The instructions to understand literature and language are evolving in an era where we are completely dependent on AI. There is a confluence of artificial intelligence (AI) and cognitive science. As this study has good scope, this paper synthesizes interdisciplinary research to reveal: (1) how machine learning (ML) and natural language processing (NLP) allow for the detection of large-scale textual patterns, despite limitations in hermeneutic depth; (2) the cognitive mechanisms that support AI-driven adaptive learning systems (e.g., dual coding, spaced repetition); and (3) ethical issues such as algorithmic bias, data privacy, and the "human-AI symbiosis" dilemma in pedagogy. Research has shown that developments are there, such as transformer-based models (e.g., BERT) in literary stylometry and the effectiveness of AI tutors in second language acquisition (SLA) by analysing 120 peer-reviewed articles published between 2015 and 2023. Observations clarify AI improves corpus analysis efficiency (e.g., detecting diachronic theme transitions in books from the 19th century with 92% accuracy; Lee & Huang, 2023) and optimises language training (30% faster competence increases compared to traditional approaches; Martinez, 2018). However, there are missing links: There is a problem with understanding metaphors which are embedded in culture (F1-score = 0.65 compared to 0.89 for human specialists; Anderson, 2022), so learners cannot rely on it. Midway is always good, wherein literary evaluation should be done by humans, and AI can look after bulk tasks like vocabulary drills. In the present times of a multidisciplinary approach, suitable integration and a transparent system which can lead to interdisciplinary cooperation among technologists, linguists, and cognitive scientists are the objectives of the study to continue discussions with respect to AI's place in education.