International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
RESEARCH ON CHATUR: CHATBOT FOR COLLEGE Prof.Gopika Fattepurkar1, Prathamesh Govekar2, Aniket Pore3, Niranjan Khude4, Gunjan Gandhi5 Prof.Gopika Fattepurkar Artificial Intelligence and Data Science Department, Ajeenkya D. Y. Patil School of Engineering, Lohegaon, Pune, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In today’s fast-paced college environment,
or complex user queries—the need for a more adaptive and intelligent solution led to the integration of the Rasa framework in the second phase of development. Rasa, an open-source machine learning framework for building contextual AI assistants, introduces Natural Language Understanding (NLU) and dialogue management capabilities, enabling the chatbot to move beyond static responses and engage in dynamic, multi-turn conversations.
students and faculty require timely access to accurate information on a variety of topics, such as admissions, course details, fees, and campus facilities. Traditional methods of information dissemination, such as websites, emails, and in- person consultations, often lead to delays and inefficiencies, causing frustration among users and increasing administrative workloads. To address this challenge, we developed Campus Query: A Q&A Chatbot for College, an intelligent conversational agent designed to provide real- time, accurate answers to frequently asked questions. This paper presents the design, development, and implementation of the chatbot, following a structured, two-phase approach. The initial phase employs a rulebased system, where the chatbot is programmed to handle common queries through predefined responses, keyword matching, and basic conversation flow. Although effective for standard queries, the limitations of this approach in handling more nuanced questions necessitated the transition to the second phase. In the second phase, we integrate the Rasa framework, which enhances the chatbot with natural language understanding (NLU), enabling it to recognize user intents, extract entities, and manage complex, multiturn conversations. This phase introduces greater flexibility, allowing the chatbot to handle more varied user inputs and follow-up interactions in a conversational context. Through rigorous testing and user feedback, the chatbot has demonstrated its ability to improve information accessibility and alleviate the burden on administrative staff. By providing immediate responses to common queries, the chatbot enhances the overall user experience for students and faculty. Future enhancements will focus on incorporating more advanced features, such as dynamic content retrieval and further personalization, to expand the chatbot’s capabilities and adaptability.
In this phase, the chatbot is enhanced to comprehend user intents and extract key entities from queries, allowing it to provide more relevant and personalized responses. Unlike the rigid pattern-matching approach of rule-based systems, Rasa leverages intent classification and entity recognition to interpret user input more flexibly. This shift empowers the chatbot to handle a broader range of questions, even when phrased in unexpected or informal ways, significantly improving the overall interaction quality. The implementation of Rasa also supports contextual flow and memory, enabling the chatbot to maintain the state of a conversation and deliver follow-up responses based on prior exchanges. This conversational depth facilitates more natural and efficient user experiences, simulating a human-like dialogue that is both intuitive and informative. By automating complex inquiries and reducing the need for manual intervention, this phase plays a crucial role in streamlining communication within the college environment. Ultimately, the integration of the Rasa framework transforms Campus Query from a basic FAQ responder into a sophisticated conversational agent. It enhances both the scalability and adaptability of the system, aligning with the broader goal of improving information accessibility for students and faculty while reducing administrative workload.
1.1Related Work
As the limitations of rule-based systems became apparent—particularly their inability to handle ambiguous
The adoption of chatbots across various sectors has seen significant growth in recent years, with notable uses in customer support, healthcare, and educational settings. These tools are increasingly leveraged to automate repetitive tasks and offer users immediate access to relevant information. Within the landscape of higher education, chatbots present an opportunity to enhance how information is distributed—particularly for common inquiries related to admissions, academic programs, and
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Key Words: Chatbot, Natural Language Understanding (NLU), Rasa Framework, Q&A System, Campus Query, Information Retrieval, Multi-Turn Conversations, Higher Education, Intelligent Agent.
1.Introduction
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