Gideon Dishon

Purpose – Recent policy reports and scholarly research highlight the potential of generative artificial intelligence (GenAI) to advance equity in education by improving access, personalizing learning, and addressing disparities. Yet past technological interventions have often fallen short of these ambitions and have sometimes reinforced existing inequalities. This study aims to examine how the distinctive affordances of GenAI interact with the characteristics of education and asks how the pursuit of equity should be conceptualized in light of these new conditions. Design/methodology/approach – This study offers a theoretical and conceptual analysis of the connection between GenAI and educational equity. Using complexity theory, it critically reviews the assumptions behind previous technology-driven equity efforts and investigates how GenAI could be used to differently influence learning systems, educational practices, and institutional dynamics. Findings – The analysis indicates that realizing GenAI’s potential to promote educational equity requires broadening the ideals that currently guide its integration in two ways. GenAI’s flexibility and capacity to support context-sensitive responses across different levels of the educational system should shift attention from personalization to system-level responsiveness, and from closing achievement gaps to enabling diverse learning pathways. Equity is thus understood not only as equalizing outcomes but also as addressing varied needs, values, and developmental trajectories at all levels of the educational system. Originality/value – This study relies on complexity theory to offer a novel conceptual framework for understanding how GenAI’s unique features could support (or impede) educational equity. It moves beyond gap-closing models, centered on personalization, to focus on GenAI’s potential impact on system-level responsiveness, articulating a more dynamic, system-oriented approach.