Web-based College Query Chatbot System Using Nlp And Retrieval-based Response Generation

• Author(s):

  Hemapriya P | Maghema R.A. | Madhumathi R | Dhanushya L | Mrs. M. Agalya

• Keywords:

  Natural Language Processing, Chatbot, Intent Classification, Web Application, Flask, React.js, SQL Server, Educational Technology, Query Automation

• Abstract:

  Managing Institutional Queries Efficiently Remains A Persistent Challenge For Colleges And Universities, Particularly When Student Numbers Are Large And Available Support Staff Are Limited. Existing Approaches Such As Physical Helpdesks, Email Threads, And Telephone Helplines Are Restricted In Availability, Inconsistent In Quality, And Unable To Scale During High-demand Periods Such As Admissions Or Examination Seasons. This Paper Presents The Design And Development Of A Web-Based College Query Chatbot System Tailored For Vivekanandha College Of Technology For Women. The Proposed System Combines A React.js Frontend, A Python Flask Backend, And A Microsoft SQL Server Database To Deliver An Always-available, Automated Query-resolution Platform. A Lightweight Natural Language Processing Pipeline Handles Query Understanding Through Lowercase Normalization, Stop Word Removal, And Keyword-based Intent Matching, Without The Use Of Any Machine Learning Model Or Deep Learning Framework. Responses Are Retrieved From A Structured Database Of Predefined Intent-response Pairs. Queries That Cannot Be Matched Automatically, Or That Involve Sensitive Matters, Are Escalated To Appropriate Human Staff Through A Built-in Escalation Mechanism. Verification And Validation Testing Confirmed That The System Correctly Handles All Defined Intent Categories, Provides Consistent And Accurate Responses, And Appropriately Escalates Unrecognized Queries. The System Significantly Reduces The Routine Workload On Administrative Personnel While Ensuring Round-the-clock Student Access To Institutional Information.

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Other Details

• Paper id:

  IJSARTV12I5105308

• Published in:

  Volume: 12 Issue: 5 May 2026

• Publication Date:

  2026-05-09

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