AI-Enhanced Deep Learning Techniques for Evaluating Progress in English L2 Learners

The current trend of learning English as a second language (L2) especially at the international level has created a need to have assessment techniques to measure the improvement of the learners. This paper analyses the effectiveness of applying the Artificial Intelligence based Deep Learning known as Seq2Seq – Sequence to Sequence models with attention mechanism to assess and forecast the advancement of English L2 learners. To analyse the learner responses, and keep record on regarding changes/improvements in the linguistic features as time progresses, Seq2Seq models are used since they are effective in handling sequential data. The residual blocks enhances upon this process by allowing the model to focus on salient portions of the input sequence for eminently more accurate evaluations. This research employs contemporary computational approaches to analyse different areas of language development such as the lexis, syntax and the flow of the language. In essence, the model relies on a substantial amount of learner data to offer a good learner profile, and point out where learner might need extra help. The study uses a comprehensive dataset comprising of samples of learner-generated texts which have been tagged with proficiency level and markers of learning progress. Further, the incorporation of attention in the Seq2Seq model is compared to conventional assessment metrics where greater accuracy in assessing learner progress is achieved. In this way, it not only offers a quick method of creating an objective evaluation based on the need, but it also aids in realizing the idea of an individual learning experience by identifying the unique strengths and weaknesses of a particular student. The idea of incorporating AI deep learning algorithms supported by seq2seq models and attention techniques to assess English L2 learners is fresh in the current methodological context because it permits the attention to relevant language features such as syntax and grammar during the assessment conducted for the purpose of this study. The attention mechanism improves the accuracy by providing focus on certain linguistic aspects. This makes the feedback more suitable, specific from data collected and more efficient in tracking the learners’ progress than other assessments. The results highlight the applicability of deep learning methods in changing the way language learning and assessment processes occur, giving educators powerful tools in the learning process of learners of English as the second language.