基于生成式人工智能的自我导向学习新形态OA

Generative Artificial Intelligence(GenAI)is reshaping learning approaches,and Self-Directed Learning(SDL)is increasingly becoming a predominant form of personalized education.From the perspective of the"morphology"of digital education,SDL shares its origins with classical personalized learning while also emerging as a new prototype for personalized learning in the GenAI era.However,there is currently a lack of systematic exploration regarding the specific mechanisms and theoretical models of SDL within GenAI-supported environments.To address this gap,this paper employs a literature review methodology,first focusing on the conceptualization of SDL supported by GenAI,along with its characteristics,including dynamic responsiveness to learning needs,iterative optimization of learning pathways,advancement and transformation of learning capabilities,and the prominence of human-machine collaborative ecosystems.Subsequently,drawing on problem system analysis methods,deep learning,and self-regulated learning theories,the paper constructs a theoretical model of SDL based on GenAI.It particularly emphasizes the scaffolding for autonomous questioning in SDL,such as text chains,question chains,question networks,and psychological mechanisms of questioning.Finally,the paper explores the five key stages of the GenAI-based SDL process:goal setting,question posing,follow-up inquiry,problem-solving,and self-regulation.This process promotes the evolution of the interactive relationship between GenAI and learners from"tool assistance"to"cognitive collaboration".