Teaching Reform of Sensor and Detection Technology Driven by Artificial Intelligence

In response to the problems of outdated knowledge and shallow practice in traditional sensor and detection technology courses, this study proposes reform of sensor course driven by artificial intelligence. In terms of content, a three-level system of “basic theory—AI fusion module—dynamic case library” is constructed, integrating artificial intelligence technologies such as deep learning and digital twins into the core teaching points of sensors to support dynamic updates of the teaching case library. The practical stage adopts a three-stage mode of “virtual simulation → semi physical debugging → system integration verification”. The evaluation mechanism has been innovated with a three-dimensional framework of “knowledge— ability— quality”, and AI-assisted analysis of experimental report logic has been introduced. The practice has shown that the curriculum teaching reform has effectively enhanced students’ systematic problem-solving abilities.