Enhancing Language Acquisition for Beginners: A Data-Driven Approach

Abstract

This paper examines the integration of Artificial Intelligence (AI) and Machine Learning (ML) tools in language learning for novice learners, with a focus on grammar correction, vocabulary acquisition, speech recognition, and enhancing motivation for continued learning. A survey to justify the selection of AI-based tools such as Large Language Models (LLMs), reinforcement learning, adaptive learning systems, and automatic speech recognition (ASR) was performed. Results indicated that LLMs significantly improved grammar accuracy, while reinforcement learning-based vocabulary tools enhance retention. Transformer-based architectures outperform traditional models in contextual language understanding and speech recognition, reducing word error rates. Additionally, AI-driven chatbots and gamification platforms improved engagement. Despite these advancements, challenges such as AI over-reliance, lack of deep personalization, and accent bias in ASR models remain. The research follows a comparative and mixed-methods research design approach with machine learning-based model evaluation, using structured and unstructured datasets. Model performance will be assessed through accuracy, precision, and recall metrics.

Ähnliche Arbeiten