New Study Proposes Courageous Minority Theory for School Change in AI Era

Shanghai, China—A new analysis by University of Kansas scholar Yong Zhao argues that schools need more than new policies or AI add-ons. In “Obsolete Schooling and the Courageous Minority: Rethinking Educational Change in the Age of Artificial Intelligence,” Zhao contends that AI is mainly revealing a deeper educational mismatch: many routines and success metrics no longer match what students need—or what school is supposed to achieve.

The article points out that AI systems can already perform tasks traditionally assigned to students, including drafting essays, answering routine questions, summarizing texts, and producing work that looks “school-appropriate.” As these outputs become easier to generate, the value of assignments that primarily reward compliance declines. In Zhao’s view, the core problem is not that AI exists, but that schooling is built around measurable performances that AI can mimic.

Zhao critiques typical reform strategies—tool bans, stricter rules, misuse detection, and incremental AI guidance—because they preserve what he calls the “grammar” of schooling. Instead of transforming learning, these approaches often change the interface while leaving in place age-graded classes, standardized curricula, subject silos, rankings, and uniform assessments.

To explain why change stalls, the paper uses the metaphor of school as a “peace treaty.” Schools are social settlements among students, families, teachers, leaders, universities, policymakers, and communities. Grades, schedules, credentials, and familiar classroom routines stabilize competing interests. When reforms try to rewrite everything at once, they threaten too many stakeholders simultaneously, leading to resistance or diluted implementation.

As an alternative theory, Zhao proposes the “courageous minority.” Rather than waiting for system-wide consensus, small groups inside schools—teachers, students, leaders, families, and partners—can design protected learning environments such as advisory programs, exhibitions, capstones, interdisciplinary projects, or “schools within a school.”

Zhao supports this approach with panarchy theory: complex systems evolve through semi-autonomous experiments embedded within larger structures. Small-scale initiatives can develop credible alternatives, attract allies, and gradually shift norms and expectations before policy catches up.

The article emphasizes urgency. Many students are already experiencing AI while assessments still reward machine-like performance over human contribution. Even rapid top-down policy would arrive too late for students moving through current systems, making experimentation a necessity rather than a luxury.

For teachers, the paper recommends designing instruction around meaningful problems, student agency, long-term inquiry, iterative revision, and public contribution. For leaders, it argues that transformation requires protecting space for experimentation rather than deploying yet another uniform reform package.

Across a global network of roughly 20 schools testing locally grounded experiments—including learner-agency days, student podcasts, AI-supported inquiry, problem-finding initiatives, innovation groups, and capstone-centered models—Zhao concludes that education’s future may hinge less on the newest technology and more on whether educators and students build schooling worth doing.