# EduChat: An AI-Based Chatbot for University-Related Information Using a Hybrid Approach
## 1 Introduction  
\"Chatbot\", an abbreviation for \"chat robot\", is an artificial intelligence (AI)-based conversational entity designed to simulate human conversation. Chatbots have emerged as an innovative technology, transforming how humans interact with machines and services. This technology, driven by advancements in natural language processing (NLP) and machine learning, enables smooth and efficient communication between users and digital systems. Chatbots find applications in various diverse fields, from customer support, e-commerce, healthcare, to education, thanks to their real-time responsiveness, personalization capabilities, and task automation. In recent years, one of the notable advancements in chatbot research is the work by [PERSON] et al. (2020) [1]. This paper introduces GPT-3 (Generative Pre-trained Transformer 3), a language model with a staggering 175 billion parameters. GPT-3 demonstrates remarkable language understanding and generation abilities by producing human-like responses. Prior to that, the work of [PERSON] et al. (2019) [2] introduced DialoGPT, a variant of the GPT-2 model fine-tuned for generating responses in conversations. By fine-tuning on conversational data, DialoGPT enhances the fluency and relevance of the generated responses. Furthermore, it is also worth mentioning a chatbot in the open research domain created by [PERSON] et al. (2020), named Meena [3], which utilizes adversarial training methods to generate interesting and contextually relevant responses. This research highlights the importance of creating meaningful conversations. Particularly in [4], [PERSON] et al. (2021)introduced the DALL-E system--an image generation system based on text descriptions. This opens up the possibility of expanding the communication capabilities of chatbots beyond text, allowing for the display and comprehension of images. These research advancements demonstrate the unique evolution of chatbots, pushing the boundaries of what AI-driven entities can achieve. From comprehensive language understanding to image generation and maintaining meaningful conversations, these breakthroughs highlight the potential of chatbots in diverse applications. Chatbots have come a long way from rule-based systems, evolving into advanced AI entities with the ability to comprehend context, emotions, and specialized domains. Breakthrough studies like the examples of GPT-3, DialoGPT, Meena, and DALL-E have showcased significant progress in the capabilities of chatbots. Constructing a chatbot for a specific domain presents substantial challenges in industry knowledge and methods. It necessitates a profound grasp of context, terminology, and industry trends to deliver precise information. The flexible application of natural language processing and machine/deep learning methods is crucial, utilizing efficient models and algorithms. This paper introduces EduChat, a chatbot system designed to address questions related to universities. EduChat is an artificial intelligence application that combines rule-based methods with novel machine learning techniques to automatically answer common queries related to universities, academic programs, admission procedures, student life, and other related topics. Within EduChat, we have also integrated the ChatGPT system to enhance the coverage of conversation and improve the interaction between humans and machines. To understand the conversations sent to EduChat, we process these sentences in a three-level sequence. First, the text sentences sent to the system are processed by a rule-based system defined by the Definite Clause Grammar (DCG) syntax [5; 6]. If the DCG rules fail to recognize the syntactic and semantic structures of the dialogues, an enhanced algorithm built upon the random forest machine learning approach [7] is used to classify these sentences into one of the 25 topic classes. Through experimentation, we establish a classification threshold based on accuracy. Sentences with classification scores below the threshold are processed by ChatGPT within the system's context.