Reshaping Smart Learning Environments with Generative Artificial Intelligence:From Element Improvement to Ecosystem Reconstruction
As a crucial pillar of educational digital transformation,smart learning environments have encountered issues such as data silos,model limitations,resource rigidity,tool complexity,inflexible services,and fragmented scenarios during their development.Generative artificial intelligence(GAI),as an emerging form in AI technology,presents new opportunities for the upgrading and transformation of smart learning environments.The study upgrades the three-layer and six-factor theoretical model of smart learning environment with generative AI as the power engine.It is argued that within this framework,the data element has shifted from low quality to high efficiency,the model element from the discriminative to the generative,the resource element from the superficial to the semantic,the tool element from the distributed to the integrated,the service element from the predefined to the adaptive,and the scenario element has moved from the marginalized to the centralized.On this basis,the study further clarifies that GAI can restructure the ecosystem of smart learning environments by transforming the ideas of talent cultivation,the concepts of knowledge and curricula,teaching modes and learning styles,educational evaluation systems,and educational governance models.The study analyzes the improvement of internal elements and the reconstruction of the external ecosystem of smart learning environments,providing theoretical research support and practical exploration direction for shaping new forms of smart learning environments.
Smart Learning EnvironmentsGenerative Artificial IntelligenceElement ImprovementEcosystem ReconstructionTheoretical Model
万方数据
维普
