so the check is unchanged: open the link, find the sentence, and follow reporting back to the original.
- 44Notes that survive the termCompression is the work, so an AI summary is a note nobody encoded — write in your own words, keep a confusion section, and use the model to interrogate your notes rather than to produce them.
- 45Building a bibliography you can defendEvery line of a reference list asserts that the work exists, that you read it, and that it supports the claim — so a model can be cited as a method you used, never as evidence for a fact.
- 46Misinformation, and what you pass onVerify provenance rather than appearance — reverse image search, the primary record, the date, and lateral reading about who runs the page — because generated media can no longer be judged by looking at it.
Module 6
Writing, and keeping your voice
Where in the writing process the thinking actually happens, how to get help at the points that do not displace it, and what makes prose recognisably yours.
By the end you can
Produce written work whose argument, structure and sentences are yours, use assistance at the specific points that do not displace your thinking, and show a process record demonstrating how the piece was made
- 47Writing With It Without Losing Your VoiceIf the first draft is not yours, everything after is editing someone else's argument.
- 48Where the thinking happensIdeas are finished by being ordered, so assistance at the claim and structure stages removes the thinking itself — the outline is the essay, and everything downstream can be helped safely.
- 49The blank page, without outsourcing itBlank-page paralysis has four distinct causes with four different fixes, and the request that removes the essay is the short one — a suggested thesis is fifteen words and it is the entire piece.
- 50Structure that carries an argumentShuffle your paragraphs — an argument breaks when reordered and a list does not — and the marks live in analysis and in stating the objection at its strongest.
- 51Editing your own proseAccept only changes you can explain, ask for diagnosis rather than replacement, and reject the standard drift towards nominalisation, hedging and abstraction that makes edited prose lifeless.
- 52Your voice, measurablyVoice is the visible record of a person making choices — which example, which objection, which concession — and it is what markers reward, so build a corpus of your own writing to compare drafts against.
- 53Feedback that is actually usefulPaste the mark scheme, name a specific hostile reader, forbid rewriting and encouragement, and take feedback on structure before sentences — then use the comments on your last five assignments to find the fault that repeats.
- 54Presentations, posters and visualsWrite the talk first and let slides follow from it, and never generate an image that carries information — a generated diagram is a picture of what a diagram looks like, with plausible wrong labels.
- 55Group work, and shared rulesAgree the group's AI rule in writing before the first session, work in a shared document with version history, and require each person to confirm they opened every source in their own section.
Module 7
Rules, honesty and the record
What your institution's policy actually says, how to disclose, why detectors are not evidence, what to do if you are accused, and the process record that protects you before anybody asks.
By the end you can
Read your institution's AI policy and place a given piece of work on the permitted, disclosed or prohibited scale, write a disclosure statement, explain from its mechanism why a detector score is not evidence, and keep a process record that would survive an academic-integrity hearing
- 56The Rules, And Why Detectors Do Not WorkDetectors measure how typical your writing is, not who produced it; keep a process record instead.
- 57Reading your actual policyFind the module handbook, the brief and the integrity policy, download them, and where they are silent ask in writing and keep the reply — because several policies make non-disclosure itself the offence.
- 58Writing a disclosure that helps youA disclosure is a factual account, not a confession — name the tool, the stage, what happened to the output, and state that you obtained and read every source, because unread sources is the actual offence in most cases.
- 59How detectors work, and why they cannotDetectors measure perplexity and burstiness — how typical the writing is — so they systematically misclassify second-language and formulaic writers, and the base rate means roughly half of flagged essays can be innocent even with generous accuracy assumptions.
- 60The process recordA process record cannot be created after an accusation, which is exactly why it works — write in a tool with version history on, never paste a finished draft into an empty document, and photograph paper drafts.
- 61If you are accusedAn allegation is not a finding — do not confess to what you did not do, get your students' union adviser involved before responding at length, and remember that being able to discuss your own work in detail is the strongest evidence there is.
- 62Where the line really isAsk what the assessment is trying to measure and whether your use interferes with measuring it — a test that generalises to tools that do not exist yet, unlike any list of rules.
- 63Accessibility, fairness and who these rules fall onAn adjustment removes a barrier to demonstrating what you know without supplying what you know — get it documented formally, because a registered adjustment is a complete answer rather than an explanation offered afterwards.
- 64What happens to what you typeStorage, training and human review are three separate questions with different answers — the training toggle is usually yours to set, an institutional account is a work account with logs, and the only content safe from every future is content you did not type.
Module 8
Assessment, and the years after it
Past papers, timed practice, vivas, long projects, applications, and the honest question of what is worth learning when the tools can do a great deal of it.
By the end you can
Prepare for closed and oral assessment by practising under the conditions of the test, manage a long project with a defensible record, and decide which capabilities to build unaided given what the tools can and cannot do
- 65Revision That Survives The Exam HallStudy under the conditions of the test: silent, timed, blank paper, nothing to ask.
- 66The Honest Case For Still Learning ItYou cannot supervise what you cannot do, and supervision is the job that is left.
- 67Past papers and mark schemesPast papers are study material rather than a final test, so start in week three, mark yourself against the scheme first, and only then ask which marks were lost and where — never for a model answer.
- 68Practising under the conditions of the testPractise in the conditions of the test — timed, silent, handwritten, cold, with nothing to look up — because memory is context-sensitive and coursework-heavy courses hide the gap until the day.
- 69Vivas, and being asked about your own workA viva tests why you made each choice and what you rejected, so rehearse out loud against a model playing a difficult examiner — and a calm "I do not know, but I would look at X" scores better than a confident invention.
- 70Long projects and dissertationsWrite a working document from week one — it converts reading into thinking, removes the blank page, and produces the strongest possible process record — and keep the dated file of how your question changed.
- 71Applications, statements and interviewsThe test for every sentence in an application is whether another applicant could have written it — so use the model to ask you questions that surface specific stories, never to produce the statement.
- 72Placements, and rules that are not your university'sOn placement three rulebooks apply at once and the strictest governs — the organisation's information does not leave its systems, and a professional body's fitness-to-practise process is a far heavier matter than academic misconduct.
- 73What to learn anywayThe shift is from production towards judgement, and judgement is built by production — you cannot supervise a skill you never acquired, which is why the argument for learning survives the tools that do the producing.