SQL, relational DBs · Machine learning, ethics, society · NEA checklist · 79 terms
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Tables, keys, relationships, SQL and data integrity.
9Software/hardware/data/people/procedures used to collect, store, process and present information for an organisation or purpose.
An organised collection of related data stored so it can be accessed, managed and updated efficiently.
A well-organised filing cabinet with labelled folders and clear rules about where everything goes — as opposed to a pile of papers on the floor (flat files).
Software used to create, manage, query and control access to databases. Examples: SQLite, MySQL, Access.
A database that stores data in related tables made of rows and columns, linked by primary and foreign keys.
A database structure that stores data about one type of entity in rows (records) and columns (fields).
A thing about which data is stored, such as a student, book, dog or order.
A property/field describing an entity, such as name, date_of_birth or price. Corresponds to a column in a table.
A field or combination of fields that uniquely identifies each record in a table. Must be unique and not blank (null).
Like your PPSN — it's unique to you and identifies you across all government systems. No two people share one; it never changes.
A field in one table that refers to the primary key of another table, creating a relationship between the two tables.
A library book's borrower ID connects to a person's record in the Members table. The borrower_id in the Books table is a foreign key pointing to the Members table's primary key.
A connection between database tables, usually through primary and foreign keys.
A relationship where one record in a table can be linked to many records in another table. Example: one owner can own many dogs.
The design/structure of a database, including tables, fields, data types and relationships.
A request for specific data from a database, often written in SQL.
A language used to query and manipulate relational databases.
SQL command used to retrieve data from a database table. Example: SELECT name FROM Students
SQL clause used to filter records based on a condition. Example: WHERE age > 18
SQL clause used to sort query results. Example: ORDER BY score DESC
Create, Read, Update, Delete — the four basic operations performed on stored data.
Unnecessary duplication of data in a database or system. Relational design reduces redundancy.
Organising database tables to reduce redundancy and improve data integrity.
The accuracy, consistency and reliability of data over its lifecycle. Supported by validation and good database design.
AI types, ML, agent-based modelling, privacy, cybersecurity and society.
10The design/study of computer systems that can perform tasks normally associated with human intelligence, such as perception, decision-making or language.
AI designed to perform a specific task or narrow set of tasks, without general human-like intelligence. Examples: recommendation systems, image classifiers, chess engines.
A hypothetical/advanced AI that could learn and solve many different kinds of problems across domains autonomously.
Distinguish: Narrow AI = one job only. AGI = could do anything a human can do. AGI does not yet exist.
AI that can generate new content such as text, images, code, audio or video in response to prompts.
When a generative AI system produces information that sounds plausible but is false or unsupported.
Like a confident student making up an answer in an exam — it sounds believable, flows well, but is factually wrong. Always verify AI-generated facts.
A type of AI where systems learn patterns from data rather than being explicitly programmed with every rule.
Traditional programming: you tell the computer "if a cat has pointy ears AND whiskers, it's a cat." Machine learning: you show the computer 100,000 photos of cats and it learns to recognise cats itself.
Data used to teach/train a machine learning model. Quality and diversity of training data affects the model's accuracy and fairness.
The learned pattern/structure produced by training, used to make predictions or classifications.
Assigning an input to a category or label, such as cat/dog or safe/unsafe. Computer vision often classifies images.
Using data/model patterns to estimate an unknown or future value/outcome.
A field of AI that enables computers to interpret images/video, often identifying objects or patterns.
A model's estimate of how likely its classification/prediction is correct, often shown as a percentage.
The percentage beside an image label in computer vision output (e.g. "Dog: 94%").
A system that suggests items/content to users based on data such as preferences, behaviour or similar users.
Netflix, Spotify, TikTok, Amazon — they all watch what you interact with and suggest similar things. Driven by ML models analysing millions of users.
Unfair or skewed results caused by biased data, design choices or assumptions in an algorithm/model.
If you train a face recognition system mostly on white male faces, it may be less accurate at recognising women or people of colour — the bias in training data becomes bias in the model.
Be able to suggest ways to reduce bias: diverse training data, bias auditing, human oversight.
Checking a system/model for unfair or inaccurate outcomes across different groups.
A design approach where humans monitor, review or intervene in automated/AI decisions.
Technology that identifies or verifies a person by analysing facial features in images/video.
Discuss both sides: benefits (security, finding missing persons), concerns (privacy, false positives, bias against certain groups).
A modelling approach where individual agents follow rules and interact, producing overall system behaviour.
