Generative Artificial Intelligence in Language and Literary Studies: A Systematic Review of Applications, Ethical Challenges, and Future Directions

Generative artificial intelligence (GenAI) is transforming language and literary studies by introducing new approaches to language learning, writing, translation, textual interpretation, literary analysis, and creative production. Powered by large language models and other generative technologies, these systems can generate and revise texts, summarize complex literary works, identify linguistic patterns, support multilingual communication, and facilitate interactive learning. However, their growing adoption raises important concerns regarding authorship, academic integrity, linguistic bias, cultural representation, interpretive reliability, data privacy, and the potential marginalization of less-resourced languages and literary traditions. This systematic review examines the applications, ethical challenges, and future directions of GenAI in language and literary studies. It adopts a structured literature-review approach to synthesize research on educational applications, computational linguistics, translation, literary interpretation, creative writing, and digital humanities. The review organizes the literature around five major themes: language learning and teaching, academic and creative writing, translation and multilingual communication, literary analysis and interpretation, and ethical and institutional governance. The synthesis suggests that GenAI can expand access to language-learning support, facilitate text-based research, improve writing processes, and support new forms of literary experimentation. Nevertheless, its effectiveness depends on human oversight, contextual accuracy, critical evaluation, and sensitivity to linguistic and cultural diversity. The review further identifies continuing challenges associated with fabricated information, algorithmic bias, unclear intellectual-property arrangements, overreliance on automated assistance, and inadequate representation of minority languages. It argues that GenAI should complement rather than replace human linguistic judgment, literary expertise, and creative agency. Future research should prioritize transparent evaluation frameworks, culturally responsive models, multilingual equity, responsible pedagogical integration, and interdisciplinary collaboration. The review contributes an integrated perspective for researchers, educators, writers, and institutions seeking to use generative technologies responsibly in language and literary scholarship.