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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM55065

Smart Complaint Registration Chatbot CampusCare Bot

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM55065

Volume:

9 |

12 |

Dec 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM55065

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

RNM

Rutuja Nandulal Mistry

DKCJ

Dr. Kalyan C. Jagdale

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Abstract

This paper presents the design, development, and thorough validation of CampusCare Bot, a smart complaint-registration chatbot created to streamline and improve how issues are resolved across a university campus. Powered by Natural Language Processing (NLP), the system can instantly interpret unstructured text-based complaints and categorize them—such as Maintenance, IT, or Safety—with an accuracy of 92%.The bot is built using a scalable Python/Flask microservices architecture, guided by design automation principles to maintain structural consistency and speed up deployment. To evaluate the system’s performance, the research includes both quantitative and qualitative analyses. A Prophet time-series model is used to forecast user activity and peaks in complaint volume, helping administrators manage resources proactively. Meanwhile, insights from user interviews show that the bot improves transparency and trust, thanks to its clean, unified design and real-time ticket-tracking features. Overall, CampusCare Bot stands as a strong example of how conversational AI can be integrated into essential administrative processes, setting a new benchmark for operational efficiency and student support in smart-campus initiatives.

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Cite This Article

Rutuja Nandulal Mistry, Dr. Kalyan C. Jagdale, "Smart Complaint Registration Chatbot CampusCare Bot," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 12, Dec 2025. https://doi.org/10.55041/IJSREM55065

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10/11/2025

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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