Dynamic AI-Human Co-Learning in Service Operations: Balancing Knowledge Sampling and Reputational Risks in a Two-Stage Model

Abstract

The rapid adoption of AI in customer-facing services, such as digital livestreaming, promises efficiency gains but introduces profound socio-technical frictions, including algorithm aversion and trust erosion. This paper develops a two-stage dynamic optimization model of AI-human co-learning over a finite horizon, endogenizing knowledge accumulation and algorithmic trust penalties in service delivery. In the first stage, the system determines the initial knowledge allocation to the AI; in the second, it dynamically paces the sampling of human interaction data to balance marginal learning benefits against endogenous reputational costs. We derive closed-form optimal daily sampling intensities and an implicit solution for initial allocation, establishing a threshold condition for the human-AI handover when algorithmic capability surpasses human knowledge. Analytical results demonstrate that an optimal policy strictly front-loads data sampling to capture compounded learning benefits, while quadratic trust penalties mathematically enforce conservative exploitation in later stages. These findings bridge mechanism design and social computing, formalizing the dynamic frictions often overlooked in static automation models. The framework provides actionable guidelines for algorithmic platform governance, enabling designers to calibrate AI initialization, manage user trust trade-offs, and pace data extraction. Extensions incorporating declining market response, heterogeneous data quality, and platform scale effects confirm the model’s robustness in complex socio-technical contexts.