Glossary

AILit Framework

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AI Agent: Advanced AI systems designed to autonomously reason, plan and execute complex tasks based on high-level goals.; Source: Generative AI Outlook Report (Abendroth-Dias et al., 2025); Types of AI Agents (Stryker, 2025)
AI Companion: Sophisticated AI entities designed for supporting and enhancing human experiences in daily activities, such as learning, working and others. They encompass emotional, social and practical aspects of daily life while fostering interactions and relationships with humans.; Source: Defining AI Companions: A Research Agenda—from Artificial Companions for Learning to General Artificial Companions (Chou et al., 2025)
Algorithm: A formula or set of rules (or procedure, processes or instructions) for solving a problem or for performing a task. Common examples include decision trees, clustering algorithms, classification algorithms or regression algorithms.; Source: DigComp 3.0 (Cosgrove & Cachia, 2025). Adapted from Generative AI Outlook Report (Abendroth-Dias et al., 2025)
Anthropomorphism: An interpretation of what is not human or personal in terms of human or personal characteristics.; Source: Merriam-Webster Dictionary (Merriam-Webster, n.d.)
Artificial General Intelligence (AGI): AI systems that can match or exceed the cognitive versatility and proficiency of a well-educated adult. No such systems currently exist.; Source: Adapted from What is Artificial General Intelligence (AGI)? (Bergmann & Stryker, n.d.)
Augmentation: The use of a machine in one task to increase productivity in other tasks.; Source: The EPOCH of AI: Human-Machine Complementarities at Work (Loaiza & Rigobon, 2024)
Automation: The process of linking disparate systems and software so that they become self-acting or self-regulating.; Source: K-12 Standards (Computer Science Teachers Association, 2017)
Bias: A systematic deviation from a true state. There are different forms of bias, such as the subjective bias of individuals, data and algorithm bias, developer bias and institutionalised biases that are ingrained in the underlying societal context.; Source: DigComp 3.0 (Cosgrove & Cachia, 2025). Adapted from Generative AI Outlook Report (Abendroth-Dias et al., 2025)
Bot/Chatbot: A computer program designed to simulate conversation with a human, usually over the internet, especially one used to provide information or assistance to the user as part of an automated service.; Source: DigComp 3.0 (Cosgrove & Cachia, 2025). Adapted from Generative AI Outlook Report (Abendroth-Dias et al., 2025)
Coding: The act of writing computer programs in a programming language.; Source: K-12 Standards (Computer Science Teachers Association, 2017)
Computational Thinking: The thought processes involved in formulating a problem and expressing its solution(s) in such a way that a computer – human or machine – can effectively carry it out.; Source: Computational Thinking’s Influence on Research and Education for All (Wing, 2017)
Computer Program: A sequence or set of instructions in a programming language for a computer to execute.; Source: Adapted from Cambridge English Dictionary (Cambridge University Press, n.d.)
Computer Science: See Informatics
Copyright: A type of intellectual property that protects original works of authorship as soon as an author fixes the work in a tangible form of expression.; Source: DigComp 3.0 (Cosgrove & Cachia, 2025). Adapted from Generative AI Outlook Report (Abendroth-Dias et al., 2025)
Cyberbullying: An aggressive, intentional act carried out by a group or individual, using electronic forms of contact, repeatedly and over time against a victim who cannot easily defend him or herself.; Source: DigComp 3.0 (Cosgrove & Cachia, 2025)
Data: Any digital representation of acts, facts or information and any compilation of such acts, facts or information, including in the form of sound, visual or audiovisual recording.; Source: DigComp 3.0 (Cosgrove & Cachia, 2025)
Data Literacy: Interacting with data (e.g. collecting, analysing, visualising, interpreting) with criticality, uncertainty and intrigue. It includes components such as context, aggregation, variability, visualisation and inference.; Source: AI Literacy: A Framework to Use, Understand and Evaluate Emerging Technology (Mills et al., 2024); Learning to Reason with Data (Rubin, 2020)
Decomposition: Breaking down a problem or system into components.; Source: K-12 Standards (Computer Science Teachers Association, 2017)
Deepfake: Generated or manipulated image, audio or video content that resembles existing persons, objects, places, entities or events and would falsely appear to a person to be authentic or truthful.; Source: DigComp 3.0 (Cosgrove & Cachia, 2025). Adapted from Generative AI Outlook Report (Abendroth-Dias et al., 2025)
Design Thinking: An approach to problem solving and innovation focussed on human-centred design, involving four phases (clarify, ideate, develop and implement).; Source: What is Design Thinking & Why is it Important? (Han, 2022)
Digital Citizenship: The capacity to participate actively, continuously and responsibly in digital environments (local, national, global, online) at all levels (political, economic, social, cultural and intercultural).; Source: DigComp 3.0 (Cosgrove & Cachia, 2025)
Digital Competence: The confident, critical and responsible use of and engagement with digital technologies for learning, at work and for participation in society. It includes information and data literacy, communication and collaboration, media literacy, digital content creation (including programming), safety (including digital well-being and competences related to cybersecurity), intellectual property related questions, problem solving and critical thinking.; Source: Council Recommendation on Key Competences for Lifelong Learning 2018 (European Commission, 2018)
Digital Literacy: The ability to access, manage, understand, integrate, communicate, evaluate, create and disseminate information safely and appropriately through digital technologies. It includes competences that are variously referred to as information literacy and media literacy, computer and ICT literacy.; Source: Guidelines for Teachers and Educators on Tackling Disinformation and Promoting Digital Literacy through Education and Training (European Commission, 2026a)
