With the rapid development of Artificial Intelligence Generated Content (AIGC) technology, the education sector is undergoing a profound transformation from digitalization to intelligence. Leveraging its powerful content generation capabilities, AIGC offers unprecedented opportunities for the personalized customization, dynamic updating, and multi-modal presentation of teaching resources. However, current applications of AIGC in educational scenarios still face severe challenges, including insufficient accuracy of generated content, prominent ethical risks, a lack of teacher competence in human-machine collaboration, and lagging evaluation systems. This paper aims to systematically review the status quo of AIGC-assisted teaching resource generation and deeply analyze core issues regarding technical reliability, educational adaptability, and ethical safety. On this basis, it proposes countermeasures such as constructing a "human-machine collaborative" generation paradigm, establishing a multi-level content audit mechanism, reshaping the teacher digital literacy system, and perfecting intelligent resource evaluation standards. Research indicates that through the dual drive of technological optimization and institutional innovation, the quality and applicability of AIGC-generated resources can be effectively improved, promoting supply-side reform in educational resources and ultimately achieving an organic unity of scaled education and personalized cultivation.
Zhou Xiaomei (2026) studied this question.