Design and Application of Teaching System for Chinese Language and Literature Major Drove by Interpreted AI Technology

Abstract

ABSTRACT To improve the teaching effect of the Chinese language and literature majors, this paper combines artificial intelligence technology and performance to build a teaching system for the Chinese language and literature majors. Based on the existing coordination algorithms, this paper proposes a classifier-based fast calculation method for successive breaking of active power flow, which provides high-quality active power information for the static safety check and control of successive breaking at a small computational cost. Moreover, this paper improves the operation effect of the teaching system, increases the calculation speed, and adopts the NR method for algorithm-sensitive faults to ensure the reliability of the power flow results. The proposed teaching system was tested in a simulated teaching environment to evaluate its effectiveness. The testing results showed that the system significantly improved student performance, engagement, and satisfaction. However, some challenges and limitations were also encountered during the testing process, such as the need for sufficient computing power and the difficulty in accommodating individual learning styles. The findings of this paper suggest that the proposed teaching system has the potential to enhance the learning outcomes of Chinese language and literature students. However, further testing and optimization are needed to address the challenges and limitations encountered in the testing process.