This paper intends to explore the opportunities and challenges of employee self-directed learning with the rise of generative AI. It examines the possible impacts of generative AI on employee learning from a psychological perspective, based on expectancy theory.
This paper reviews existing literature on the role of AI in employee learning and applies expectancy theory to analyze its potential effects. Through this approach, the article explores how the availability of generative AI may influence employees’ self-directed learning, with a particular emphasis on its impact on motivation to learn explicit versus tacit knowledge.
The study highlights a growing body of research on AI’s influence on employee learning; however, there has been limited focus on its impact on employee motivation for self-directed learning. Based on expectancy theory and previous research, this study argues that the availability of generative AI leads to a decrease in motivation to learn explicit knowledge while increasing the perceived value and motivation of learning tacit knowledge.
This article calls for more attention from scholars to understand the impacts of generative AI on employees’ motivation for self-directed learning, especially in relation to different types of knowledge, beyond the current focus on formal training programs.
To address challenges such as inaccurate firm-specific information from AI, companies should develop programs that provide accurate knowledge and facilitate the learning of verified tacit information. While generative AI can reduce the perceived value of explicit knowledge, organizations should promote strategies to encourage its learning alongside tacit knowledge.
This viewpoint contributes to our understanding of the effects of AI availability on employee motivation for self-directed learning of different types of knowledge. It provides valuable insights for organizations on the challenges and opportunities associated with the increasing use of AI.
