An Interactive Chatbot System for College Enquiry

Abstract

The embedding of chatbots in different applications has transformed support systems for users, especially in education. In this paper, an AI-powered chatbot designed for university students to solve academic and administrative-related questions is introduced. Based on artificial intelligence (AI) and natural language processing (NLP), the system is developed to understand user questions and provide accurate, context-based answers. The chatbot functions as a virtual assistant with a formal model of communication and a database of pre-established answers to respond to questions pertaining to admissions, timetables, and other college matters. With the aid of sophisticated algorithms, the system processes user input, detects intent, and formulates suitable responses independently. The results illustrate the capabilities of the chatbot to support student services, decrease response times, and enhance overall user satisfaction. The study indicates the revolutionary potential of AI-based chatbots in educational settings as a scalable solution for the efficient handling of student queries.

Introduction

Educational institutions often receive numerous student queries regarding admissions, courses, and administrative processes. Manually managing these enquiries is inefficient. This project presents an AI-powered chatbot developed using PHP and MySQL, integrated with OpenAI’s NLP API, to automate responses and improve communication within colleges.

Unlike traditional rule-based or purely generative chatbots, this system uses a hybrid model that selects the best response strategy based on the type of query. It aims to reduce response time, lessen the burden on administrative staff, and provide instant, accurate answers to students.

Key Components:

• Literature Review: Highlights prior studies showing that while chatbots enhance student engagement, challenges remain in natural language understanding and personalization.

• Research Insights: Random Forest classifiers outperformed Naive Bayes in handling complex queries, while older AIML-based bots showed limited success with informal questions.

• Methodology:

  • Data collection from institutional FAQs and student queries

  • Chatbot built using PHP and MySQL

  • OpenAI’s NLP API used for natural language processing

  • Web-based deployment for real-time accessibility

  • Ongoing improvement through user feedback and testing

• Challenges Identified:

  • Limited natural language flexibility

  • Scalability concerns

  • Dependence on high-quality data

  • Integration limitations with third-party platforms

Results:

The chatbot achieved 85% accuracy with an average response time of 1–2 seconds, effectively handling repetitive student enquiries and improving user experience.

Conclusion

The chatbot effectively meets the requirement of real-time and correct information in academic institutions. Though it has drawbacks such as narrow natural language understanding, future development like incorporating NLP and enlarging the knowledge base can help it become more effective. In general, the projectdemonstrates the capability of chatbots to make communication more efficient and enhance the operational efficiency of colleges.

References

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Copyright

Copyright © 2025 Bushra Lalkot, Gazala Shaikh, Shakila Siddavatam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.