Terms and concepts for discussing AI in education

1. Educational technology

21st Century Skills, the 4C’s

The four C’s stand for Critical thinking, Communication, Collaboration, Creativity and were formulated in the P21 framework (Partnership for 21st Century Skills, 2002). These skills are considered central for students in today’s society. What is most important for teachers is to use AI in ways that strengthen, rather than replace, the four C’s.

Bloom’s digital taxonomy

Bloom’s taxonomy is a classic model from 1956, later revised by Anderson and Krathwohl (2001), and digitised by Andrew Churches (2008). It places learning objectives in cognitive levels ranging from remembering to creating. What is most important for teachers is to understand how AI activities can be mapped onto different levels and avoid staying only at superficial learning goals.

DigCompEdu (Digital Competence Framework for Educators)

DigCompEdu is the EU’s framework for teachers’ digital competence, developed by the European Commission, Joint Research Centre (2017). The framework highlights how teachers need digital skills in teaching, assessment, and professional development. What is most important is to include AI competence as part of teachers’ professional responsibilities.

SAMR model (Substitution, Augmentation, Modification, Redefinition)

SAMR stands for Substitution, Augmentation, Modification, Redefinition and describes how technology can transform learning activities, from simple replacement to completely new types of tasks. The model was developed by Ruben Puentedura (2006) and is widely used to analyse the added pedagogical value of technology. What is most important for teachers is to reflect on when AI truly redefines teaching instead of just replacing old methods.

SECTIONS model

SECTIONS stands for Students, Ease of use, Cost, Teaching functions, Interaction, Organisational issues, Networking, Security. The model was introduced by Tony Bates (1995, updated 2014) as a decision-making tool for selecting technology in education. What is most important for teachers is to use the model to decide whether AI tools are pedagogically justified and practically sustainable.

SOLO taxonomy (Structure of the Observed Learning Outcome)

SOLO stands for Structure of the Observed Learning Outcome and was developed by Biggs and Collis (1982). The framework is used to describe the depth of student understanding, from simple to complex levels. What is most important for teachers is to design teaching where AI supports deeper learning rather than just quick answers.

TPACK model (Technological, Pedagogical, Content Knowledge)

TPACK stands for Technological, Pedagogical, Content Knowledge and emphasises the interplay between technology, pedagogy, and subject matter. The model was formulated by Mishra and Koehler (2006) to show how effective teaching requires a balance of these three areas. What is most important for teachers is to strengthen their technological competence while maintaining pedagogical and subject integrity.

UDL (Universal Design for Learning)

UDL stands for Universal Design for Learning and was developed by the organisation CAST in the 1990s. The framework aims to create inclusive teaching through varied forms of representation, engagement, and expression. What is most important for teachers is to see AI as a tool to increase inclusion and accessibility in the learning environment.

2. Use of AI

Academic integrity

Academic integrity is a classical principle of honesty, responsibility, and fairness in learning and research. With AI, the boundary of what counts as a student’s own work is challenged. What is most important for teachers is to establish clear rules for AI use and explain why they are necessary.

Affordance

Affordance was introduced by James J. Gibson (1977) to describe the possibilities a user perceives that a tool offers. In AI, this means that different users may see different pedagogical possibilities in the same tool. What is most important for teachers is to highlight the difference between actual function and perceived affordance.

AI literacy

AI literacy means the ability to understand, use, and critically evaluate artificial intelligence. The concept has been developed in research by scholars such as Long and Magerko (2020), building on earlier work on digital literacy. What is most important for teachers is to build AI literacy among students as a new foundational academic skill.

Assessment validity

Assessment validity refers to whether an examination really measures what it is intended to measure. The concept was strongly established in educational research by Messick (1989). What is most important for teachers is to design assessments that evaluate student understanding rather than AI’s capabilities.

Cognitive partnership

Cognitive partnership describes how AI can function as a thinking partner rather than just a tool. The idea is based on distributed cognition research by Salomon, Perkins, and Globerson (1991). What is most important for teachers is to show students how AI can support creativity and reflection without replacing their own thinking.

Digital agency

Digital agency refers to the user’s self-determination and active ability to act in digital environments. The concept has been discussed by researchers such as Hinostroza (2008) and Ferrari (2013) in relation to digital competence. What is most important for teachers is to train students to make active and conscious decisions about AI use instead of being passive recipients.

Ethical AI

Ethical AI means using AI in accordance with principles of fairness, transparency, responsibility, and human rights. This has been emphasised by Luciano Floridi and in the EU’s ethical guidelines for AI (2019). What is most important for teachers is to show students that ethics must always be part of technology use.

