Quantifying Maladaptive Cognitive Offloading: A Behavioral Framework for AI-Supported Decision Making Under Complexity

The integration of AI tools into professional knowledge work has created a critical measurement gap: organizations track AI adoption but cannot distinguish adaptive cognitive support from maladaptive dependency, a distinction with direct implications for how professionals adapt their decision-making processes under complex and time-pressured conditions. We ask whether maladaptive cognitive offloading can be measured from behavioral traces, and which signals matter most. We introduce COB-ML, a behavioral framework combining a six-layer feature architecture with a three-branch stacking ensemble of language-model, tabular, and sequential models. Across five datasets (70,673 survey records, 1,837,989 AI conversation logs, and 69,362 physiological windows), behavioral features outperformed an occupation-only baseline by ΔF1=+0.200 (best tabular model). The ensemble reached a Macro-F1 of 0.762 under a cross-dataset protocol that is in-domain for its language-model branch, and an XGBoost branch showed ICC(A,1)=0.611 against WESAD stress labels, preliminary and indirect evidence only. SHAP analysis associates trust disposition and verification behavior, rather than usage frequency, with maladaptive offloading. Monitoring adoption alone may therefore miss the strongest signals of cognitive dependency risk, and COB-ML offers a transparent starting point for measuring them.