From algorithmic dependency to reflective partnership: Reframing AI as a strategic learning tool in the workplace

Purpose The study explored algorithmic dependency and introduced the Reflective AI Partnership (RAP) model for organizations to apply during AI integration in their learning environment. Design/methodology/approach The study employs Schon’s reflective practitioner theory and cognitive offloading theory together with emerging AI in learning literature to conduct a conceptual analysis. Findings Algorithmic dependency may, in certain cases, reduce reflective learning if left unchecked, as outlined in six propositions. A constructive, actionable RAP model is proposed to facilitate organizational learning. Research limitations/implications Empirical validation is needed, as this is a conceptual framework. For cross-sectional experimental studies, a validated algorithmic dependency scale can be used. Practical implications HR practitioners, L&D leaders, and management can use the RAP model to develop AI literacy and rethink how to work with AI. Originality/value The study proposes the concept of algorithmic dependency as a theoretical construct in a distinct and measurable manner.