Function

Function is a concept from design and systems theory that refers to what a tool can technically do. In the AI context, it describes the built-in capacities programmed by developers. What is most important for teachers is to distinguish between technical function and pedagogical value.

Human-in-the-loop

Human-in-the-loop means that humans must always have an active role in AI processes. The concept was established in AI and machine learning research in the 1990s and is now a principle of responsibility and quality assurance. What is most important for teachers is to emphasise that AI must never become a final authority without human oversight.

Professional AI-gency

Professional AI-gency is an extension of digital agency, focusing on professional and academic practice. The concept is used in current AI pedagogy research in the 2020s to describe how AI becomes part of professional identity. What is most important for teachers is to prepare students to use AI as part of their future professional roles.

Responsible Research and Innovation (RRI)

Responsible Research and Innovation, often abbreviated RRI, is an EU framework from 2011 that ensures innovation is ethical, inclusive, and sustainable. RRI is widely used as a guideline for policy development and technology research. What is most important for teachers is to view AI as part of broader societal responsibility.

Sustainable AI use

Sustainable AI use refers to employing AI in ways that are pedagogically, ethically, and environmentally sustainable in the long term. The issue has been raised by UNESCO and the UN during the 2020s. What is most important for teachers is to reflect on when AI improves learning and when it risks harming quality or equality.

3. How AI works

AGI (Artificial General Intelligence)

AGI stands for Artificial General Intelligence and refers to a hypothetical form of AI that could perform intellectual tasks at the same level as humans, with general problem-solving and transfer of knowledge between domains. The concept has been discussed since the 1950s by researchers such as John McCarthy and Marvin Minsky but has not yet been realised. What is most important for teachers is to distinguish between today’s narrow AI (such as generative AI and machine learning) and the future vision of AGI, in order to avoid exaggerated expectations or misunderstandings.

Agentic AI

Agentic AI refers to artificial intelligence systems designed with a degree of autonomy, enabling them to act as agents that initiate actions, pursue goals, and adapt to changing contexts. The term is increasingly discussed in connection with UNESCO’s call for “enhancing human agency” in the AI era and the EU’s guidelines on “human agency and oversight” as a key requirement for trustworthy AI. For teachers, the most important takeaway is to distinguish between AI that simply processes input and produces output, and AI that demonstrates agent-like behaviour, which requires stronger oversight, ethical safeguards, and pedagogical reflection about responsibility in learning contexts.

Augmented intelligence

Augmented intelligence is a term popularised by IBM in the 2010s and means that AI should enhance rather than replace human intelligence. It highlights collaboration between humans and machines. What is most important for teachers is to present AI as a support for creativity and judgement rather than as a replacement.

Bias

Bias in AI refers to systematic distortions that arise when models are trained on partial or skewed data. The issue has been discussed in research since the 1980s and highlighted strongly by Crawford and Calo (2016). What is most important for teachers is to show students how bias can influence AI outcomes and encourage critical evaluation.

Explainability

Explainability refers to the ability to understand why AI produces a certain result. The concept was established through DARPA’s Explainable AI programme (2016). What is most important for teachers is to stress that AI often operates as a “black box” and requires critical analysis.

Generative AI

Generative AI refers to systems that can create new content such as text, images, audio, or code. The term was established in the 2010s and popularised in 2022 with ChatGPT and other models. What is most important for teachers is to understand both the potential of generative creation and the risks of fabricated content.

Hallucination

Hallucination refers to when AI fabricates information that appears convincing but is false. The term became established in AI research around 2020 with the spread of language models. What is most important for teachers is to train students always to verify information and sources.

Machine learning

Machine learning is a branch of AI where computers are trained to detect patterns in data. The term was introduced by Arthur Samuel (1959) and underpins most modern AI systems. What is most important for teachers is to explain that AI does not “understand” the world but only works with statistical patterns.

Prompting

Prompting is the practice of formulating effective instructions to AI to obtain relevant results. The concept grew within the AI community in 2021–2022 as language models spread. What is most important for teachers is to train students to ask good questions rather than settle for the first answer.

Traditional AI (rule-based AI)

Traditional AI refers to early forms of artificial intelligence based on rule-based systems and logical decision trees. This type of AI was developed between the 1950s and 1980s by researchers such as John McCarthy and Allen Newell, where humans manually programmed the rules. What is most important for teachers is to understand the difference between traditional AI and today’s machine learning-based or generative AI, as this helps students see why modern systems are more flexible but also harder to explain.

Transparency

Transparency means that AI systems openly disclose their processes and sources. Transparency is a central principle in the EU’s AI Act (2021–2024), but is often limited in commercial tools. What is most important for teachers is to emphasise the importance of transparency for academic credibility.