Design and Implementation of an AI Chatbot for University Admission Enquiries

AI chatbot trained on a Nigerian university's prospectus to answer admission and academic enquiries 24/7.

About this project

This project trains and deploys a chatbot using an LLM with retrieval-augmented generation (RAG) over a university's prospectus, fees schedule, and FAQs. It replies on the school website and WhatsApp.

Suggested tech stack

Python
LangChain
OpenAI / Gemini
ChromaDB / Pinecone

Chapters 1–5 outline

Chapter 1

Introduction: background to AI chatbots and RAG, statement of the problem (admission offices are overwhelmed by repetitive enquiries every academic session), aim and objectives, research questions, scope, significance of the study, and definition of terms.

Chapter 2

Literature Review: theoretical framework, review of related works on AI chatbots and RAG, gaps in existing studies, and a summary positioning this project.

Chapter 3

Methodology / System Analysis and Design: Python + LangChain + an LLM API + pinecone/chromadb for vector search. Includes data collection method, system requirements, use-case and architecture diagrams (or population, sample size, and instrument).

Chapter 4

Implementation and Results: a deployed chatbot integrated with the university website and WhatsApp Cloud API. Presentation of findings, testing, evaluation, and discussion of results.

Chapter 5

Summary, Conclusion and Recommendations: key findings, contribution to knowledge, limitations, and recommendations for further research.

Get this project done — chapters, code, defence support

Final Year writes the full project for you. Original content, on time, with chat support up to defence day.

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