THE ECONOMICS OF STUDENT TIME IN THE AI ERA: MODELING STUDY OPTIMIZATION FOR STUDENT ARCHETYPES

Porto Massimiliano

Abstract


This paper develops a microeconomic framework for analyzing how students optimally allocate study time across academic disciplines under heterogeneous cognitive constraints and evolving technological environments. By embedding physiological frictions—within-session study fatigue (α_i ) and non-discretionary background stress (S^σ )—into a two-good production setting, the model generates a strictly concave Study Possibilities Frontier (SPF) consistent with empirical patterns of diminishing cognitive returns. Preferences and difficulty parameters jointly determine the student’s optimal study mix, yielding closed-form interior solutions for most technological specifications. The model is calibrated to four archetypal student profiles defined along dimensions of cognitive efficiency and academic motivation. Simulation results show that general technology uniformly expands the SPF but interacts asymmetrically with student heterogeneity. An autonomous output floor (TECH2) provides the strongest welfare gains for high-fatigue or low-engagement students, while multiplicative productivity tools (TECH3) benefit high-aptitude students once baseline efficiency is sufficiently high. Domain-specific artificial intelligence further mitigates the opportunity cost of difficult subjects, though misuse that substitutes for active problem-solving can induce cognitive depreciation. Overall, the framework demonstrates that educational interventions are not one-size-fits-all. As artificial intelligence and automation reshape academic environments, institutions must transition from passive mass instruction to targeted, structurally informed pedagogical strategies that align with the heterogeneous friction profiles of the student population.

JEL: A22, I21, O33, C61


Keywords


opportunity cost, study possibilities frontier, AI-assisted learning, student archetypes, cognitive fatigue, optimization

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DOI: http://dx.doi.org/10.46827/ejes.v13i9.6976

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