AI augments instructor feedback in economics education but systemic inequities persist without structural reform
Original framing: “AI helps instructors give better feedback but can't replace them, trial suggests” — Phys.org
The overreliance on AI in education mirrors historical patterns of technological solutionism, from the introduction of standardized testing to the rise of MOOCs, all of which promised efficiency but deepened inequalities. The adjunctification of faculty labor, a trend accelerating since the 1970s, creates the conditions where AI is even considered as a solution to overburdened instructors. The trial’s focus on economics—a discipline that has long been complicit in neoliberal education reforms—further underscores this historical continuity.
The University of Michigan trial reveals a paradox: AI can enhance feedback when used as a tool, but its integration into education is shaped by deeper systemic failures, particularly the adjunctification of faculty labor and the neoliberal redefinition of education as a marketable service.
The study’s narrow focus on technical efficiency obscures how AI adoption perpetuates inequalities, from the racialized and gendered distribution of grading labor to the erasure of Indigenous and Global South pedagogies. A systemic solution requires reimagining education as a public good, where technology serves—not replaces—human relationships, and where marginalized voices shape the systems that govern their learning. This demands not just technical fixes but a cultural shift: from viewing AI as a silver bullet to embracing it as one tool among many in a broader movement for educational justice, rooted in the wisdom of diverse traditions and the collective labor of students and educators alike.