Leveraging Transformer-Based Natural Language Processing for Adaptive Language Learning in Digital Humanities Environments

Abstract

The convergence of transformer-based natural language processing (NLP) and digital humanities (DH) has opened unprecedented opportunities for automated language learning, discourse analysis, and pedagogical innovation. This study presents a corpus-driven computational investigation into the efficacy of large language models (LLMs) and domain-specific fine-tuned transformers; particularly BERT, RoBERTa, and T5; in supporting adaptive language learning platforms, automated assessment, and semantic retrieval within digital humanities contexts. Drawing on a multi-source corpus of 102,620 documents spanning academic journals, literary archives, e-learning transcripts, social media data, parliamentary debates, and news corpora (totalling over 13.1 million tokens), the research employs a mixed-methods design integrating quantitative NLP evaluation metrics (accuracy, Cohen's kappa, BLEU, ROUGE) with qualitative thematic analysis. Findings reveal that fine-tuned transformer models achieve accuracy rates between 79.4% and 94.3% across core language tasks, with adaptive e-learning applications demonstrating a 78% improvement in learner retention. Five overarching themes emerge: algorithmic bias, multimodal integration, low-resource language processing, human-AI collaborative writing, and real-time assessment. The study contributes a validated six-phase methodological framework for deploying NLP in DH-driven educational environments and advances theoretical understanding of computational pedagogy at the intersection of language, technology, and culture.