The Impact of Natural Language Processing on Literacy Education and Practice

Natural Language Processing (NLP): NLP has revolutionised many domains in recent years, and literacy learning is one of those areas to have benefitted. Designed as a primer for literacy educators, this paper explores how the rapid developments in NLP are changing traditional pedagogical approaches and improving learning outcomes. This research investigates the integration of naturally processing technology tools in educational frameworks supporting reading, writing and comprehension skills for diverse learner demographics through a systematic review method on current literature and case studies. Automated essay scoring, sentiment analysis and language modelling are innovative tools that employ NLP technologies to evaluate student performance. These resources provide not only instant but also customized responses that are essential to building a richer vocabulary and language comprehension. As an example, automated essay scoring systems speed up the grading process while also provide specific feedback on grammar, coherence and argument structure; so that students can improve their own writing over multiple iterations. This is particularly important when it comes to turn-based and asynchronized learning but difficult for teachers who lack freeware sentiment analysis tools that can provide information on students' levels of emotional engagement with the text itself. Additionally, NLP-fueled reading aids (both native tools and third-party add-ons like text-to-speech or speech-to-text apps) remove obstacles for students with dyslexia who are mastering literacy.