A Systematic Review of AI Chatbots in Education Using NLP and LLMs

AI chatbots have emerged as an important part of the contemporary education structure, assisting students in academics and administration, as well as increasing their access to information. Due to the rule-based nature of early chatbots, their flexibility and comprehension capacity were limited. However, due to advances in Natural Language Processing (NLP), Deep Learning (DL), and Large Language Models (LLM), increasingly sophisticated chatbot systems that understand contexts and can interact with users have been introduced. This paper provides a comprehensive analysis of twelve notable research articles that highlight the use of AI chatbots in education. The selected research articles are analyzed in terms of chatbot architecture, technology, data sources, and evaluation criteria under the framework of systematic literature review. The results demonstrate a shift from rule-based machine learning techniques to RAG model, deep learning, and semantic embeddings. These novel methodologies imply higher quality and accuracy of responses. However, there are numerous including hallucinations in generative models, lack of evaluation metrics, scalability, and trust in data sources. This research highlights the present research trends, limitations of existing methodologies, and future directions of research in developing trustworthy, scalable, and intelligent AI chatbot applications in education settings.

AI chatbots; conversational AI; educational technology; natural language processing; deep learning; retrieval-augmented generation

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