Artificial Intelligence (AI) in literary studies has disrupted traditional schools of thought regarding textual analysis, interpretation, and criticism. This research establishes a framework that is AI-powered: the Literary Interpretative Neural Algorithm (LINA), which shows promise for the analysis of complex linguistic patterns, thematic structures, and stylistic elements in teaching and learning language. With its hybrid approach integrating Natural Language Processing (NLP), transformer-based deep learning models, and sentiment analysis, LINA assesses literary texts ranging across historical and contemporary genres. Contrasting the conventional methods of literary analysis often judged through the lens of subjective interpretation, LINA enables data-driven, unbiased analyses of themes, character development, and intertextual relationships. The research further examines the capability of AI to reveal those aspects that have remained obscure: to establish hidden patterns, authorial intent, and the evolution of genre over aeons. The effectiveness of the model is validated in contrast to a heterogeneous corpus of literary works, including insights derived from the proposed model against traditional critical methods. This study concludes by emphasizing that AI-enhanced literary analysis could serve to advance academic discourse, automate the tasks of literary classification, and provide additional layers for text interpretation. Contributions will lie at the interaction between AI and humanities in translation and publications, stressing the need for interdisciplinary approaches in the digital age. Future work will characterize refined AI approaches for deeper semantic understanding and ethical issues in automated literary criticism.

Given the increasing importance of English proficiency in today’s globalized world, this study introduces an innovative approach to learning the language. Recognizing the complexities of language acquisition, the research proposes a novel method that combines recent advancements in reinforcement learning algorithms with educational knowledge to create a flexible and adaptive learning environment. This methodology charts a personalized learning path by thoroughly evaluating students’ reading, listening, and observational skills. By meticulously developing an application based on the suggested technique, 50 participants engaged in a practical application that utilized reinforcement learning algorithms to evaluate student performance and identify individual strengths. The continuous effectiveness assessments demonstrate the model’s resounding success, with an impressive 98% course completion rate and 97% accuracy. The platform aims to enhance cross-linguistic abilities, such as critical thinking and effective communication, alongside traditional language proficiency. This method empowers students globally by seamlessly integrating cutting-edge technology with pedagogical insights, offering a tailored and dynamic learning environment to help students thrive in the evolving educational landscape of the 21st century.

Automated translation of information regarding English language has become imperative in this worldwide phenomenon. The erstwhile methods of performing Extract, Transform, and Load (ETL) of data involve a lot of manual effort besides taking a long time and being error-prone. A linguistics-based deep learning approach is proposed to improve the efficiency and accuracy of ETL for automated translation of English language data. The particular approach is focused on the adoption of deep learning techniques for automatically processing and translating data in English language without extensive manual intervention. In conjunction with this, it provides the use of linguistics knowledge, such as syntax, semantics, and grammar, for building an accurate deep learning model for the extract-transform-load process of being applied in the translation process. The method has also proved to be promising in experimentation with results competing favorably with common ETL methods speed and accuracy and showing good scalability. Linguistic insights in combination with deep learning have created for the specified approach the possibility of bringing efficiency, accuracy, and automation to the translation of the English language.