A crowd simulation: each person (agent) follows simple rules — "avoid bumping into others," "move towards exit." From these simple rules, complex crowd movement patterns emerge.
An individual simulated entity in an agent-based model, such as a person, car, animal or cell. Agents have state and rules.
The simulated world/context in which agents exist and interact. Examples: grid, network, map.
A behaviour instruction that determines how an agent or simulation changes over time. ABMs are built from rules.
Complex overall behaviour that arises from many simple interactions between agents.
A flock of starlings (murmuration) — each bird follows 3 simple rules (stay close, avoid collision, match speed). From these, the whole flock creates stunning, complex patterns no single bird planned.
A simulation using repeated random trials to estimate possible outcomes. Example: running a dice simulation 10,000 times to estimate probability.
Principles about what is right, fair and responsible when designing/using computing technology. Key concerns: privacy, bias, accountability, safety.
A person's right/ability to control access to information about themselves.
Protecting personal data from misuse, unauthorised access or unlawful processing.
EU data protection law setting rules for handling personal data, including fairness, transparency, minimisation and rights of individuals.
For coursework: do not collect unnecessary personal data. Anonymise where possible. Get consent for surveys.
Information relating to an identified or identifiable person. Examples: name, email, image, location data.
Removing or altering personal identifiers so individuals cannot be identified from the data.
Freely given, informed agreement for data to be collected or used for a specific purpose.
Protecting computer systems, networks and data from unauthorised access, attacks or damage.
Malicious software designed to harm, disrupt or gain unauthorised access to systems/data.
Malware that blocks/encrypts access to data or systems and demands payment to restore access.
The 2021 HSE cyberattack (ransomware on Irish health service) is a real-world example used in exam contexts.
A social-engineering attack that tricks people into revealing information or clicking malicious links, often via fake messages.
"Casting a net" (phishing ≈ fishing) to catch unsuspecting users. A fake bank email asks you to "verify your details" — any credentials you enter go straight to the attacker.
A security system that monitors/filters network traffic based on rules, blocking suspicious connections.
A security guard at a building entrance — checks everyone going in and out, blocks people who don't belong.
Software that detects, blocks or removes malicious software.
Rules for creating and managing passwords, such as minimum length, complexity requirements and no reuse.
Authentication requiring two different forms of evidence, such as password plus a phone/app code.
A cash machine needs both your card AND your PIN. If someone steals your card, they still can't get in without the PIN. That's two factors.
The gap between people who have access/skills to use digital technology and those who do not.
A situation where algorithms repeatedly show users content that matches existing views, reducing exposure to other perspectives.
If you only ever read articles you agree with, and algorithms keep showing you more of the same, your "bubble" gets smaller. You see less of the world's range of opinions — relevant to elections and social media.
Being able to identify who is responsible for a system's decisions, actions or harms.
Making clear how a system uses data, makes decisions or affects users. Important in ethics/AI.
Using technology in ways that consider environmental impact, resource use and long-term effects.
Everything you need to know for the LCCS coursework project and report.
11The externally assessed individual project where a student creates a computational artefact and report based on the annual brief. Worth 30% of LC CS overall marks.
The official task requirements that the coursework artefact/report must respond to. Always map your project to its requirements explicitly.
The minimum project features specified in the coursework brief. Can earn strong marks if done well.
Additional project features specified in the brief to reach higher achievement levels. Attempt only when basic requirements are solid.
Coursework report section showing research into the brief, existing systems and the chosen topic. Do not just describe your final artefact here.
Coursework/report stage where you describe how the project will work, technologies used, and include design diagrams such as flowcharts.
Coursework/report stage covering development progress, testing, problems solved and algorithm explanation. Largest report section.
Judging how well the final artefact meets the brief and suggesting justified improvements. Use requirement-by-requirement evaluation.
A record of development milestones and work completed over time. Update weekly/fortnightly throughout the project.
A table recording tests carried out, expected results, actual results and outcome/fix. Very useful evidence for the Create section.
A diagram giving an overview of how the system or algorithm works. Brief often explicitly asks for a detailed flowchart.
A list acknowledging sources such as books, websites, datasets, code and media used in the project. Missing references can be a plagiarism risk.
A report table showing word counts for relevant coursework sections and total word count. Follow current brief's word limit.
The submitted folder containing essential digital components/programs of the artefact. Keep file structure clear.
The submitted folder containing index.html and report assets such as images, CSS and video files. Report must open locally, not online-only.