A Study on the Construction of an AIGC-Enabled Experiential Learning Model for Enhancing Digital Literacy among Community Residents

Abstract

With the rapid development of artificial intelligence-generated content (AIGC) technology and its penetration into various social domains, community residents are confronted with an increasingly complex digital environment, which places higher-level demands on their digital literacy, encompassing operational, critical, collaborative, and innovative competencies. Traditional models for cultivating digital literacy exhibit limitations in terms of situational authenticity, depth of interaction, and personalized support. This study aims to construct an AIGC-enabled experiential learning model for enhancing digital literacy among community residents. The research first analyzes the intrinsic coupling logic among the technological paradigm of AIGC, the contemporary connotations of digital literacy, and experiential learning theory. It then deconstructs the core elements of the model, including dynamic contextualized task design driven by AIGC, a subject participation mechanism characterized by deep human-machine collaboration, and the generation of adaptive learning pathways based on continuous feedback. Finally, the study delves into the efficacy mechanism of the model in facilitating knowledge construction and skill transfer, examines its potential risks and ethical boundaries, and envisions the evolving role of AIGC in future learning ecosystems. This research provides theoretical reference and a model framework for innovating the cultivation pathways of digital literacy among community residents in the context of intelligent technology.