Organizational Learning Research in the Context of Artificial Intelligence: Literature Review and Future Directions

How organizations adapt to rapidly changing and dynamic environments through continuous learning is considered central to organizational learning theory.Organizational learning has been driven by human experience and social interaction,facilitating knowledge creation,transfer,and application.However,the introduction of artificial intelligence (AI) is redefining the premises and mechanisms of learning,influencing traditional theoretical frameworks,and introducing complexities in multi-level interactions.These changes are primarily reflected in three areas:the expansion of learning actors,the restructuring of learning mechanisms,and the governance of learning outcomes.Although human-machine interactive learning is reshaping the paradigm of organizational learning,there is still a lack of a systematic understanding of the evolution of organizational learning research under the impact of AI.Thus,this study aims to comprehensively review the evolution of organizational learning research in the context of AI. Specifically,this study conducts a systematic review of 74 key articles on organizational learning in the context of AI.Using the “individual-group-organization” framework of organizational learning as its foundation,the study conducts a review across four key themes:① individual-level learning in the AI context; ②group-level learning in the AI context; ③organizational-level learning in the AI context; ④ multi-level interactive learning in the AI context.Individual-level learning focuses on how a single individual modifies their cognitive structures and behavioral patterns through intuition and interpretation,with its core centered on knowledge construction at the individual level.Group-level learning refers to the process in which multiple individuals engage in collaborative learning within a group,emphasizing the interpretation and integration of individual knowledge within the team.Organizational-level learning concerns the integration and institutionalization processes across teams,focusing on how knowledge extends from the team level to the entire organization.Multi-level interactive learning examines the flow and interaction of knowledge across individual,group,and organizational levels,emphasizing how feed-forward and feedback processes shape learning at different levels. The findings reveal several critical points.Specifically,at the individual level,AI enhances intuition,supports interpretive reasoning,and promotes action reflection,improving decision-making and adaptability in complex environments.However,it may also lead to negative consequences such as over-reliance on technology and a decline in autonomous learning capabilities.At the group level,AI facilitates the building of cognitive consensus,enhances collaboration efficiency,and improves decision-making effectiveness,but it may also reduce the diversity of knowledge within the group.At the organizational level,AI restructures digital memory mechanisms,optimizes knowledge management,and drives cross-boundary learning through data,enhancing organizational adaptability and stakeholder engagement,while also introducing potential ethical risks.In terms of multi-level interactive learning,AI accelerates the speed and breadth of knowledge flow both internally and externally,breaking down the knowledge barriers between organizations and ecosystems,and making multi-level interactive learning within organizations more dynamic,complex,and competitive.Building on this,this study constructs a research framework for organizational learning in the context of artificial intelligence,revealing the systematic evolution of organizational learning research under the impact of AI.First,AI is identified as a catalyst for changes in learning contexts,driving individuals,groups,and organizations to adopt integration pathways.Second,the integration of AI is observed to reshape adaptive actions in human-AI collaboration,with these actions influenced by technological,organizational,and environmental factors.Finally,the outcomes of these adaptive actions reinforce organizational systems through feedback mechanisms,enabling coevolution between AI and organizational systems,driving firm’s adaptive innovation and growth,and forming a self-reinforcing cycle of coordinated evolution. This study identifies research gaps and proposes four future research directions:①analyzing the multidimensional interactions of individual learning in the AI context,focusing on complex tasks,cross-boundary integration,and long-term effects.For example,the cognitive reshaping mechanism of AI in key managerial learning,the potential exploration of AI in individual cross-domain knowledge integration,and the negative effects of long-term interaction with AI on individual learning.②Deconstructing the psychological processes of group learning in the AI context,including trust building,role perception,and collaboration structures.For example,the trust mechanism of shared cognition in teams under AI contexts,AI-driven role cognition and dynamic task allocation models in teams,and the impact of team structure optimization on fostering diverse perspectives and collective intelligence.③Uncovering the dynamic adjustments of organizational learning in the AI context,such as memory updates,knowledge integration,and contextual adaptation.For example,the systematic management of organizational digital memory in AI contexts,the integration mechanism of AI knowledge and human knowledge within organizations,and the multi-context adaptation model of data-driven learning.④Constructing the co-evolution mechanism between AI capabilities and multi-level learning,emphasizing influence pathways,capability emergence,and governance frameworks.For example,the influence pathways of multilevel organizational learning on AI capabilities,the emergent capabilities and strategic consequences of AI in interaction with multilevel learning,and the critical role of AI governance in the co-evolution process. This study has three contributions:First,it reveals the transformations in organizational learning under the AI context,encompassing changes in learning contexts,processes,and outcomes.These include the disruptive impact of AI on learning contexts,the expansion of feedforward and feedback mechanisms,and the adaptive evolution of human-AI collaboration,thereby extending the applicability and developmental scope of organizational learning theory in the AI era.Second,it systematically reviews and synthesizes the core themes and limitations of existing research on organizational learning in the AI context,constructing a comprehensive research foundation and theoretical framework.Third,it addresses the major gaps in current studies by proposing future research directions,offering clear pathways and guidance for advancing organizational learning research in the AI era.