AI in Education and Learning: Perspectives on the Education Ecosystem

AI impacts individual students’ learning, schools and higher education institutions, curriculum design, and national and international educational policy-making. However, the holistic picture has been difficult to draw. Therefore, this chapter aims to analyze how AI is influencing teaching and learning at different levels of the educational ecosystem. The study is literature-based and focuses on the following questions: What does the latest education research literature describe as major findings when AI has been applied in teaching and learning? and How does AI impact different levels of the educational ecosystem and what kind of development is to make AI a meaningful tool in education and learning? We have a lot of evidence that AI has affected positive outcomes in cognitive learning, increased students’ motivation and engagement in learning, and helped students with socio-emotional challenges by increasing self-confidence and encouraging students with supportive feedback. Conversational AI tools have provided new opportunities for dynamic dialogue through generative AI. We also have many AI tools for teachers’ work in classrooms and for their development. The curriculum process and AI literacy have taken the first steps both in schools and higher education. Despite these positive observations, many challenges still exist. Most of them are related to the whole ecosystem. Micro, meso, and macro levels are working separately, and there is a lack of connections between and within levels. The big challenge is also that connections between the educational ecosystem and technological providers need to be stronger and functioning. In the discussion the recommendations for the future are presented: Overcoming the distance between policy and practice, more co-creation between technological designers, researchers, and practitioners, more focus on long-term impact, and clarification of ethical issues throughout the educational ecosystem.