A method for generating personalized learning content based on AIGC

In the realm of the swift advancement of Generative Artificial Intelligence (AIGC), the methodologies for generating individualized learning content have emerged as a forefront subject of inquiry in education. This study examines recent research developments and application trends of AIGC in educational content production, emphasizing individualized learning content generation techniques utilizing AIGC technology and its usefulness in practical applications. Initially, by employing educational resource libraries and learning behavior data, data quality is augmented, and analytical efficiency is refined by preprocessing techniques including data cleansing, resource categorization and labeling, multimodal alignment, and consistency verification. Subsequently, the Transformer architecture is employed to extract learning characteristics from behavioral data, while content relevance is augmented via knowledge point embedding and multimodal content production. A reinforcement learning approach is incorporated to execute a feedback-driven dynamic optimization procedure. Ultimately, comparison trials confirm the substantial benefits of the AIGC-based individualized learning material generation method in enhancing generation efficiency, optimizing resource flexibility, and improving learning outcomes. AIGC enhances educational quality by producing tailored learning materials, so effectively advancing the United Nations' Sustainable Development Goal (SDG 4) for excellent education. AIGC, through technology-driven educational innovation, can revolutionize traditional educational resource allocation, enhance social inclusion, and guarantee equitable educational opportunities, thus fostering comprehensive societal transformation.