International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI Based College Enquiry Chatbot |
|---|---|
| ABSTRACT | The AI-Based College Enquiry Chatbot is an intelligent and automated system developed to simplify the process of handling enquiries related to educational institutions. Students, parents, and other users often require information regarding admissions, courses, fee structures, scholarships, placements, academic calendars, examination schedules, and campus facilities. Traditional enquiry methods, such as phone calls, emails, and in-person visits, are time-consuming and require significant manual effort from administrative staff. These methods may also lead to delays, inconsistencies, and limited accessibility outside working hours. To overcome these challenges, this project implements an AI-powered chatbot based on the Retrieval-Augmented Generation (RAG) architecture. The system collects information from official college documents, including PDF files, and processes the content by extracting text and dividing it into smaller sections. These sections are then transformed into vector embeddings and stored in a vector database to enable efficient information retrieval. Whenever a user submits a query, the chatbot identifies the most relevant information using semantic search techniques and generates an accurate and context-aware response through a Large Language Model (LLM). The backend of the system is developed using Python and FastAPI, while the frontend is built using React.js (Next.js) and Tailwind CSS to create a responsive and user-friendly interface. PostgreSQL with the pgvector extension is used for efficient data storage and retrieval. Additional development tools, such as GitHub, Postman, Netlify, and Railway, are used to support deployment, testing, and maintenance of the system. The proposed system significantly reduces the workload of administrative staff, provides consistent and reliable information, and offers twenty-four-hour accessibility to users. The chatbot improves communication efficiency, enhances user satisfaction, and provides a scalable solution for modern educational institutions. Future improvements may include Optical Character Recognition (OCR), conversation memory, hybrid search methods, role-based authentication, and analytical dashboards to further improve the overall performance of the system. |
| TITLE | |
| AUTHOR | TANIYA ANIL BHISE, ANISHA ATMARAM GAWADE, POOJA RUPESH KESARKAR, ADVITA TUSHAR SHINDE, PROF.P. H. DONGARE Department of Computer Engineering, Yashwantrao Bhonsale Institute of Technology, Sawantwadi, MH, India Guide, Department of Computer Engineering, Yashwantrao Bhonsale Institute of Technology, Sawantwadi, MH, India |
| VOLUME | 187 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1408008 |
| pdf/8_AI Based College Enquiry.pdf | |
| KEYWORDS | |
| References | [1] E. Rich and K. Knight, Artificial Intelligence, 3rd Edition, Tata McGraw Hill, 2017. [2] Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd Edition, Pearson, 2010. [3] J. Weizenbaum, “ELIZA—A Computer Program for the Study of Natural Language Communication,” Communications of the ACM, Vol. 9, Issue 1, pp. 36–45, 1966. [4] A. Shawar and E. Atwell, “Chatbots: Are They Really Useful?”, Journal for Language Technology and Computational Linguistics, 2007 [5] FastAPI. (n.d.). FastAPI Official Documentation. https://fastapi.tiangolo.com/ [6] Hugging Face. (n.d.). Hugging Face Transformers Documentation. https://huggingface.co/docs/transformers [7] Gradio. (n.d.). Gradio User Documentation. https://gradio.app/ [8] Facebook AI Research. (n.d.). FAISS: Library for Efficient Similarity Search. https://github.com/facebookresearch/faiss [9] OpenAI. (n.d.). Introducing GPT Models and Usage. https://openai.com/research |
