The Feminist AI Education (FemAIEdu) project investigates how critical feminist approaches to AI and data studies can transform the foundations of AI education. Rather than focusing solely on technical proficiency or ethical compliance, we examine how AI is taught and learned within higher education and research contexts as a site where knowledge, power, and social values are actively constructed and encoded into systems.
Across universities, AI education often treats issues such as fairness, bias, and ethics as add-on modules or matters of individual responsibility. This framing reduces systemic problems, such as exclusion, exploitation, and invisibility, to technical errors or isolated misjudgments, obscuring their deeper historical and institutional roots. As a result, students are trained to "fix bias" without questioning why certain harms, silences, or asymmetries emerge in the first place.
This project challenges that paradigm by asking: What would AI education look like if it began from feminist understandings of power, care, and accountability? Through a collaboration between KTH Royal Institute of Technology, Stockholm University (SU), and University of Manchester (UoM), we will co-develop and pilot feminist pedagogical approaches and teaching materials. These will support students in identifying and interrogating the structural conditions that produce harm and silence in AI, focusing on whose perspectives and experiences are excluded, overlooked, or marginalized, rather than only addressing their surface-level manifestations.
Bringing together feminist theory, critical data studies, and computing education research, the FemAIEdu project will develop a replicable framework for Feminist Pedagogies for Just and Accountable AI. We will design, prototype, and evaluate interdisciplinary teaching interventions, including counterdata mapping, participatory audits, and critical model storytelling. These methods aim to reveal how AI systems embed assumptions about whose knowledge counts and whose experiences remain invisible.
By foregrounding structural awareness, epistemic justice, and collective responsibility, the project lays the groundwork for a new generation of AI education; one that equips students not only to build systems, but to critically understand and reshape the conditions under which those systems are imagined and deployed.