The aim of this study is to investigate the role of Natural Language Processing (NLP) in developing personalized training materials for blind people. Despite the availability of different formats of training materials, blind people often face challenges in accessing and using them effectively. To address this issue, we developed personalized audio-based training materials using NLP techniques that adapt to the individual needs and preferences of blind users. In this study, we describe the design and development of the personalized training materials and evaluate their effectiveness through user feedback. Our results indicate that the personalized audio- based training materials are effective in improving the accessibility and effectiveness of training for blind people. The study highlights the potential of NLP to enhance the accessibility of training materials and improve the educational outcomes of blind people. The implications of the study for future research and practice are discussed.
Remedial teaching in EFL classrooms involves providing additional support and guidance to students who are struggling with their English language skills. Different approaches can be used to address the individual needs of these students, including differentiated instruction, small group instruction, peer tutoring, scaffolded instruction, multisensory instruction, use of technology, and individualized learning plans. These approaches promote personalized instruction, targeted feedback, and a supportive learning environment, ultimately helping struggling students improve their English language proficiency.
Standardized testare intended to determine if the student graduates or not if the teachers are doing well and if the schools are improving. They are administered, qualified and interpreted in the same way to be able to compare the results of large groups of students.With standardized tests, the teacher role changes, especially when dealing with institutional tasks in addition to their regular class work and activities. Several of the teachers’ responsibilities include collecting, organizing and analyzing data, grouping and regrouping students, developing the curriculum and coordinating student tasks. These tasks and institutional tests take between 60 and 110 hours in a year. To prepare students, teachers usually use predesigned curriculums that they did not develop and cannot modify to fit the needs of their students in their courses.
Individualized teaching, tailored to the unique needs and abilities of each learner, has long been an educational ideal. With the advent of artificialintelligence (Al) technology, this goal is becoming more achievable than ever before. This article explores the role of Al in individualized teaching, focusing on its applications in adaptive learning, personalized content delivery, and assessment. It also examines the benefits, challenges, and future prospects of integrating Al into education.