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Academic Integrity 

Compliance with ethical and professional principles, standards and practices and consistent system of values, that serves as guidance for making decisions and taking actions in education, research and scholarship. (National Academic Integrity Network - NAIN) 

Accountability 

Ensures that responsibility for AI decisions remains with identifiable individuals or entities, and that mechanisms are in place in case of harm or failure (Guidelines for the Responsible Use of AI in the Public Service). 

AI literacy 

An individual's ability to clearly explain how AI technologies work and impact society, as well as to use them in an ethical and responsible manner and to effectively communicate and collaborate with them in any setting.  (Chiu, et al., 2024). 

 AI literacy extends beyond technical proficiency to encompass ethical considerations, societal impacts, and practical applications. It includes competencies such as understanding AI mechanisms, evaluating its implications, and applying AI in diverse contexts. (Biagini, 2025) 

AI System 

A machine-based system capable of operating autonomously and producing outputs like predictions, recommendations, or decisions based on input data (Guidelines for the Responsible Use of AI in the Public Service). 

Artificial Intelligence 

At the beginning it was described as "Computer programs which seemingly exhibit intelligence, that is, computers perform tasks which when performed by humans require them to be intelligent” (Rajaraman, 2014). Nowadays "now described as computer systems performing complex tasks, hitherto performed by humans, such as reasoning, making decisions, or solving problems. (IBM, 2024)  

Assistive Artificial Intelligence 

Assistive artificial intelligence applications are used to enhance accessibility and personalised learning for students with disabilities. These applications include intelligent tutoring systems and adaptive learning platforms that create inclusive educational environments.  (Arias-Flores et al., 2025)

Critical Digital Literacy 

The ability to apply a critical lens to all aspects of digital technologies, including digital information, and through research, reflection and discussion to arrive at a considered position on their design and/or redesign (Pegrum, et al.,2022) 

Compliance 

Adhering to relevant legal and ethical guidelines, such as the EU AIAct and GDPR, when designing and using AI systems (Guidelines for the Responsible Use of AI in the Public Service). 

Content Generation  

AI's ability to autonomously generate text, images, audio, or video (Guidelines for the Responsible Use of AI in the Public Service). 

Data Collection & Processing  

The process of gathering, cleaning, and preparing data for use in AI models in a manner that complies with data protection regulations (Guidelines for the Responsible Use of AI in the Public Service). 

Decision Framework for AI  

 A structured approach to evaluate whether AI is an appropriate solution for a given problem or potential improvement (Guidelines for the Responsible Use of AI in the Public Service). 

Deep Learning 

Is a subset of machine learning that was developed in the 1980s and is based on using so-called artificial neural networks to find patterns in training data. They are called ‘deep’ because multiple layers of nodes and activation functions are used to explore the data. 

Digital Literacy 

It is the ability to access, manage, understand, integrate, communicate, evaluate and create information safely and appropriately through digital technologies for employment, decent jobs and entrepreneurship. It includes competences that are variously referred to as computer literacy, ICT literacy, information literacy and media literacy (UNESCO, 2018). 

Diversity, Non-Discrimination, and Fairness AI 

A key principle of responsible AI that ensures equitable outcomes and prevents bias in AI systems (Guidelines for the Responsible Use of AI in the Public Service).  

Ethics Guidelines for Trustworthy AI  

A document by the European Commission’s High-Level Expert Group (HLEG) outlining principles and principles for responsible AI (Guidelines for the Responsible Use of AI in the Public Service).  

EU AI Act 

First European Act to regulate Artificial Intelligence. Legislation enacted by the European Union to ensure that AI systems are used in a safe, transparent manner that is aligned with fundamental human rights. It categorises AI systems by risk levels and sets compliance principles accordingly (Guidelines for the Responsible Use of AI in the Public Service). 

GDPR (General Data Protection Regulation)  

European regulation governing data protection and privacy, critical for AI systems handling personal data (Guidelines for the Responsible Use of AI in the Public Service). 

Generative Artificial Intelligence 

A machine-based system designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments' (EU Artificial Intelligence Act 2024).  

GPT (Generative Pre-trained Transformer) 

A type of large language model that uses a transformer architecture. GPT models are pre-trained on vast datasets and fine-tuned for specific tasks, capable of generating human-like text across a variety of contexts. (OECD,  2026. Digital Education Outlook.) 

Hallucination 

A phenomenon wherein a large language model (LLM)—often a generative AI chatbot or computer vision tool—perceives patterns or objects that are nonexistent or imperceptible to human observers, creating outputs that are nonsensical or altogether inaccurate (IBM). 

Human Agency and Oversight 

A principle ensuring human control and intervention in AI systems, prioritising ethical decision-making (Guidelines for the Responsible Use of AI in the Public Service) 

Large Language Models (LLMs) 

A category of foundation models trained on immense amounts of data, making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks (IBM). 

Machine Learning 

It is a set of algorithms that attempt to model the underlying patterns or distributions in data. 

Prompt 

A prompt is the input text or instruction given to a GenAI model to produce a specific output. Prompt engineering involves designing and refining prompts to achieve the desired responses or behaviours from the GenAI model, often through trial and error.  (OECD, 2026. Digital Education Outlook). 

Regulatory Compliance 

The principle that AI systems align with regulations such as the EU AI Act and GDPR (Guidelines for the Responsible Use of AI in the Public Service). 

Responsiveness 

The capacity of AI systems to adapt to user needs, providing personalised, human-centred public services (Guidelines for the Responsible Use of AI in the Public Service). 

Societal and Environmental Well-Being 

 A principle that ensures AI systems contribute positively to society and minimise environmental harm (Guidelines for the Responsible Use of AI in the Public Service). 

Technical Robustness and Safety 

 A principle ensuring AI systems are resilient, secure, and reliable under all conditions (Guidelines for the Responsible Use of AI in the Public Service). 

Transparency 

The principle that AI systems are explainable, clear, and understandable to users and stakeholders (Guidelines for the Responsible Use of AI in the Public Service). 

Verification & Validation 

 A phase in the AI lifecycle where AI models are tested for compliance with ethical and legal standards (Guidelines for the Responsible Use of AI in the Public Service).