An Optimization-Based Artificial Intelligence Framework for Teaching English at College Level Under Tribhuvan University

Abstract

Learning and computing breakthroughs among students are beginning to converge due to the rapid growth of digital technology. Artificial Intelligence (AI) has made an impact on the way we teach English at college level. It has an enormous potential of providing digitalized and completely personalized learning to each English language teacher. This quantitative quasi-experimental research offers a strategy for incorporating Artificial Intelligence (AI) in English language teaching at college level. The participants 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. The researcher employed 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. The findings were presented descriptively and the researcher assessed the model's criteria, designed a comparison test, and conducted a survey questionnaire to check the reliability and effectiveness of the prediction. The evidence shows that Enhanced Whale Hyper-Tuned Artificial Neural Network (EWH-ANN) EWH-ANN can be employed to optimize English instruction at college level in general and verbal improvement in particular. It can make English teaching more efficient and customized to fulfil 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.