Under the Framework of Deep Learning Cognitive Theory and Bloom's Taxonomy: Investigating the Role of Artificial Intelligence in Fostering Higher-order Thinking in Engineering Education

This study aims to explore the role of artificial intelligence in cultivating students' higher - order thinking (analysis, evaluation, and creation) abilities in engineering education within the framework of deep learning cognitive theory and Bloom's Taxonomy of Educational Objectives. Students from multiple engineering majors of a specific university were selected as the sample, and an experimental group and a control group were set up. The experimental group adopted AI - assisted teaching, while the control group used traditional teaching methods. Python was used for data processing and algorithm design. Data were collected through methods such as standardized tests, student work evaluations, classroom performance observations, and thinking process interviews, and the results were presented using various visualization techniques. The findings show that AI - assisted teaching significantly enhances students' higher - order thinking abilities, providing a theoretical and empirical basis for the effective integration of AI technology in engineering education. However, the study has limitations such as a narrow sample range and short intervention time. Future research can be conducted in directions like expanding the sample and long - term tracking.