Amid the swift advancement of artificial intelligence technology, personalized teaching models have emerged as a critical area of research within the education sector. This study, following an examination of the present landscape and developments in personalized teaching models and innovations, underscores the particular applications of data-driven personalized teaching models in education. Firstly, learner profiles are created using educational big data, delivering an in-depth examination of students' learning traits and knowledge proficiency to provide accurate data support for individualized instruction. Secondly, tailored learning resource suggestions, derived from hybrid recommendation algorithms, markedly improve students' educational experience and resource usage efficiency. Ultimately, comparative trials confirm the efficacy of data-driven tailored teaching innovation models in enhancing learning results across various educational environments. The findings indicate that, in contrast to traditional teaching approaches, data-driven tailored instruction offers substantial benefits in improving learning outcomes, refining learning pathways, and suggesting resources. This not only validates the efficacy of tailored instruction but also offers essential theoretical and practical insights for its design and implementation.
Zhenghua Hu (Fri,) studied this question.