Enhancing EFL Education through Hybrid Attention Models in Higher Education and Vocational Training

Vocational training is recognised for enhancing economic development, social welfare, and employment opportunities. International practice can strengthen trainers’ technical abilities and employability in both initial and continuous vocational education and training. This method’s theory emphasises the significance of emotional variables in assessing an individual’s proficiency in a foreign language. The examination of EFL programs at universities and technical institutions employs innovative empirical approaches. BiRCAtt is an attention-driven methodology that improves the efficacy of EFL and vocational chatbots. It is a hybrid bidirectional recurrent convolutional neural network. Min-max normalisation, noise elimination, and outlier detection are preprocessing techniques. CountVectorizers are techniques for feature extraction that improve learning. The experimental results indicate that BiRCAtt attains a superior maximum accuracy of $\mathbf{9 5. 6 3 \%}$ compared to previous models. The study indicates that chatbot performance is enhanced via preprocessing and feature extraction. The results illustrate the effectiveness of advanced neural network models in EFL instruction, especially at the university and professional levels. Future research should prioritise the enhancement of chatbot performance and the expansion of their educational applications globally.