Fostering Self-Directed Growth with Generative AI: Toward a New Learning Analytics Framework
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915265876328448 |
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| author | Mao, Qianrun |
| author_facet | Mao, Qianrun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20851 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fostering Self-Directed Growth with Generative AI: Toward a New Learning Analytics Framework Mao, Qianrun Computers and Society Artificial Intelligence 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. |
| title | Fostering Self-Directed Growth with Generative AI: Toward a New Learning Analytics Framework |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2504.20851 |