Optimization and Application of Generative AI Algorithm Based on Transformer Architecture in Adaptive Learning

At present, generative AI has problems of insufficient content generation accuracy, weak personalized response, and low reasoning efficiency in adaptive learning scenarios, which limit its in-depth application in intelligent teaching. To solve this problem, this paper proposed a Transformer fine-tuning method based on low-rank adaptation technology, which realized efficient parameter update of pre-trained models through low-rank matrix insertion, and combined the instruction fine-tuning strategy to perform domain adaptation training on the model for the constructed educational scenario dataset. At the same time, a dynamic prompt construction mechanism was introduced to enhance the model’s context perception ability of individual learners’ behaviors, thereby achieving precise alignment and personalized control of generated content. This paper embeds the “wrong question guidance” and “knowledge graph embedding” mechanisms in the model, provides intelligent feedback based on student errors, and promotes in-depth understanding of subject knowledge through knowledge graphs. Experimental results showed that this method scored higher than 0.9 in BLEU and ROUGE-L. The average response delay was low, which was significantly better than the traditional fine-tuning method. This method showed good adaptability and practicality in the fusion of generative AI and adaptive learning and provided a generalizable optimization path and application solution for intelligent education systems.