This research presents an innovative framework for developing educational chatbots that redefine student support by integrating advanced natural language understanding (NLU), intent recognition, and pragmatic analysis. Leveraging machine learning techniques, including pre-trained models like BERT, the chatbot achieves state-of-the-art performance in recognizing user intents and delivering contextually relevant responses. By addressing the limitations of traditional systems, such as poor personalization and difficulty in handling nuanced queries, this framework enables dynamic, adaptive, and engaging interactions. The chatbot transcends conventional query handling through pragmatic analysis, allowing it to interpret subtle nuances, emotional states, and real-world contexts. This ensures personalized responses that align with individual learning needs, fostering deeper student engagement and comprehension. Fine-tuned with diverse datasets and instructional materials, the system is robust and scalable, making it suitable for a wide range of educational applications. This approach also emphasizes human-like interaction, combining emotional intelligence with context-aware capabilities to create a supportive learning environment. By enhancing response accuracy, adaptability, and user engagement, the chatbot sets a new benchmark in educational technology. Ultimately, this research demonstrates transformative potential in creating intelligent, scalable, and highly effective tools for modern education, paving the way for a more personalized and interactive learning experience.