Large Language Models: A Review with its Role, Applications and Challenges & Future Directions

Abstract

The use of Large Language Models (LLM) has become a key to transforming the educational landscapes and enabling intelligent, adaptive learning through the help of GPT-4 and Llama series. This paper gives an in-depth discussion of the contribution of LLMs in the field of education starting with the initial work done using the machine in personalized learning, automated content generation, and real-time student interaction. Among the important examples, we describe adaptive assessment systems, which are dynamically adjusted to the performance of the learner, AI-based curriculum development in a wide range of disciplines, virtual tutoring of underserved communities, and multilingual translation to universal accessibility. A comprehensive review of the literature will summarize the existing works, and a new differentiation analysis will be performed between the most popular state-of-the-art articles in critical metrics: model accuracy (perplexity scores), computational scalability, compliance with ethics (bias and fairness indices), pedagogical effect (learning gain metrics), and the ability to be deployed in a low-resource environment. Results indicate that the advantages of the use of LLMs in increasing access and efficiency include scores of up to 25 points higher in engagement metrics than traditional methods, but uncovered some longstanding issues, such as the risks of hallucinations, lack of data privacy, training corpora bias based on cultural factors, and compatibility with the existing educational infrastructure.