Cultivation Method Analysis for Teachers’ Teaching Ability Driven by Artificial Intelligence Technology

Abstract

Strengthening construction for teaching staff is an eternal theme of development and construction for colleges, and it is also the focus of personnel management in colleges. China is swiftly transitioning from the industrial age to the era of intelligence as a result of the rapid growth of information technology and artificial intelligence. Colleges and universities have reached a new stage in their evolution, one marked by intelligent use of technology, as represented by the fourth generation of information technology: cloud computing, big data, and artificial intelligence. Higher standards for college professors’ teaching abilities have been imposed by this new policy. It is therefore beneficial to evaluate teachers’ teaching abilities from an artificial intelligence perspective to improve the overall quality of college education. First, this work researches and improves the teaching ability training strategies of college teachers driven by artificial intelligence from different levels. Second, this work proposes a neural network (IPSO-BP) for evaluating the teaching ability of college teachers via artificial intelligence technology. Aiming at the issues in BP network, this work constructs IPSO by improving the weight decay strategy and learning factor of PSO algorithm. Then, it uses IPSO to optimize the BP to construct IPSO-BP. Third, the results of the experiments in this work suggest that the strategy proposed here is both feasible and preferable.