AI Learning Intention, Engagement Dynamics, and Behavioral Consequences: A Critical Re-examination

Abstract

Purpose: While much scholarship evaluates learning effectiveness within conventional training environments, limited attention has been given to how AI-centered learning ecosystems reshape learner agency and outcomes. This study reconceptualizes the connection between AI-driven learning systems and employee behavioral consequences. Rather than assuming direct positive outcomes, this research critically examines whether AI-learning intention genuinely translates into meaningful behavioral transformation. Drawing from the Theory of Planned Behavior (TPB) and Self-Determination Theory (SDT), the study reassesses the relationships among learning intention, engagement processes, and behavioral outcomes.
Design/Methodology/Approach: Using survey data collected from Indian industry professionals, the study applies structural equation modeling (SPSS-AMOS 27) and Hayes PROCESS macro to test direct and indirect relationships between AI learning intention, engagement patterns, and behavioral outcomes.
Findings: Results indicate that although AI learning intention factors (attitude, subjective norm, perceived behavioral control) show statistical associations with behavioral outcomes, the magnitude of actual behavioral transformation depends heavily on the depth and quality of engagement. Engagement does not merely mediate but conditions the extent to which AI intention results in observable workplace change.
Originality/Value: This study challenges overly optimistic assumptions regarding AI-based learning systems and emphasizes the conditional nature of behavioral outcomes in technology- driven learning environments.