AAAPS: AI-Augmented Academic Performance Simulation (1.0.0)
Agent-based model simulating 60 undergraduate CS students over 8 semesters to investigate how differential AI tool access affects academic inequality, performance distributions, and dependency formation. Implements a three-phase AI dependency mechanism (effort reduction, metacognitive miscalibration, capability erosion) under five policy scenarios with a 4,050-run parameter sensitivity sweep.
Release Notes
Initial release. Implements 60 undergraduate student agents over 8 semesters under 5 AI-access policy scenarios (baseline, free market, universal AI, subsidy, mixed policy). Includes three-phase AI dependency mechanism (effort reduction, metacognitive miscalibration, capability erosion), 4,050-run parameter sensitivity sweep via joblib parallel execution, and full analysis pipeline.
Associated Publications
“Equal Access, Unequal Benefit: An Agent-Based Simulation of AI Tool Access in Higher Education” submitted to JASSS
This is a companion discussion topic for the original entry at https://www.comses.net/codebases/dc1dc57b-9fb3-498e-9abc-98d91920f542/releases/1.0.0/