AI for Teachers
For teachers who now have to teach and use AI, often with no training and no projector.
- Level
- Nothing assumed
- Lessons
- 57
- Reading time
- 487 min
- Price
- Free, no sign-up to read
For schoolteachers anywhere who now have to teach and use AI, usually without training. How to explain it to a class that asks hard questions, plan lessons without handing over your judgement, build worksheets and differentiated versions that actually differentiate, mark without being unfair, handle AI-written homework when detectors do not work, redesign the assignments that matter, teach students to use it honestly, and run all of it with chalk when the power is out.
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Go to AI at WorkModule 1
Understanding the machine you are about to use
Before a single worksheet, you need a working picture of what a language model does — accurate enough to survive a Grade 8 class that argues back, and useful enough to predict where it will fail you. This block builds that picture from the mechanism: prediction from frequency, the two modes of answering, the memory limit of a conversation, the curriculum it has actually read, the arithmetic it cannot do, and why the same question gets two answers. It also settles which free tool runs on the phone you actually own.
By the end you can
Explain to a class, a colleague and a parent what a language model does and does not do, predict from the mechanism which kinds of question it will answer reliably and which it will get confidently wrong, and choose a free tool that runs on the phone you actually have with its training-on-your-data setting turned off
- 1Explaining AI to a class that argues backIt predicts likely next words from human writing, so sounding right and being right come apart.
- 2The free tools you actually haveEvery major model has a free tier that runs in a phone browser or inside WhatsApp; the choice that matters is not which is cleverest but which one you have turned off training on your data.
- 3Where it is reliable, and where it is notReliability tracks how many times a fact has been written down by people — so the model is sound on photosynthesis and unsound on the founding date of your district, and you can predict which before you ask.
- 4Why it cannot look it upA model on its own has no way to consult anything at answer time; when a tool adds web search it fetches pages and summarises them, which fixes staleness but not judgement about which page to trust.
- 5The conversation has a memory limitA model can only see a fixed window of text at once, so it holds a chapter but not a textbook and forgets the instruction you gave forty messages ago; start a new chat per task and paste the material in every time.
- 6It has read the American curriculumThe training text is dominated by US and UK educational writing, so unprompted the model assumes Common Core, dollars and an American exam format; paste your syllabus and a sample paper and it adapts well, but it will never do so unasked.
- 7A chatbot is not a calculatorNext-word prediction is a poor way to multiply, so every number in generated material is checked with a calculator, and the safe pattern is to have the model write the working while a separate tool does the arithmetic.
- 8Same question, different answerAnswers are sampled from a distribution rather than looked up, so asking the same question three times and comparing is a real check — disagreement flags an unreliable question, though agreement never proves the answer is right.
Module 2
Prompting as a teacher
The difference between a generic output and a usable one is almost entirely what you put in the prompt, and the things that matter most are things only you know. This block turns that into a craft: a five-part template, the one example that beats three paragraphs of description, the second message that does more than the first, control of voice and reading level, roles that steer and roles that do nothing, formats that survive being pasted into a document, what to do when the tool refuses a legitimate lesson, and a prompt file that turns one good result into a repeatable one.
By the end you can
Write a prompt that carries the five things only you know — class, constraints, prior lesson, misconception and format — iterate on the output rather than accepting the first draft, control the reading level and register of what comes back, and keep a personal prompt file that makes a good result repeatable next term
- 9The five things only you knowEvery teaching prompt should carry who the students are, what constrains the room, what came before, which misconception is live, and what shape the output must take — because the model can only condition on what is in front of it, and none of those five are.
- 10Show it one of yoursOne pasted example of your own work steers the output harder than any description of it, because the model copies the shape of what it is shown — including, if you are not careful, the flaws.
- 11The second message matters more than the firstTreat the first output as a draft and spend your effort on the critique — specific, pointed at one thing, with the reason — because a model steered by feedback improves fast, while a first prompt rewritten five times mostly does not.
- 12Set the voice and the reading level"Simple" is not an instruction; sentence length, word frequency, idiom and the ratio of concrete to abstract are, and the model drifts back to its default register after a few paragraphs unless the constraint is restated.
- 13Roles that help, and roles that do nothingA role steers the model toward the writing of people who hold that role, so "act as an examiner" changes the output and "act as a world-class expert" does not; the roles worth using are the ones whose writing looks different from the default.
