AIComprehend: An Adaptive Reading Comprehension Learning Platform Using Machine Learning

This paper offers AIComprehend, a machine learning-based online platform whose objective is to improve the English reading comprehension of its users using an adaptive learning approach. The project entailed the development of a web-based application that consisted of multiple-choice reading comprehension questions of varying difficulty levels and knowledge components. A four-week beta testing of the application was carried out at Daniel R Aguinaldo National High School where 58 students were divided into control and experimental groups, with the experimental group using the application for 30 minutes daily. The Pre-test and post test were conducted four weeks apart with identical difficulty to assess the intervention. Results showed that the mean Pre-test score of 6.97 for the experimental group improved to a post-test score of 7.94, signifying an approximately 13.9% improvement. The control group, however, saw a decline from 8.46 in the pre-test to 6.19 in the post-test, marking a roughly 26.8% decrease. Moreover, the accuracy (79.06%) and AUC (65.95%) scores of the PFA-based system showed potential as reading comprehension performance tracing tool.