The Limits of Predictive Student Risk Models
Predictive student risk models promise timely intervention by identifying students at risk before failure becomes visible. Recent reporting highlights AI driven alerts that flag patterns of attendance, grades, and engagement earlier than ever. Such systems serve an obvious purpose. Fire alarms work because smoke precedes damage. Education, however, does not follow the same logic. Intellectual struggle often precedes understanding. Moral development resists prediction. Purposeful wandering can look indistinguishable from disengagement when reduced to behavioral signals. Risk models detect deviation from expected paths, yet education has never unfolded along a single path. Older academic traditions assumed uneven progress and delayed clarity as part of formation. Early warning systems may help institutions respond faster, but they cannot tell us whether a student is becoming thoughtful, resilient, or wise.
Further Reading
Gregor Reisch, Margarita Philosophica, 1503. Houghton Library, Harvard University. Public domain.