Technology is becoming easier to use, while its effects on learning are becoming harder to judge. At the University of Ljubljana, Faculty of Education, this tension shapes discussion about teacher preparation. Educators need to recognise when digital technology and AI extend learners’ thinking, and when they quietly take over the thinking that produces learning. Whenever AI enters the classroom, the decisive question is what intellectual work remains with the learner.
The Informatics for All reference framework for school is a useful starting point. It notes that digital technology can support and even replace cognitive tasks through automation, and sets the aim that learners use digital tools in a conscious, responsible, confident, competent and creative way. Two of its core areas, Data and information and Responsibility and empowerment, are especially important in an AI-rich classroom: learners need to know how systems work and to judge what those systems ought to do.
DIGITAL FIRST turns this principle into a question of teacher competence. Its research in ten partner countries, competence maps for primary and secondary teachers, a tertiary programme and a continuing professional development framework connect informatics knowledge with pedagogy and transversal competences. It draws on Informatics for All and DigCompEdu, from teaching, assessment and empowering learners to information and media literacy, responsible use and digital problem solving, and identifies AI and other emerging technologies as priorities for continuing professional development.
For teacher education, conscious use can be made concrete through task design. Technology supports thinking when it helps a learner inspect data, test a hypothesis, compare explanations, debug a solution or revise work after feedback. It replaces cognitive work when it defines the problem, selects the evidence, produces the reasoning and delivers the final artefact before the learner has made meaningful choices. The same tool can support or weaken learning, depending on timing, prompts, assessment and the teacher’s decisions.
Data literacy and AI literacy belong together in this judgement. Data literacy means understanding how data are collected, analysed, and used; checking their quality and bias; and protecting privacy. AI literacy adds how AI systems learn from data, what their outputs can and cannot show, and why responsibility remains with people. The 2026 OECD and European Union published Empowering learners for the age of AI: An AI literacy framework for primary and secondary education, which describes four connected domains: Engage with AI, Create with AI, Manage AI, and Shape AI. Manage AI asks learners to divide work intentionally between humans and AI, while Create with AI asks them to maintain human agency.
UNESCO’s AI Competency Framework for Teachers (2024) adds five dimensions: human-centred mindset, ethics of AI, AI foundations and applications, AI pedagogy, and AI for professional learning. The European Commission’s revised Guidelines on the ethical use of artificial intelligence and data in teaching and learning for educators (2026) likewise ask educators to evaluate whether AI applications support learning objectives and to consider human agency and oversight, transparency, fairness, privacy, wellbeing and accountability. These frameworks move teacher competence beyond operating a tool towards professional judgement before, during and after its use.
Recent evidence also argues for caution. Helen Pearson’s 2026 Nature feature, How to stay smart in the age of AI: the science of critical thinking, reports that learning depends on effortful cognitive processing, and that students may outsource the very activities through which scientific thinking develops: formulating hypotheses, interpreting evidence, considering alternatives and working with uncertainty. Studies of generative AI’s effects are still conflicting, but one lesson is emerging: task structure matters. Teacher education can preserve productive effort by asking learners to think before consulting AI, compare its answer with evidence, revise their own reasoning and disclose how the tool contributed.
As AI systems become agentic, able to plan and act across several steps, the boundary around human work matters even more. For higher education, Murekian and Sudarshan (2026) published an article, Navigating agentic AI: a call for reimagined academic literacies, in which they propose an AI literacy centred on critical evaluation, prompt literacy, co-authorship, ethical awareness and recognition of AI limitations, with students documenting how AI was used and where their own input shaped the outcome. Such practices keep assessment focused on knowledge and judgement rather than the polish of an automated output.
The COST Action ConsciousTech adds a wider ethical lens. It understands normative consciousness as the awareness, understanding and internalisation of the legal norms, ethical principles and social expectations that guide behaviour and choices in the tech community. In education, this means responsible use cannot be reduced to a compliance checklist. Teachers and learners need repeated practice in noticing whose values a system embeds, who benefits, who carries the risk, and when declining a technology is the responsible choice.
The Faculty of Education therefore sees initial teacher education and continuing professional development as connected responsibilities. Teachers need opportunities to analyse classroom cases, design and test AI-supported activities, observe what learners actually do, and revise an activity when technology removes the intended reasoning or creative work. Such professional judgement keeps technology in the service of human understanding and agency, with thinking, learning and creativity at the centre.
References:
Caspersen, M. E., Diethelm, I., Gal-Ezer, J., McGettrick, A., Nardelli, E., Passey, D., Rovan, B., & Webb, M. (2022). Informatics reference framework for school. Informatics for All. https://doi.org/10.1145/3592625
DIGITAL FIRST Consortium (2025). D4.1 Informatics teacher education and training in the EU today – research. https://digitalfirstnetwork.eu/wp-content/uploads/2025/09/D4.1-Informatics-teacher-education-and-training-in-the-EU-today-%E2%80%93-research.pdf
DIGITAL FIRST Consortium (2025). D4.2 Competencies map for informatics teachers in primary school. https://digitalfirstnetwork.eu/wp-content/uploads/2025/09/D4.2-Competencies-map-for-informatics-teachers-in-primary-school.pdf
DIGITAL FIRST Consortium (2025). D4.3 Competencies map for informatics teachers in secondary school. https://digitalfirstnetwork.eu/wp-content/uploads/2025/10/D4.3-Competencies-map-for-informatics-teachers-in-secondary-school.pdf
OECD & European Union (2026). Empowering learners for the age of AI: An AI literacy framework for primary and secondary education. OECD Publishing. https://doi.org/10.1787/65cd27d4-en
Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084
European Commission, Directorate-General for Education, Youth, Sport and Culture (2026). Guidelines on the ethical use of artificial intelligence and data in teaching and learning for educators (revised edition). Publications Office of the European Union. https://doi.org/10.2766/7967834
Pearson, H. (2026). How to stay smart in the age of AI: the science of critical thinking. Nature, 657(8133), 873–875. https://doi.org/10.1038/d41586-026-02930-6
Murekian, O., & Sudarshan, S. (2026). Navigating agentic AI: a call for reimagined academic literacies. Journal of Learning Development in Higher Education, 39. https://doi.org/10.47408/jldhe.vi39.1640
COST Association (2026). Memorandum of Understanding for the implementation of COST Action CA25110 Normative Consciousness in the Tech Community (ConsciousTech), COST 024/26. https://www.cost.eu/actions/CA25110/
