Description
Physics education is, at its core, an education in systems. It trains students to model the world as structures governed by definite rules, and this systemic mindset is among the most powerful and transferable habits that physics instils. Artificial intelligence, by the OECD definition and in its computational foundations, is precisely such a system, built, trained, and operated through data and algorithms. This research argues that physics education therefore carries a distinctive responsibility as AI becomes embedded in learning and society. It must help students draw a clear and principled line between what can be captured through system design and what belongs to subjective experience. The concern is practical as well as philosophical. If educators or students come to believe that artificial intelligence fully replicates the human mind, this distorts how they understand knowledge, observation, and their own role as inquirers, and it quietly erodes the human-centred foundation on which education rests. Drawing on the philosophy of mind, cognitive science, and the foundations of computation, this research positions first-person subjective experience, including perception, attention, and cognition, as an irreducible dimension of human understanding that systematic computation can emulate but not replicate. It contends that fundamental physics, with its long engagement with observation, measurement, and the place of the observer, is uniquely suited to teach this distinction with rigour rather than sentiment. The research proposes how educators can foreground systems thinking and the limits of systems together, cultivating epistemological awareness, critical reflection, and intellectual humility. It concludes that by clearly distinguishing system design from subjective experience, physics education can establish a vital foundation for a genuinely human-centred education system, one that prepares students to use artificial intelligence wisely without mistaking it for the mind.
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