Fostering Self-Directed Growth with Generative AI: Toward a New Learning Analytics Framework
It aims to develop equitable, adaptive, and sustainable learning systems for learners in the digital era, though it appears incremental as it builds on existing concepts.
This study introduces the A2PL framework that integrates Generative AI with Learning Analytics to cultivate Self-Directed Growth in learners, addressing gaps in current research on AI-mediated education and Self-Directed Learning.
In an era increasingly shaped by decentralized knowledge ecosystems and pervasive AI technologies, fostering sustainable learner agency has become a critical educational imperative. This study introduces a novel conceptual framework integrating Generative Artificial Intelligence and Learning Analytics to cultivate Self-Directed Growth, a dynamic competency that enables learners to iteratively drive their own developmental pathways across diverse contexts.Building upon critical gaps in current research on Self Directed Learning and AI-mediated education, the proposed Aspire to Potentials for Learners (A2PL) model reconceptualizes the interplay of learner aspirations, complex thinking, and summative self-assessment within GAI supported environments.Methodological implications for future intervention design and learning analytics applications are discussed, positioning Self-Directed Growth as a pivotal axis for developing equitable, adaptive, and sustainable learning systems in the digital era.