Multimodal generation and personalization algorithms for educational content using generative AI

Generative artificial intelligence, as a technology with powerful language generation and content creation capabilities, has shown a wide range of application potential in the field of education. This paper focuses on the application of generative AI in educational content creation, analyzes the current development status and main challenges of the current technology, discusses the generative model and algorithm optimization methods applicable to educational scenarios, and describes in detail the key technological aspects of data preprocessing, model design, and algorithm optimization. Through typical application cases, the practical effects of generative AI in intelligent textbook authoring, personalized learning resource generation and multimodal content creation are demonstrated. The study shows that generative AI can effectively improve the efficiency and quality of educational content creation, but it still needs to be further optimized in terms of data privacy protection, authenticity and applicability of generated content. This paper looks forward to the future development direction of this field, and puts forward the research focus of interdisciplinary integration, technological innovation and educational fairness, which provides reference and suggestions for the in-depth application of generative artificial intelligence in educational content creation.