Algorithmic Governance in Lifelong Learning Systems: Artificial Intelligence, Educational Policy Transformation, and Human Development - Kamal Kunwar (Artificial Intelligence in Lifelong and Life-Course Education, 2026)
Purpose – This study aims to address the theoretical gap between artificial intelligence in education and digital governance by explaining how algorithmic decision systems reshape governance structures in lifelong learning. While existing studies often examine AI in education or governance independently, limited attention has been given to how algorithmic governance influences education policy, institutional decision-making, and human development across the life course. Therefore, this study proposes a conceptual framework to explain the role of AI-driven governance mechanisms in mediating policy processes and optimizing learning systems.Design/methods/approach – This research employs a conceptual and critical analytical approach by synthesizing interdisciplinary literature related to artificial intelligence governance, education policy, and human development. Through systematic conceptual analysis, the study develops the Algorithmic Lifelong Learning Governance Model (ALLGM) as a theoretical framework to explain the interaction between algorithmic systems, educational governance, and lifelong learning policy implementation.Findings – The analysis identifies three central governance mechanisms within the proposed model: algorithmic policy mediation, predictive learning governance, and data-driven human development optimization. These mechanisms demonstrate how AI-driven systems can transform policy implementation processes, enable personalized learning pathways, and influence institutional decision-making within lifelong learning ecosystems. Research implications/limitations – As a conceptual study, the framework has not yet been empirically validated through real-world educational governance data or institutional case studies. Therefore, the generalizability of the model remains limited. Future research is required to empirically test the ALLGM framework across different educational systems and governance contexts to assess its practical applicability.Originality/value – This study contributes to the emerging field of algorithmic governance by integrating digital governance theory with lifelong learning policy analysis. The proposed model offers a novel theoretical perspective on how AI-driven decision systems reshape educational governance and highlights the importance of democratic accountability, ethical oversight, and inclusive policy design in AI-enabled lifelong learning environments.