Enhancing Writing Skills with AI: Personalized Feedback Mechanisms for English Learners
Sridevi Dasam, Padmavathi Goriparthi, Mahesh Manchanda, Pavan Kumar Nowbattula, R. Subhashini, Ponni Valavan M · 2024
The increasing demand for strong writing skills in academic and professional environments underscores the necessity of developing advanced feedback systems capable of delivering personalized and context-aware guidance. Traditional feedback methods often fail to provide the specific, timely, and individualized insights needed to enhance writing proficiency effectively. To address these limitations, this research introduces a novel approach utilizing the T5 model, a state-of-the-art Transformer-based architecture renowned for its performance across various language tasks. The proposed system leverages T5's text-to-text framework to offer comprehensive feedback on grammar correction, style enhancement, and overall writing improvement. By transforming written text into a format that the model can process and analyze, the T5 model enables the generation of feedback that is not only accurate but also tailored to the specific needs of the learner. The system's ability to provide real-time, contextually relevant feedback marks a significant advancement over conventional methods often lacking the depth and precision required for meaningful writing improvement. Experimental evaluations confirm the effectiveness of this approach, with the T5 model achieving a BLEU Score of 4.12, a ROUGE Score of 0.62, and a METEOR Score of 0.31. These metrics highlight the model's superior ability to align with reference texts and capture nuanced feedback, ultimately contributing to the enhancement of learners' writing skills. This research demonstrates the potential of the T5 model as a powerful tool in educational settings, offering a robust solution for personalized learning and fostering the development of advanced writing capabilities in diverse learning contexts.