Research on the Transformation and Effect of Lifelong Learning Methods Supported by Artificial Intelligence Jie Peng
The Open University of Ya^an
Abstract
With the continuous iteration and in-depth development of artificial intelligence (AI) technology, it has gradually penetrated into the entire process of education and teaching, exerting a profound and long-lasting impact on the form and system structure of lifelong learning. Compared with the traditional learning mode centered on teacher-led and one-way knowledge transmission, AI-supported lifelong learning places greater emphasis on the construction of personalized learning paths, interactive communication among multiple subjects, and real-time feedback on the learning process. This transformation has not only profoundly changed the learning behaviors and cognitive modes of adult learners, but also exerted a significant impact on their learning engagement, learning experience and learning outcomes, while putting forward new challenges and requirements for the optimal operation of the lifelong learning system. In this context, this paper focuses on the core issue of the transformation of lifelong learning methods in the context of AI support, constructs a theoretical analysis framework centered on the transformation of learning methods, clearly defines the connotation and characteristics of the transformation of AI-supported lifelong learning methods from three core dimensions: learning behavior, learning path and learning support, and systematically explores its influence mechanism and action path on learning effects. The research adopts a combination of literature analysis and theoretical combing, systematically sorts out the research results in related fields, constructs a theoretical model of the transformation of AI-supported lifelong learning methods and learning effects, and clarifies the measurement standards and methods of each variable. On this basis, empirical data from front-line adult learners are collected through questionnaire surveys, and quantitative analysis methods such as structural equation modeling (SEM) are used to test and revise the theoretical model, so as to provide a solid empirical basis and scientific theoretical analysis perspective for the optimization and improvement of the lifelong learning mode and the sound development of the lifelong learning system in the context of digital transformation.
