Exploring the Path of AIGC and AI Agents Empowering Front-End Teaching and Learning

Dongxing Wang; Wang Yu; Weixing Wang

doi:10.26689/jcer.v9i11.12943

Dongxing Wang Faculty of Artificial Intelligence, Guangdong Polytechnic Institute, Zhongshan 528458, Guangdong, China
Wang Yu Faculty of Innovation and Design, City University of Macau, Macau 999078, China
Weixing Wang Faculty of Artificial Intelligence, Guangdong Polytechnic Institute, Zhongshan 528458, Guangdong, China

DOI: https://doi.org/10.26689/jcer.v9i11.12943

Keywords: AIGC, AI intelligent agent, Front-end education, Teaching and learning efficiency

Abstract

In response to the pain points of rapid iteration of front-end education technology, large differences in learner foundations, and a lack of practical scenarios, this paper combines generative artificial intelligence and AI agents to analyze the empowerment logic from three dimensions: knowledge ecology reconstruction, cognitive collaborative upgrading, and teaching methodology innovation. It explores its application scenarios in teaching and learning, sorts out challenges such as technology adaptation and learning dependence, and proposes paths such as building an exclusive AI ecosystem and optimizing the guidance mechanism of intelligent agents to provide support for the digital transformation of front-end education.

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