Integrating Text Analysis and LLM to Explore the Development and Application of AIGC in Education

As discussions surrounding artificial intelligence (AI) and speculative design in education continue to grow, this study aims to assess how large language models (LLMs) analyze the application of AI-generated content (AIGC) and speculative design in current educational contexts, as well as their potential for future development. A sample of 30 articles on the application of AIGC and speculative design in education was collected, and a comprehensive text analysis method was employed, utilizing LLMs for summary analysis and opinion discussions. Through a cross-comparison of these methods, this research examines the similarities and differences in the impact of AI-driven tools on education. It explores their potential expanded functionalities in the educational field. In summary, this study integrates qualitative and quantitative data for comparative analysis, providing theoretical foundations and practical guidance on the future application of LLMs in education. The study also offers solutions to challenges and areas of concern for AIGC in education, as identified through LLM-generated inquiries, which can serve as a reference for future improvements. Further research can validate the effectiveness of these tools and explore their potential applications in diverse educational environments.