The Evolution of Research on AI and Education Across Four Decades: Insights from the AIxEd Framework

Abstract This paper presents a new framework (AI $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> Ed) to categorize the various kinds of relationships between artificial intelligence (AI) and education in terms of two axes. Using this framework, we examine the evolution of the field of Artificial Intelligence in Education over four decades by examining papers published in AIED proceedings (1985, 1993, 2021, and 2024) and the International Journal of Artificial Intelligence in Education (2004, 2014, and 2021). We argue that AI’s role in education extends beyond its use as a practical tool for solving educational problems. AI also serves as a conceptual analogy for understanding human intelligence and learning. However, we show that this way of thinking about AI and education, which was once prevalent, has received much less focus in recent years. We suggest that the growing enthusiasm among researchers for using generative AI, as evidenced by papers in AIED 2024, offers opportunities to deepen our insights into student knowledge and learning processes. Finally, we propose new directions for future AIED research that span the different kinds of research in AI $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> Ed.