Natural Language Processing and Large Language Models: AI for Education, Communication, and Digital Content Analysis

Abstract

Natural language processing (NLP) has progressed from narrow, task-specific statistical models to large language models (LLMs) capable of generating fluent, contextually coherent text across virtually unlimited domains, a transition that has reshaped three previously distinct application areas simultaneously: education, human communication, and digital content analysis. This paper reviews the recent empirical literature on transformer-based NLP and LLM applications across these three domains, synthesizing systematic reviews of LLM integration in language-teacher education and broader educational settings with the transformer-architecture literature on sentiment analysis, sarcasm detection, and misinformation identification in social media content. Particular attention is given to the specific mechanisms, intelligent tutoring, personalized feedback generation, bidirectional contextual encoding, and pretrained-language-model fine-tuning, that the reviewed studies identify as driving reported performance gains, alongside the equity, reliability, and academic-integrity concerns that temper these gains. Comparative tables map LLM educational applications to their reported outcomes and adoption contexts, cross-reference transformer-based content-analysis tasks against the architectures and accuracy levels each study reports, and set documented risks and limitations against the mitigation strategies the literature proposes. The review finds that LLMs and transformer-based NLP models now achieve consistently strong empirical performance, frequently exceeding 90% accuracy or F1-score on sentiment, sarcasm, and misinformation-detection benchmarks, and demonstrate measurable educational benefit through intelligent tutoring and personalized feedback, but that reliability, bias, temporal drift, and unequal access to these tools remain substantial and only partially resolved challenges across all three application domains.