Development and Optimization of Language Reading Comprehension Aids Based on Natural Language Processing

Abstract

This study looks into the development and optimization of language reading comprehension aids based on Natural Language Processing (NLP) and assesses their usefulness in improving reading comprehension skills. Using a structured methodology that includes data collection, model training, interface design, and empirical evaluation, the study highlights the revolutionary potential of NLP technology in improving comprehension instruction. A random sample of 100 individuals from various educational backgrounds was randomized to experimental and control groups, with the former using NLP-based tools and the latter using traditional study methods. Following a four-week intervention session, participants were given standardized reading comprehension exams. The results show a considerable difference in comprehension scores between the experimental and control groups, with the former scoring significantly higher. Also, interaction with certain characteristics in the experimental group, such as text summary and tailored recommendations, was strongly connected with comprehension outcomes. These findings emphasize the relevance of tailored, adaptive help in text comprehension and the potential of NLP-based aids in boosting educational outcomes. While the study provides persuasive evidence for the efficacy of NLP technology in improving reading comprehension, additional research is needed to investigate the findings' generalizability across varied groups and educational settings.