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Abstract

In the digital era, the application of artificial intelligence in education has brought revolutionary changes. Personalized and intelligent generation of educational content is key to enhancing teaching efficiency and the learning experience. Traditional methods of generating educational content suffer from inefficiency and a lack of personalization. To address these issues, this study proposes a method for the automated generation of educational content based on semantic analysis, aimed at improving the quality and efficiency of content generation. Initially, the research explores semantic analysis of educational content based on knowledge extraction, introducing a novel lightweight semantic analysis model capable of efficiently extracting key knowledge points from educational materials. Subsequently, the study investigates a semantic analysis-based content generation approach, utilizing masked semantic style encoding and an innovative method for indiscriminate semantic stylization of text generation to achieve consistent and personalized educational content output. These methods not only reduce the demand for computational resources but also enhance the level of content personalization. Overall, the proposed methods provide new technical avenues for the generation of intelligent educational content and lay the groundwork for the development of personalized educational resources to meet the diverse needs of learners. This has significant theoretical and practical value for advancing innovation and development in educational technology.

Keywords

intelligent education content generation semantic analysis knowledge extraction personalized learning text generation

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Automated generation of educational content based on semantic analysis

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