# Centralized Database Access: Transformer Framework and LLM/Chatbot Integration-Based Hybrid Model
## 1 Introduction
Ease of use was examined through system interactions during e-learning sessions, focusing on aspects such as knowledge presentation, clarity of responses, and the appropriateness of the information provided. The study also examined the limitations and errors encountered with ChatGPT and emphasized the need for critical thinking when using ChatGPT [6]. Focusing on AI-driven e-learning methods, a group of authors presented their EDUBOT, a student chatbot powered by AI and a learning management system (LMS). The system provides students with resources for each topic and solves their problems. This product offers features such as a dynamic front end, interactive query search, personalized student-centric assessment, and Super User [7]. The use of generative language models to enhance interactive learning with social learning robots is proposed in a model that combines the technological capabilities of generative language models with the educational tasks of a social robot that acts as a tutor and learning partner. The social robot generates explanations, asks questions, corrects errors, and gives answers based on a pre-trained GPT-3 model to explore the potential of generative language models for interactive learning with social robots at different levels of abstraction [8]. It is interesting to mention a chatbot that processes voice messages in addition to written communication, which is particularly practical for students with disabilities [9]. Additionally, numerous comprehensive overviews of current studies on the implementation of AI and chatbots in education were compiled, with a particular emphasis on the field of e-learning [10; 11; 12; 13; 14; 15]. Furthermore, an innovative hybrid model based on the transformer framework and using an API for an existing large language model (LLM)/chatbot is proposed. This model successfully handles the complexity of centralized storage for educational materials. Considering the challenges posed by a fragmented and disparate database, the model provides an elegant solution that integrates precise, mathematically defined functions using sophisticated algorithms to enable in-depth analysis and text processing. The architecture of the model is based on the integration and customization of the API for the existing LLM/chatbot model. Chatbots can be broadly categorized into two types: rule-based and AI-based. Rule-based chatbots provide users with different options that they can explore. They are most often used for simple tasks such as answering frequently asked questions (FAQs). AI-based chatbots use artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) technologies and algorithms to understand the various keywords that users type while chatting with them. These chatbots are trained over time and learn which responses they should provide according to user queries. A hybrid chatbot is a blend of rule-based and AI-driven chatbots. It follows pre-set commands for fallback scenarios when the AI is unsure of the answer, avoids misinformation by signaling its uncertainty, and uses machine learning for complex queries. As the name suggests, hybrid chatbots are a mixture of simple, rule-based bots and advanced, context-aware bots. They combine the strengths of both types of chatbot technology. They can handle common, repetitive requests to provide immediate assistance, but they can also understand questions that require some judgment. A hybrid chatbot combines the strengths of rule-based and AI-based chatbots, providing a more balanced and versatile tool. In the landscape of AI-driven education models, the chatbot for education system model proves to be an automated framework that utilizes artificial intelligence and machine learning to address user queries swiftly and accurately in the educational domain. While it facilitates real-time access to information for both students and educators, its reliance on pattern recognition and data analysis may pose challenges for precision and adaptability.