Conversational systems can be built using BERT’s (Bidirectional Encoder Representations from Transformers) pre-trained language understanding. As AI advances, chatbots have become essential for human interaction. The purpose of this research is to enhance access and communication in the field of education by developing a BERT-based FAQ chatbot. The study aims to leverage BERT’s advanced language understanding capabilities to create a conversational system that can effectively interpret and respond to user queries in an educational context. BERT, a pre-trained language model, is utilized by this research to develop a conversational interface. The query-response dataset is used for fine-tuning the chatbot, with BERT variants used for comparison. Context-aware generation for response generation is prototyped in Python and tested on Google Colab. Performance is measured using evaluation metrics such as Accuracy, ROUGE, and BERTScore (Precision, Recall, F1). Superior performance is shown by SBERT, with the highest precision (0.8443), recall (0.7724), and F1 score (0.8065) observed. It is shown by the results that educational chatbots are effectively supported by BERT and its variants through the assurance of semantic accuracy. However, difficulties with personalization, nuanced understanding, and technical jargon are included among the limitations. Contextual accuracy in educational chatbots is advanced by improved AI-driven student support systems. Human intervention is minimized by an optimized FAQ model, information retrieval is streamlined, and interaction efficiency in academic environments is enhanced. BERT and its variants are applied to educational chatbots in this study, with practical insights being offered through pseudocode and testing. It also highlights common chatbot issues and suggests ways to enhance decision-making using BERT models.