Automated Deep Learning Approaches in Variational Autoencoders (VAEs) for Enhancing English Writing Skills

Abstract

Automated Writing Evaluation (AWE) tools, such as Grammarly and GPT-based models, have become increasingly prevalent in educational settings, offering immediate feedback to enhance writing skills. However, these systems often fall short in delivering personalized, context-sensitive feedback, particularly for English as a Foreign Language (EFL) learners. This research introduces a novel approach using Variational Autoencoders (VAEs) to develop an advanced writing assistance system that addresses these limitations. The methodology involved designing and implementing a VAE-based system that analyzes individual writing patterns and provides tailored feedback on grammar, coherence, and stylistic elements. Experiments were conducted to evaluate the system’s performance against traditional AWE tools using metrics such as accuracy, BLEU scores, and user satisfaction ratings. The findings revealed that the VAE-based system outperformed existing tools, achieving a 92% accuracy rate in grammar correction and an 83% F1-score in coherence improvement, while offering competitive performance in stylistic suggestions. This research bridges the gap between traditional pedagogical methods and advanced technological applications, fostering a more personalized and engaging writing experience for learners. By leveraging deep learning techniques, this study demonstrates significant advancements in writing instruction, addressing the critical gap in the literature regarding the effectiveness of AWE tools in providing adaptive feedback. The implications underscore the importance of integrating innovative technologies into writing instruction, ultimately promoting better outcomes for learners and educators. This research paves the way for further exploration of AI-driven tools in educational contexts, enhancing learners’ writing skills and contributing to the evolution of automated writing evaluation.