An Optimization-Based Artificial Intelligence Framework for Teaching English at the College Level

Abstract

Owing to the rapid evolution of digital technology, learning, and computing breakthroughs among students are beginning to converge. The current method of English language teaching is considerably different from the traditional method. This quantitative, quasi-experimental research offers a strategy for incorporating Artificial Intelligence (AI) in college English teaching. The participants in the study consisted of 100 bachelor-level students studying at a constituent college of Tribhuvan University, Nepal. The participants were selected using simple random sampling and divided into two groups: the study group and the control group. I employed a questionnaire and test as the instruments to collect the data. The collected data was analyzed using SPSS 2.0 which is a tool for analyzing quantitatively challenging data. To check the reliability and effectiveness of the prediction, I assessed the model's criteria, designed a comparison test, and conducted a survey questionnaire. The evidence shows that Enhanced Whale Hyper-Tuned Artificial Neural Network (EWH-ANN) EWH-ANN can be employed to optimize English instruction at the college level in general and verbal improvement in particular. It can make English teaching more efficient and customized to fulfill individual students' necessities. The study concluded that The Whale Optimization Algorithm (WOA) can be used to tune the hyper-parameters of Artificial Neural Network (ANN) to improve the accuracy of the operation.