Measuring and Mitigating Competency Erosion

This chapter establishes a comprehensive research program for investigating AI-induced competency erosion through neurobiologically grounded measurement frameworks and mathematical modeling approaches. Building on competency erosion patterns identified in Chap. 5 , we present detailed neurobiological mechanisms underlying expertise acquisition, maintenance, and decay, demonstrating how “use-it-or-lose-it” principles in neural systems create measurable vulnerabilities during AI adoption. We develop mathematical models linking individual exponential decay patterns to organizational S-curve dynamics, grounded in established neuroscience research on synaptic plasticity, myelination, and neural competition. The chapter introduces the ACEI as a proposed research framework rather than a validated measurement tool, positioning it as a systematic agenda for investigating competency erosion across professional domains. The ACEI employs a novel ratio-based formulation that models AI dependency as a multiplicative degradation factor rather than using traditional weighted averaging approaches, reflecting neurobiological evidence of cognitive interference and competitive neural processing. Through empirical examples calibrated against documented erosion patterns in medical and legal education, we demonstrate the framework’s practical application while acknowledging the substantial validation research required. The chapter establishes a multi-phase research program spanning foundational validation studies, longitudinal tracking investigations, and applied implementation research. Strategic interventions at individual, organizational, and educational levels are examined as approaches for preserving critical human capabilities while leveraging AI’s productivity benefits. This work contributes the first systematic framework for measuring AI-induced competency erosion, establishing both theoretical foundations and practical research pathways for maintaining cognitive sustainability in increasingly AI-augmented professional environments. Rather than presenting finished measurement tools, we propose a comprehensive research paradigm that can guide empirical investigation of this emerging phenomenon while preserving human intellectual capital alongside technological advancement.