AI-Powered Narrative Generation for Personalized Learning in Primary Schools
Annotatsiya
Personalized storytelling in elementary school increases participation and retention by tailoring stories to each student's individual interests and learning style. But today's schools aren't flexible enough to make lessons that are tailored to each student. This study's neural text generation model is based on an improved GPT-2 architecture. It uses learner profiles that include interest vector, reading level, and emotional tone. The model uses Byte Pair Encoding for input formatting and token-level conditioning, ensuring that the narrative it generates is relevant and coherent. When BLEU, METEOR, and human-rated engagement metrics are used to measure performance, the results are better than the baselines for general storytelling. Specifically, personalized outputs boosted participation 24% and understanding by 18% in experimental classroom environments. The results show that AI-powered personalized stories work well in preschool and kindergarten. This method enables adaptive learning systems to change based on each student's needs.