AI-based chatbot

Over the years, Artificial Intelligence (AI) chatbots have evolved significantly, moving from basic rule-based systems to more advanced natural language processing models capable of handling complex queries and generating human-like speech. However, despite these advancements, there are still...

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Bibliographic Details
Main Author: Tan, Brendon
Other Authors: Vidya Sudarshan
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
AI
RAG
Online Access:https://hdl.handle.net/10356/181089
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-181089
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spelling sg-ntu-dr.10356-1810892024-11-14T11:44:26Z AI-based chatbot Tan, Brendon Vidya Sudarshan College of Computing and Data Science vidya.sudarshan@ntu.edu.sg Computer and Information Science AI LLMS RAG Over the years, Artificial Intelligence (AI) chatbots have evolved significantly, moving from basic rule-based systems to more advanced natural language processing models capable of handling complex queries and generating human-like speech. However, despite these advancements, there are still limitations, such as hallucinations and lack of consistency. The goal of this project is to bridge the gap between existing AI chatbot capabilities and the specific needs of educational content delivery, offering a solution that promotes both effective learning and engagement. This project focuses on the development of an AI-powered chatbot designed to generate contextually relevant quizzes from lecture slides. By integrating modern machine learning techniques like Retrieval-Augmented Generation (RAG), the chatbot efficiently processes and retrieves lecture content to create personalized quizzes that align with the specific module and topic selected by the user. The chatbot aims to improve student learning by offering accessible, on-demand quizzes, fostering engagement and reinforcing understanding of key concepts. Bachelor's degree 2024-11-14T11:44:26Z 2024-11-14T11:44:26Z 2024 Final Year Project (FYP) Tan, B. (2024). AI-based chatbot. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181089 https://hdl.handle.net/10356/181089 en SCSE 23-1116 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
AI
LLMS
RAG
spellingShingle Computer and Information Science
AI
LLMS
RAG
Tan, Brendon
AI-based chatbot
description Over the years, Artificial Intelligence (AI) chatbots have evolved significantly, moving from basic rule-based systems to more advanced natural language processing models capable of handling complex queries and generating human-like speech. However, despite these advancements, there are still limitations, such as hallucinations and lack of consistency. The goal of this project is to bridge the gap between existing AI chatbot capabilities and the specific needs of educational content delivery, offering a solution that promotes both effective learning and engagement. This project focuses on the development of an AI-powered chatbot designed to generate contextually relevant quizzes from lecture slides. By integrating modern machine learning techniques like Retrieval-Augmented Generation (RAG), the chatbot efficiently processes and retrieves lecture content to create personalized quizzes that align with the specific module and topic selected by the user. The chatbot aims to improve student learning by offering accessible, on-demand quizzes, fostering engagement and reinforcing understanding of key concepts.
author2 Vidya Sudarshan
author_facet Vidya Sudarshan
Tan, Brendon
format Final Year Project
author Tan, Brendon
author_sort Tan, Brendon
title AI-based chatbot
title_short AI-based chatbot
title_full AI-based chatbot
title_fullStr AI-based chatbot
title_full_unstemmed AI-based chatbot
title_sort ai-based chatbot
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/181089
_version_ 1816859017207611392