Disinformation: Verifiably false or misleading information that is created, presented and disseminated for economic gain or to intentionally deceive the public. It can cause public harm.; Source: Guidelines for Teachers and Educators on Tackling Disinformation and Promoting Digital Literacy through Education and Training (European Commission, 2026a)
Filter Bubble: An echo chamber (a bounded, enclosed media space that has the potential to both magnify the messages delivered within it and insulate them from rebuttal) primarily produced by ranking algorithms on digital platforms, such as search engines and social media, which personalise information without any active choice on the part of an individual.; Source: DigComp 3.0 (Cosgrove & Cachia, 2025); Adapted from Echo chambers, Filter Bubbles and Polarisation: a Literature Review (Arguedas et al., 2022)
Generative AI (Gen AI): AI systems that can generate content from general instructions (e.g. text, images, audio, video, code), process existing content (e.g. translate, correct) or analyse data (e.g. sort, summarise) based on patterns learnt from existing data. These systems generate outputs in response to user instructions, known as prompts, and rely on models trained to predict and produce relevant information.; Source: Guidelines on the Use of AI and Data in Teaching and Learning for Educators (European Commission, 2026b)
Hallucination: Phenomena where AI algorithms invent information that sounds plausible but is not factual.; Source: Generative AI Outlook Report (Abendroth-Dias et al., 2025)
Informatics: A distinct scientific discipline known in many countries as Computer Science or computing. It is characterised by its own concepts, methods, body of knowledge and open issues. Informatics covers the foundations of computational structures, processes, artefacts and systems, the design of their software, their applications and their impact on society.; Source: Informatics Education at School in Europe (European Commission, 2022)
Intellectual Property (IP): Someone’s idea, invention, creation, etc., that can be protected by law from being copied by someone else.; Source: Cambridge Dictionary (Cambridge University Press, n.d.)
Large Language Model (LLM): A neural network trained on massive amounts of text that can be used in a variety of language tasks such as sentence completion, question answering, machine translation and chatbot functions. Large language models are one of the technologies that make up generative AI.; Source: AI Learning Priorities for All K-12 Students (CSTA & AI4K12, 2025)
Machine Learning: The study of algorithms and models that machines use to perform a task without explicit instructions. Machine learning algorithms improve with experience. Advanced machine learning algorithms use neural networks to build a mathematical model based on patterns in sample “training” data. Machine learning algorithms are best used for tasks that cannot be completed with discrete steps, such as natural language processing or facial recognition.; Source: Hands-On AI Projects for the Classroom (Blair Black & Brooks-Young, 2021a-c)
Media Literacy: The ability to access, comprehend, analyse and create media, while reflecting on its impact on individuals, institutions and society.; Source: Navigating an Evolving World: First Draft of the Media and Artificial Intelligence Literacy (MAIL) Assessment Framework (OECD, 2026b)
Metacognition: Comprises both the ability to be aware of one’s cognitive processes (metacognitive knowledge) and the ability to regulate them (metacognitive control). Metacognitive knowledge encompasses knowledge of oneself as a learner (such as strengths, weaknesses, preferred time of day for study, preferred study location) and how the human brain stores, organises and retrieves information as well as effective strategies to complete the task.; Source: Guidelines for Teachers and Educators on Tackling Disinformation and Promoting Digital Literacy through Education and Training (European Commission, 2026a)
Misinformation: Verifiably false information that is spread without the intention to mislead and often shared because the user believes it to be true.; Source: Guidelines for Teachers and Educators on Tackling Disinformation and Promoting Digital Literacy through Education and Training (European Commission, 2026a)
Natural Language Process (NLP): AI technology used to understand and interact with humans’ natural language. Natural language processing powers technologies such as voice experiences and assistants, text predictors, grammar checks, text analysers (such as spam filters) and language translators.; Source: Hands-On AI Projects for the Classroom (Blair Black & Brooks-Young, 2021a-c)
Neural Network: Artificial neural networks are currently modelled after the human brain. While a brain uses neurons and synapses to process data, neural networks use layers of nodes with directed connections. Some of these connections are more important than others, so they have more weight in determining the outcome. Just like people, machines with neural networks learn through experience. As a machine processes a set of data, it recognises patterns, assigns more weight to the most important information, learns to process inputs in order to develop the most accurate outputs and creates a model from which to make future predictions or decisions. There are many types of neural networks, each with different designs, strengths and purposes.; Source: Hands-On AI Projects for the Classroom (Blair Black & Brooks-Young, 2021a-c)
Personalisation: Digital services that are tailored to individual users’ interests and preferences, especially through the application of algorithms to the users’ online behaviours.; Source: Generative AI Outlook Report (Abendroth-Dias et al., 2025)
Predictive AI: AI trained to detect patterns in existing data and forecast what is likely to happen next. This exists in tools like recommendation engines, spam filters and personalised learning applications.; Source: Generative AI vs. Other AI Types (Microsoft, n.d.)
Programming: The craft of analysing problems and designing, writing, testing and maintaining programs to solve the problems.; Source: K-12 Standards (Computer Science Teachers Association, 2017)
Reinforcement Learning (RL): A type of machine learning process in which autonomous agents learn to make decisions by interacting with their environment.; Source: What is Reinforcement Learning? (Murel & Kavlakoglu, n.d.)
Source: The starting place or the origin of a piece of information.; Source: Guidelines for Teachers and Educators on Tackling Disinformation and Promoting Digital Literacy through Education and Training (European Commission, 2026a)