- 14Prompting for formats you can printThe model writes in markdown by default, which turns into stray asterisks and hashes when pasted into Word or WhatsApp, so name the destination and ask for plain text, a numbered list, or CSV — whichever survives the paste.
- 15When it refuses a lessonRefusals come from a cautious safety filter matching surface features — words about weapons, drugs, sex, self-harm, violence — not from an understanding of your lesson, so stating the educational context, the age, and the syllabus reference usually clears a legitimate topic.
- 16Keep a prompt fileA good result is only repeatable if you saved the prompt, the pasted context and the output together — so keep a plain text file, dated, and use the tool's stored-instructions feature for the things you paste every time.
Module 3
Planning and making materials
This is where the hours go back to you. A term's worth of explanations, passages, questions, visuals, slides and revision aids can be drafted in evenings rather than weekends — provided the judgement stays in your hands and the checking is not skipped. This block takes each kind of material in turn, shows the prompt that produces it, the specific way it fails, and the check that catches the failure, and ends by lifting the view from one lesson to a whole term.
By the end you can
Produce a term's worth of classroom material — explanations, reading passages, question banks, diagrams, slides and revision aids — that is checked, localised and pitched to the class in front of you, and name the parts of a lesson and a unit that should never be handed to the model
- 17Planning with AI, keeping the judgementAI supplies breadth; you supply the class. Ask it for misconceptions, not for whole lesson plans.
- 18Worksheets, and three versions of themName the axis — support, representation, extension — or you get dilution, and always work the sheet yourself.
- 19Four explanations of one ideaAsk for the same idea as an analogy, a worked example, a story and a set of contrasting cases, then audit each analogy for where it breaks — because the model produces explanations fluently and never marks the point at which its own analogy stops being true.
- 20Passages with the words you are teachingA generated comprehension passage is only useful if it carries your vocabulary list, your setting and your reading level at once — specify all three, then check that every question's answer is actually in the text.
- 21A question bank you can trustBuild questions into a spreadsheet tagged by topic, type and difficulty, generate the answer key in a separate pass and reconcile the two, and let a term of student results tell you which questions are actually hard.
- 22Diagrams and visuals without a designerImage generators are good at scenes and bad at diagrams — they cannot reliably place labels, draw accurate anatomy or keep a map true — so ask the model for a diagram description or diagram code and draw the diagram yourself, and use image generation only for illustration.
- 23Slides, and the projector you sometimes haveGenerate the outline and the speaker notes, not the slides — a deck built from a generated outline in Google Slides or LibreOffice Impress carries your structure, prints as a handout when the projector fails, and avoids the pretty, empty deck that slide generators produce by default.
- 24Revision: flashcards and spaced practiceThe evidence for retrieval practice and spacing is strong and the model can generate a term's flashcards and a spaced schedule in minutes; the failure is cards that test recognition rather than recall, and the fix is a card format that forces the student to produce the answer.
- 25A unit plan, not just a lessonAsk the model to map a term — sequence, prerequisites, where each topic is revisited, where the checks fall — and then correct it against the two things it cannot know: your school's calendar and which topics your students have found hard before.
Module 4
Assessment, feedback and marking
Marking is where the hours go and where the fairness risk is highest, so this block is careful. It starts from the line between what a machine can judge and what only you can, then builds the pieces that keep AI on the right side of it: a rubric both a student and a model can read, feedback that gets used, a way for students to get feedback before they submit, analysis of a whole class's errors, a phone-camera workflow for handwritten scripts, an exam paper drafted against a coverage grid, and peer assessment with the model as a third marker rather than the first.
By the end you can
Use AI to give faster and more specific feedback on drafts while keeping every grade defensible — write a rubric a machine can apply and a student can read, run a bias check on any marking tool before trusting it, draft an exam paper against a coverage grid, and turn a class set of wrong answers into next week's teaching
- 26Marking: what a machine cannot fairly judgeA machine can give feedback; a grade you cannot explain to a parent is not a grade.
- 27Writing a rubric both can readA rubric criterion must be observable in the text — "cites a specific line and explains its effect" rather than "shows understanding" — because a vague criterion makes the model fall back on length and fluency, which are the biases that land on your weakest writers.
- 28Feedback students actually useAsk the model for two specific next steps and no praise — because it is trained toward agreeable, encouraging answers, and a comment that opens with three compliments is one the student stops reading before the useful part.
