Using Augmentation-Based AI Tool at Work: A Daily Investigation of Learning-Based Benefit and Challenge - Shao, Yiduo; Huang, Chengquan; Song, Yifan; Wang, Mo; Song, Young Ho; Shao, Ruodan (Journal of management, 2025)
Augmentation-based artificial intelligence (AI) artifacts are increasingly being incorporated into the workplace. The coupling of employees and AI tools, given their complementary strengths, expands and expedites employees’ access to information and affords important learning opportunities. However, existing research has yet to fully understand the learning-based benefits and challenges for employees in augmentation. Integrating insights from AI augmentation literature and cognitive load theory, we conducted a daily diary study to understand employees’ experience using augmentation-based AI at work on a daily basis. We theorized and found that, on the one hand, frequent usage of augmentation-based AI during a workday was associated with greater knowledge gain and subsequently better task performance at the end of the workday. On the other hand, using augmentation-based AI frequently also led employees to experience information overload, which in turn impaired their performance and recovery at the end of the workday. In addition to elucidating the countervailing mechanisms, we identified employee openness to experience as a dispositional factor, and positive affect as a momentary state that shaped the effects of using augmentation-based AI over the workday. Our research has implications for understanding AI augmentation dynamics from a learning-based perspective, as well as AI’s impact on employees at large.