Revolutionizing Campus Communication: NLP-Powered University Chatbots

Artificial intelligence (AI) based chatbots leverage programmed software instructions to simulate human speech and user interaction. These versatile tools can be employed in various domains, from managing smart home devices to providing personal virtual assistants. They can also be useful in responding to common queries and can make information easier to access. In response to this need, we developed a specialized chatbot tailored for the academic environment by training an NLP model to answer frequently asked questions (FAQs) the need of searching through the university website. The main goal is to optimize user engagement and streamline information retrieval within a university setting. By employing ML and NLP techniques, we enhance the chatbot's capabilities, enabling it to provide effective and precise answers, contributing to a more seamless and efficient experience for users seeking information about the university. The study discusses the pivotal decision-making process between implementing a custom neural network and the BERT model. Through a comparative analysis, the custom neural network emerges as the preferred solution, displaying efficiency, quick deployment, and superior accuracy in handling task-specific queries. While BERT presents unparalleled versatility in natural language processing, its resource-intensive pre-training, and challenges in adapting to the intricacies of the university-specific dataset limit its efficiency in this application. This research emphasizes the importance of customization to meet the unique demands of a university chatbot, providing valuable insights for developers seeking to strike a balance between efficiency and specialization in similar applications.

@article{Ramakrishnan2024,
  title     = {Revolutionizing Campus Communication: NLP-Powered University Chatbots},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Ritu Ramakrishnan and Priyanka Thangamuthu and Austin Nguyen and Jinzhu Gao},
  doi       = {10.14569/IJACSA.2024.0150606},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150606}
}