- 29Feedback before submission: the student-facing loopStudents can get useful critique on their own drafts if given a prompt card that forbids rewriting — but the model wants to help and will start rewriting anyway, so the card needs a re-statement line and the task needs a paper trail.
- 30Reading thirty answers at oncePaste a whole class's answers to one question and ask the model to group them by the error they contain — the groups are the reteaching plan — while remembering that it will find patterns in six answers as readily as in sixty.
- 31Marking handwritten work with a phoneA phone photograph of a handwritten script can be transcribed by the model well enough to give feedback on — but it will quietly correct the student's errors as it reads, and for maths it misreads the symbols that matter, so transcription is a draft to check, never a record.
- 32Writing the exam paperDraft the paper from a coverage grid — topic against type against marks — rather than from a prompt for "a test", and then sit it yourself against the clock, because the model does not know your board's structure until shown and cannot feel how long forty marks take.
- 33Peer assessment with a third markerWhen students mark each other against a rubric, a model marking the same scripts becomes a third opinion that makes disagreement visible and discussable — and the disagreements, not the scores, are what teach students to assess.
Module 5
Integrity: AI-written work and what to do about it
The question every staffroom asks first is "how do I catch it", and the honest answer is that you mostly cannot, and that trying to harms the wrong students. This block replaces catching with designing: why detectors fail and what to do instead, assignments that ask for what the model cannot reach, per-task rules students can follow, a written class policy with a consequence ladder and an appeal, process evidence that holds up, a fair procedure for the case you do suspect, a rethink of what homework is now for, and the conventions older students will need for declaring AI use at university.
By the end you can
Set an AI policy for a class that students can follow and you can enforce — per-task levels, a written consequence ladder, process evidence from version history and in-class drafting, redesigned high-stakes tasks — and handle a suspected case without a detector score and without wronging a second-language writer
- 34Why detectors fail, and what to do insteadDetectors flag careful second-language writing as AI; use process evidence and conversation instead.
- 35Assignments AI cannot do for youDifficulty does not stop AI; specificity to your room, your week and your students does.
- 36Teaching students to use it honestlySet a permitted level per task and require one line of disclosure; bans only make use invisible.
- 37Writing the class policyA class AI policy is one page, written with the students, that says what is permitted on each kind of task, what disclosure looks like, what happens on the first and second breach, and how a student appeals — because a rule that lives in the teacher's mood is not a rule anyone can follow.
- 38Process evidence that holds upVersion history in Google Docs or Word, an in-class first paragraph, and a short drafting portfolio make the writing process visible for every student as a matter of routine — signals of authorship, never proof, and used to support students rather than to trap them.
- 39Handling a case fairlyWhen a case reaches a decision, the standard is what a reasonable colleague would conclude from the evidence you can show — the process record, the conversation, the student's own explanation — never a detector score, and the default outcome is redoing the work, with the second-language writer given explicit protection from the accusation that reads unsurprising prose as machine prose.
- 40Rethinking homeworkHomework that asks for a product a model can produce — summaries, comprehension answers, definitions, essays — has stopped measuring anything; keep homework that is retrieval, practice with self-checking, reading with a one-line response, or evidence from the student's own home and street.
- 41Citing and declaring AI useOlder students will meet institutions that require a declaration of AI use, and the conventions exist — APA, MLA and Chicago each have one — so teach the declaration as a normal line of academic honesty, while being clear that the underlying question of who authored a machine-assisted text is unsettled everywhere.
Module 6
Teaching AI itself
At some point the subject is not your subject but the machine, and a class of thirteen-year-olds wants to know how it works, whether it thinks, why it drew their classmate's face, and whether it will take their jobs. This block is the unit: what to teach at which age, the sorting game for younger children, the deepfake lesson for older ones, the companion chatbot a teacher needs to understand, a two-week project that builds the mechanism by hand, what the studies actually say about AI tutors, and how every subject — not only computing — can carry the teaching.
By the end you can
Teach a unit on AI to any age from 8 to 18 without a device — prediction, training data, classification, bias, confident error and the deepfake problem — and read a study on AI tutoring well enough to say what it found, on whom, and what it means for how students in your class should be allowed to use the tool
- 42An AI lesson with no screen and no powerThe mechanism — prediction, confident error, learned from us — can be taught with chalk and voices.
- 43What to teach at which ageThe mechanism scales down further than people expect — pattern and sorting at 8, prediction at 11, training data and bias at 14, how it is built and what it is for at 16 — and no country has a settled AI curriculum yet, so the honest frame is a progression rather than a syllabus.
