Fostering Self-Directed Growth with Generative AI: Toward a New Learning Analytics Framework
Abstract
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.
Cite
@article{arxiv.2504.20851,
title = {Fostering Self-Directed Growth with Generative AI: Toward a New Learning Analytics Framework},
author = {Qianrun Mao},
journal= {arXiv preprint arXiv:2504.20851},
year = {2025}
}