The effective use of artificial intelligence in learning environments in pre-primary and primary education

The use of artificial intelligence (AI) in learning environments in pre-primary and primary education is rapidly growing. However, there is limited evidence on how AI can challenge and improve learning outcomes, enhance the teaching techniques and support the teaching profession and how automating administrative tasks helps teachers focus on pedagogy and teaching challenges. Most existing research is from extra-EU contexts, which significantly limits the transferability of findings to EU education and training systems and is often conducted on a limited number of participants and covering short-time spans, hindering the possibility of generalising its findings. Moreover, research on the implications of AI tools for equity and inclusion in education is lacking. There is also a need to better investigate the challenges that AI poses to teaching and learning as well as pupils’ well-being. AI is developed to increase human efficiency yet also comes with often unexpected risks and challenges, especially when being applied to child audiences. The research (including through SSH disciplines) could explore how AI can enhance curricula, improve teaching methods, and personalise learning through adaptive technologies. It should also consider the developmental appropriateness of AI tools for young learners, focusing on cognitive, emotional, and social growth. Research on AI’s impact on early childhood education and its role in supporting foundational skills would be valuable. Additionally, the research should emphasise the aspect of teacher support, including how AI can assist with lesson planning, assessment, and feedback. It should also explore training programs to help educators use AI effectively. Finally, inclusivity and accessibility are key angles to consider, ensuring AI tools are suitable for all students, including those with disabilities or from diverse backgrounds, and promoting multilingual and multicultural education. Longitudinal studies are also needed, as current research is fragmented and looking at the short term. Proposals should focus on the need to understand long-term cognitive, social, and emotional impacts, which requires sustained, multi-year investigations. Furthermore, the role of media and digital tools requires attention. Proposals should consider the devices and platforms through which children encounter AI (for example robots, tablets, smartphones etc) and how these shape interactions and learning outcomes. Relevant research questions to address include: How can AI-powered tools personalise learning experiences safely and effectively and support teachers in addressing diverse student needs? What are the potential benefits and challenges of integrating AI into initial teacher education and continuous professional development programmes? How can AI assist teachers in automating administrative tasks, allowing them to focus more on pedagogy and student engagement? What ethical considerations and potential risks, including in relation to wellbeing, should be taken into account when using AI in education to support teachers and students? What is the typical profile of teachers who are currently using AI-powered tools and which uses and perceived gratifications motivate using AI-powered tool? How can AI tools motivate children to learn? How can AI-based interventions address learning gaps based on individual students’ needs (including disabilities) and learning styles? What changes do educational programmes need to perform on a pedagogical level for the successful integration of AI? What do teachers need to implement the use of AI safely and effectively? Proposals can apply a variety of methods including rigorous experimental and/or quasi-experimental methods, but also experience sampling and general survey methods, to evaluate the effectiveness of existing AI tools or to test new AI tools. Proposals should evaluate the costs associated with the policy measures or programmes analysed. Proposals should look at different forms of AI tools, e.g. Generative AI, Intelligent Tutoring Systems etc. Proposals should also apply qualitative research methods to cover the ethical dimensions. Interdisciplinary approaches and close cooperation with educational institutions and educators are essential. Cooperation with media literacy organisations is encouraged. Clustering and cooperation among selected projects under this call topic and other relevant projects are strongly encouraged. Particular attention should be paid to identifying and mitigating biases in AI systems to ensure equity and inclusion in education systems.