- 44The sorting game for younger childrenClassification, not generation, is the right first model for a child — a machine shown many labelled examples learns to sort new ones, and gets it wrong at the boundary — and it can be taught with a pile of pictures and two boxes, with Teachable Machine as the free follow-up if a laptop appears.
- 45Image generators and deepfakesFabricated intimate images of classmates are a present problem in schools, and the lesson that helps is not "how to spot a fake" — which is becoming impossible — but what the tools do, why it is a serious harm and increasingly a crime, and exactly what a student should do in the first hour if it happens to them.
- 46The AI companion problemCompanion chatbots are built to keep the user talking and to agree with them, which is exactly what a lonely fourteen-year-old wants and exactly what a struggling one should not have — so a teacher needs to know what the apps do, what the warning signs are, and where the safeguarding route runs.
- 47A student project that teaches the mechanismStudents aged 14 and up can build a working next-word predictor by hand from a page of text — a tally table of which word follows which — and generate sentences from it, and the moment their own tiny model produces fluent nonsense is the moment the big one stops being magic.
- 48The evidence on AI tutorsThe best-run studies so far find that unrestricted chatbot access during practice raises practice scores and lowers exam scores, while a tutor constrained to give hints does not harm and sometimes helps — so the finding is about design, and a teacher should let students use the tool for explanation and hints, not for answers.
- 49AI in every subject, not just computingThe progression needs no timetable slot if each subject carries the piece that is naturally its own — history teaches source and provenance, science teaches evaluation, languages teach the reliability gradient, art teaches the authorship debate, maths teaches the arithmetic failure — and a department that agrees who teaches what gets a whole unit for the cost of one meeting.
Module 7
The multilingual, mixed-ability classroom
Most classrooms in the world hold several languages and a wide spread of reading ability, and this is where these tools can do the most good and the most quiet harm. This block is practical: translation with a back-translation check, explanations that use the language students actually speak, formats for students who struggle to read, free speech-to-text and text-to-speech, support for students with disabilities that stops short of replacing the specialist, the student who asks the machine what she will not ask you, the student who is far ahead, and the honest situation of a home language the model has barely seen.
By the end you can
Use AI to bridge language and ability gaps in a real classroom — translate and back-translate a worksheet, produce a levelled and a dyslexia-friendly version of the same text, set up free speech-to-text and text-to-speech on a phone — while stating from the mechanism where translation quality falls off and which students it fails
- 50Translation, and the back-translation checkTranslation quality tracks how much text exists in a language — excellent for Spanish and Hindi, uneven for Yoruba and Amharic, poor for languages with a few million speakers and little web presence — and the cheap check is to translate the result back and compare it with what you started with.
- 51Explaining in the language students actually speakStudents in most classrooms think in a mixed language — Hinglish, Sheng, Taglish, Pidgin, Arabic with French — and the model can explain in that mix if shown a sample of it, which makes an explanation land that the textbook's formal register never did.
- 52For students who struggle to readThe same text can be made reachable for a struggling reader by chunking, spacing, a glossary and an audio version — all free, all in minutes — and the trap is simplifying the ideas when the barrier was the format.
- 53Speech-to-text and text-to-speech, for freeDictation and read-aloud are built into every phone and cost nothing, and they work well in major languages and less well in accented English and small languages — so test with the actual student before relying on them, and treat the transcription as a draft the student corrects.
- 54Supporting students with disabilitiesAI gives a teacher without a specialist some real tools — image description for a blind student, live captions for a deaf one, structured and predictable materials for an autistic one, chunked tasks for ADHD — and the line to hold is that these support the student's plan rather than substituting for the professional who should write it.
- 55The quiet student and the private questionStudents ask a chatbot what they will not ask a teacher in front of the class — the question that reveals they are behind — and the same privacy that makes them ask means nobody corrects the wrong answer, so a teacher turns the private questions into a class routine.
- 56Stretching the student who is aheadThe student who finishes in four minutes now has an unbounded question-answerer — and is also the student least likely to be corrected when it is wrong, because she is confident and you are busy — so the stretch is to make her the checker, not the consumer.
- 57When the home language has no training dataFor a language with a few million speakers and little written on the web, the model is not a translator and will not become one through prompting — so use it for the English side only, involve speakers for the rest, and know that projects exist to build the data and that whose languages get models is a political question, not a technical one.