Missed the live sessions, or want to go through them again? Full recordings, lecture notes, and the quiz and flashcard tools we built live — all three classes, right here.
Discover how to craft powerful AI prompts that explain complex mathematical concepts using stories, analogies, and intuitive breakdowns — instead of just asking AI to "solve this." Watch the full lecture and access the complete notes below.
Learn how to turn NotebookLM into a personal maths tutor — feeding it your notes and textbooks, then using it to generate summaries, explanations, and even audio walkthroughs of tricky concepts. Watch the full lecture below.
Built for educators — how to use AI to design interactive quizzes, flashcards, and visual explainers that make maths click for students. Watch the full lecture, then try the tools we built live in the session below.
A growing library for maths teachers and maths students: a real problem, the exact AI prompt that solves it, and the actual result it produced. Copy the prompt, use it today.
You teach Calculus & Analytical Geometry to CS/IT Semester 3 students. The formal limit definition is second nature to you — but it lands as pure noise for students coming from a computing background, not a maths one.
"Act like a passionate computer science professor, and I am a maths communicator and passionate teacher. Help me in this situation: I want to teach the limits of calculus topic to CS and IT, Sem 3 students. In maths, limits are all about when the input value x is really, really close to a number (let's say 'a'). The output value f(x) is really, really close to a specific number (let's say 'L'), and this number L is the LIMIT of f(x). But professor, this will not make sense to Sem 3 CS and IT students — what should I do so that limit can actually make sense to them?"
A full teaching strategy reframing limits through CS-native ideas the students already trust.
View the resultYou've prepared and learnt a topic, you feel confident about it — and now you want to actually test your knowledge and intuition, not just re-read your notes.
"Create a [20-question] exam on [TOPIC] at [BEGINNER/INTERMEDIATE/ADVANCED] level based on my goal of [GOAL]. Mix multiple-choice, short-answer, scenarios, practical tasks, and explain-in-your-own-words questions. Don't show answers until I finish. Then grade me 0–100 and report my strengths, weaknesses, incorrect answers, correct explanations, and topics I should review next."
You don't just want to pass — you want to actually feel the topic, remember it for years, and build the intuition from scratch instead of memorising it for an exam.
"Act as an expert math tutor. Teach me [like derivatives in calculus] as if I am intelligent but have zero prior exposure. Follow these rules strictly: 1. Use simple everyday language and an intuitive real-world analogy before introducing any formulas. 2. Break the explanation down into tiny, digestible pieces (one idea at a time). 3. After explaining a single micro-concept, stop and ask me to explain it back to you in my own words (Feynman Technique). 4. Do not move to the next step until I successfully explain it back. Correct my misconceptions with Socratic questioning rather than just giving me the answer."
A one-micro-concept-at-a-time walkthrough that only advances once you've proven you understand it in your own words.
View the resultTurn a topic into an interactive quiz, a flashcard deck, or a story-driven explainer in minutes — then refine the prompt to match your students' level, instead of writing every question by hand. That's exactly what Class 3 walks through.
NotebookLM works well because it stays grounded in the notes and textbooks you upload — so it can summarise, explain, and generate audio walkthroughs without inventing facts outside your source material. See Class 2 for the full workflow.
Not if it's used right. This masterclass treats AI as a study and teaching partner — explaining, quizzing, summarising — rather than a shortcut that solves problems for you without building the underlying concept.