Glossary

The words behind AI and STEAM, explained properly.

31 terms written for parents, teachers and principals. No jargon defended with more jargon.

Quick answer

This glossary defines the 31 terms schools meet most often when starting an AI or STEAM programme, from machine learning and prompt engineering to rubrics and student data privacy. Each entry gives a one-sentence definition, the context behind it, and how the idea appears in a NASCA classroom.

Entries are grouped into AI, Curriculum, Robotics, Assessment, and Data and Safety. Every term links to the NASCA programmes where students meet it.

AI

Artificial intelligence

Artificial intelligence is software that learns patterns from data and uses them to make predictions or generate new content, instead of following rules a person wrote by hand.

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Machine learning

Machine learning is the part of AI where a model improves at a task by being shown labelled examples, rather than by being programmed with explicit rules.

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Generative AI

Generative AI produces new text, images, audio or code by predicting what should come next, based on patterns learned from very large amounts of training data.

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Hallucination

A hallucination is when a generative AI states something false as though it were fact, usually because the model is predicting plausible language rather than retrieving verified information.

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Prompt engineering

Prompt engineering is the practice of writing instructions that get a reliable, specific result from an AI model, using context, constraints, examples and a clear description of the output you want.

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AI literacy

AI literacy is the ability to recognise where AI is being used, understand roughly how it works, judge its output, and decide when using it is appropriate.

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Training data

Training data is the collection of examples a model learns from. Its size, balance and quality set the ceiling on how well the finished model can perform.

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Model bias

Model bias is a systematic error that makes an AI system perform worse for some groups than others, usually because the training data under-represented them.

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AI ethics

AI ethics is the study of how automated systems should be built and used: who benefits, who is affected, what is disclosed, and who is accountable when the system is wrong.

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Quantum computing

Quantum computing uses quantum bits, which can hold combinations of states, to attack a narrow class of problems that conventional computers find impractically slow.

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Curriculum

STEAM education

STEAM education teaches science, technology, engineering, arts and mathematics as one connected practice, usually through projects where students design, build and test something real.

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Computational thinking

Computational thinking is a problem-solving approach built on four moves: break a problem down, spot the patterns, ignore what does not matter, and write a repeatable procedure.

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Design thinking

Design thinking is a structured way to solve problems for real people: understand them, define the problem sharply, generate options, build a rough prototype and test it with users.

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Project-based learning

Project-based learning organises teaching around an extended, real task that students complete and present, with content taught at the point it is needed.

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Capstone project

A capstone project is a substantial final piece of work where a student applies everything learned across a programme to one problem of their own choosing.

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Coding

Coding is writing instructions a computer can execute, typically starting with block-based tools in primary school and moving to text languages such as Python by middle school.

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Python

Python is a general-purpose programming language with readable syntax and the largest ecosystem of data science and AI libraries, which makes it the standard first text language in schools.

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App development

App development is building software people can actually use, covering interface design, logic, data storage and the testing that happens before anyone else touches it.

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Data science

Data science is the practice of turning raw data into decisions, through cleaning, analysis, visualisation and a clear account of what the numbers do and do not support.

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