用生成式AI构建学习者自主成长的新分析框架
Fostering Self-Directed Growth with Generative AI: Toward a New Learning Analytics Framework
- 提出A2PL模型，融合生成式AI与学习分析，支持学习者自我驱动发展
- 强调目标设定、复杂思维与自我评估在AI辅助环境中的动态互动
- 适合关注AI教育应用与学习者自主性的研究者和实践者
在去中心化知识生态与普遍存在的AI技术时代，培养可持续的学习者自主性已成为关键教育使命。本研究提出一个整合生成式人工智能与学习分析的新型概念框架，旨在培育自我导向成长这一动态能力，使学习者能在多样化情境中持续推动自身发展路径。基于现有自决学习与AI辅助教育研究的空白，提出的面向学习者的潜力激发（A2PL）模型重构了学习者抱负、复杂思维与总结性自我评估在生成式AI支持环境中的相互作用机制。本文讨论了对未来干预设计与学习分析应用的方法论启示，将自我导向成长定位为构建数字时代公平、适应性强且可持续学习系统的核心轴心。
原文摘要 · Abstract (English)
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.
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