# Centralized Database Access: Transformer Framework and LLM/Chatbot Integration-Based Hybrid Model
## 2 Modeling Background
The chatbot model used in this study consists of four different modules: the front end, the back office, the knowledge-based module, and the e-learning bot module. Following the design philosophy of fluidity and adaptability, the e-learning bot module is designed for continuous learning and refinement and facilitates the simultaneous handling of many interactions with students [27]. The second model, presented by the group of authors around [PERSON] et al. [28] In the paper \"Chatbot for Education System\", introduces an automated framework that uses artificial intelligence and machine learning to answer user queries about education quickly and accurately. The chatbot is designed to provide students and educators with simultaneous access to information and real-time solutions to their queries. The automated system is configured to process user queries using the robust capabilities of artificial intelligence and machine learning. It is equipped with knowledge from various sources and uses techniques such as pattern recognition, natural language processing, and data analysis to compare user queries with existing knowledge in the database. Artificial intelligence enables the chatbot to learn from user queries and improve its responses over time. Machine learning is used to train the chatbot to recognize patterns in data and improve accuracy. Natural language processing (NLP) makes it easier for the chatbot to understand and interpret human language. Pattern recognition is used to compare user requests with information in the database. Data analysis is used to extract relevant information from the knowledge base and provide accurate answers to user queries. When a user enters a query, the chatbot extracts the relevant keyword from the given query and generates a response. If the data are not available in the static database, the chatbot retrieves it from online sources. The chatbot is designed to be scalable, user-friendly, and highly interactive. It can be a valuable tool to provide free advice to students, suggest different courses to them, and offer information about the education system [28]. In the context of developing a forthcoming hybrid model based on the fundamental findings of two key papers, it is important to recognize the significant contributions of various research initiatives in the field of artificial intelligence (AI) within the educational domain. These studies play a crucial role in enhancing understanding of the challenges and functionalities associated with the integration of AI in education. From the wealth of relevant research, the following contributions stand out, offering a systematic exploration of the aforementioned field. The outline follows a logical sequence that begins with innovations in machine learning environments, expands to applications in education, addresses challenges, highlights positive aspects, and concludes with a detailed analysis of performance and system optimization. The first research paper by [PERSON] and [PERSON] [29] introduces a revolutionary programming environment designed to enable students to manage the complexity of training machine learning models. By using a block-based programming language, this innovation addresses the inherent limitations of existing learning platforms. A key highlight of the study lies in the practical application of this environment. Two notable examples are the development of a question-answering chatbot and a celebrity look-alike program, both of which were carefully trained on extensive datasets. These practical implementations demonstrate the effectiveness of the proposed programming environment in real-world scenarios and highlight its potential to bridge the gap between theoretical learning and practical application. The next research work is concerned with exploring the transformative capabilities of large language models (LLMs) in the field of education.