The development of artificial intelligence has far-reaching effects on the skills needed in different areas of society and on the structures of working life, and thus also on schools and education. Throughout history, new technologies have shaped society and economic structures, such as the printing press, the steam engine, electricity or semiconductor technology. They enabled the transition from an agricultural society to an industrial society and later to an information society and digitalisation. While these technologies have had a major impact on production and logistics, as well as on communication and the economy, none of them has really challenged our traditional conception of knowledge and knowing. That said, the printing press, the internet and digitalisation have indeed significantly increased the amount of information and our access to it.
The development of artificial intelligence challenges us and our understanding of knowledge and knowing in a whole new way. Generative AI constructs and represents information in a very human-like way, even though it is based on Large Language Models (LLMs), which, in effect, is probabilistic computation. However, it is incredible how well language models produce information, both verbal and visual. The key question is: what kind of information or knowledge will learners need in society or workplaces in the future? How will teaching in schools have to change to enable learners to acquire the skills required in this age of artificial intelligence?
“Can we still tell what is real and what is created by artificial intelligence? Do we have the means to distinguish the truth from the untruth?”
The conception of knowledge describes our understanding of the origin of knowledge, its nature, permanence, variability, and how knowledge is created or produced. We are accustomed to treating knowledge as a well-founded true belief, often modelled on reality and produced by humans. The prevailing constructivist view of knowledge emphasises the cumulative nature of knowledge, either as the product of an individual or as the result of collaborative knowledge construction. Socio-cultural artefacts such as language or mathematics are central to the production of knowledge. Knowledge is thought to be the result of human cognitive processes, of thinking.
However, although AI handles, processes and produces information, it does not have human-like understanding or awareness. AI cannot create meaning or interpret information in a human way, although generative AI does an excellent job of mimicking human-like behaviour in its outputs. Nevertheless, the information produced by AI is always based on machine learning models, algorithms and underlying data. AI does not have the human conscience or capability for ethical or moral reasoning; nor the conscience and capability for emotion and empathy. The responsibility for knowing – for using and interpreting the information generated by AI – lies with the human, the AI user.
Pedagogical change
The change in the conception of knowledge is inevitably followed by a pedagogical change, which must consider the new need for knowledge and skills, for knowing. The constructivist conception of knowledge is strongly reflected in the mainstream of school pedagogy; the constructivist conception of learning, which sees learning as the result of either the individual or the collaborative construction of knowledge by learners.
However, in the age of AI, a new kind of epistemic flexibility (i) is needed, which can be defined as a flexible way of combining different ways of knowing, thinking strategies and various types of knowledge (such as that produced by AI), always in a way that is appropriate to the situation. Epistemic flexibility is closely related to metacognitive skills, i.e. the ability to monitor, control and reflect one’s own thinking.
Learning needs to be viewed in a new light with the development of artificial intelligence (ii)
Surface learning
- Goal: Recalling facts
- Outcome: Capability to apply information only in a narrow context, if at all
- Methods: Information acquisition
- Focus: Facts
Deep learning
- Goal: Understanding
- Outcome: Capability to apply knowledge in various situations
- Methods: Collaborative knowledge construction
- Focus: Knowledge
Future learning
- Goal: Creating new solutions
- Outcome: Capability to create new solutions for various new situations
- Methods: Co-creation and co-innovation
- Focus: Thinking skills and strategies, as well as innovation practices
In the near term, AI will not eliminate the need to learn e.g. reading, arithmetic or the core concepts of different subjects. However, it will change the kind of knowledge construction and thinking skills learners need. For example, learners will need computational thinking (iii) and data processing skills as prerequisites for understanding AI and how it works. These skills are necessary to understand automated decision-making and the use of machine learning in areas such as social media, hybrid influencing, internet search services or banking services. Computational thinking relies heavily on abstract reasoning, linguistic problem solving and mathematical reasoning.
Artificial intelligence in schools
A school’s ability to respond to the changes in a digitalised society is determined, among other things, by the education system’s conception of technology (the concept and understanding of artificial intelligence), the conception of learning, teaching practices and up-to-date know-how. By developing these elements, schools and education can navigate the age of AI and ensure that learners have up-to-date skills relevant to society and working life.
In schools, AI cannot only be treated as a new technology, but as a force affecting democracy and freedom of expression in societies. It should be seen as a tool for automated censorship, hybrid influence, and information production. A key challenge for schools is to equip learners with critical thinking, problem-solving, source criticism and creativity skills related to the use of AI. These skills are a prerequisite for active participation and ensuring freedom of expression and democracy. In addition, the ethics of AI and the ethical use of technology must be a central focus in school curriculum.
“AI is an innovation in itself, but above all, AI is a platform for new innovations.”
The use of AI in education requires that its users understand what AI is, what kinds of errors AI can make, and what types of biases AI outputs can have. It should also be noted that AI applications are not isolated from cultural and historical contexts, values and other contextual factors. Currently, AI applications are primarily based on the commercial development work of large multinational corporations, the data they use, and the computing capacity of the cloud servers they provide. AI applications rarely consider the unique characteristics of small, less widely spoken languages or their cultural contexts.
Read the entire AI Guide for Teachers here.
References
(i) Markauskaite, L., & Goodyear, P. (2017). Epistemic fluency: Innovation, knowledgeable action and actionable knowledge. Springer Dordrecht.
(ii) Silander, P., Riikonen, S., Seitamaa-Hakkarainen, P., & Hakkarainen, K. (2022). Learning Computational Thinking in Phenomena-Based Co-creation Projects: Perspectives from Finland. In Computational thinking education in K-12: Artificial intelligence literacy and physical computing (pp. 103-119). MIT press.
(iii) Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 366(1881), 3717.
Read the entire AI Guide for Teachers here.
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