Personalized information technology instructional design based on transformer model

With the continuous advancement of educational informatization, personalized teaching has received widespread attention as an important means to improve teaching quality and meet students’ differentiated needs. Especially in the field of information technology teaching, how to use advanced artificial intelligence technology to optimize teaching design and improve learning effect has become a frontier topic of educational research. As a deep learning technology with powerful sequence modeling capabilities, Transformer model has been widely used in natural language processing, recommendation systems and other fields. Based on Transformer model, this paper discusses its application and effect in personalized information technology instructional design. By analyzing the experimental data of 200 students, this paper constructs a personalized teaching model based on Transformer, which can dynamically adjust the teaching content and learning path according to students’ learning behavior and knowledge mastery. The experimental results show that after adopting the instructional design based on Transformer model, students’ academic performance is improved by 22.5 %, and the accuracy of personalized recommendation content is improved by 30 %. In addition, students’ learning initiative and participation have also increased significantly, and the number of classroom interactions has increased by 40 %. The research shows that the Transformer model can not only accurately identify students’ learning needs, but also realize real-time teaching adjustment, which provides a new solution for personalized information technology teaching. This study provides useful reference and practical support for the future development of intelligent teaching.