The words behind the tools you already use – so you can study smarter, spot the traps, and hold your own in any conversation about AI.
Academic integrity – Doing and crediting your own work honestly – including being upfront about any AI help, under your school or uni's rules.
Agent – AI that can carry out tasks – click, browse, run steps – not just reply in a chat.
AI detector – Software that tries to guess whether you used AI. It's unreliable and biased against non-native and plain writers – it should never be the only evidence.
Cognitive offloading – Letting a tool do thinking for you. Fine for some things – risky when it's the very skill you're meant to be learning.
Context window – How much the AI can “hold in mind” at once. Go past it and it forgets the start of the conversation.
Generative AI – AI that makes new things – essays, images, code, audio – rather than just sorting what already exists.
Hallucination – When AI confidently makes something up – a fake quote, a wrong number, a citation that doesn't exist. It sounds right, so always check.
LLM (large language model) – The tech behind chatbots like ChatGPT: trained on huge amounts of text to predict the next word, over and over.
Productive failure – The evidence that trying a hard problem before you get help makes the learning stick deeper.
Prompt – The instruction you give the AI. Clearer, more specific prompts get better answers.
Sycophancy – AI's habit of agreeing with you even when you're wrong. Don't mistake confidence for correctness.
The sandwich method – Do the work yourself first, use AI to critique or extend it, then revise – and note what you changed. You keep the learning; you gain the polish.
Token – The word-chunks an AI reads and writes. Length limits and pricing are counted in tokens, not words.
Verification – Checking what AI tells you against a reliable source before you trust it or hand it in.