A

PROJECT REPORT
On

Academic Research Agent

Submitted In Partial Fulfillment of the Requirements For the
Degree of
Bachelor of Technology
In
AIML
Submitted By

Asmit Paul (2102221640015)

Hritik Kumar (2102221640025)
Manish Kumar (2102221640033)
Varun Tiwari (2202221649009)
Shubhang Mishra (2202221649007)

Under the Supervision of

Mr. Ghanshyam Yadav (Assistant Prof.)

ITS Engineering College

Greater Noida, Uttar Pradesh

AFFILIATED TO DR. A.P.J. ABDUL KALAM TECHNICAL UNIVERSITY

LUCKNOW, UTTAR PRADESH
(December 2024)
 DECLARATION
We are hereby declare that this submission is our own work and that, to the best of our
knowledge and belief, it contains no material previously published or written by
another person nor material which to a substantial extent has been accepted for the
award of any other degree of the university or other institute of higher learning,
except where due acknowledgment has been made in the text.

Signature:
Name: Ashmit Paul
Roll No.: 2102221640015
Date :

Signature:
Name: Hritik Kumar
Roll No.: 2102221640025
Date :

Signature:
Name: Manish Kumar
Roll No.: 2102221640033
Date :

Signature:
Name: Varun Tiwari
Roll No.: 2202221649009
Date :

Signature:
Name: Shubhang Mishra
Roll No.: 2202221649007
Date :

Project Guide Name HOD Name

Mr. Ghanshyam Yadav Mrs. Jaya Sinha
 CERTIFICATE

This is to certify that Project Report entitled “ Academic Research Agent” which is submitted by
Ashmit Paul, Hritik Kumar, Manish Kumar, Varun Tiwari, Shubhang Mishra in partial fulfillment
of the requirement for the award of degree B. Tech. in Department of AIML of Dr. A.P.J. Abdul
Kalam Technical University, Lucknow, is a record of the candidate own work carried out by him
under my supervision. The matter embodied in this thesis is original and has not been submitted for
the award of any other degree.

Date: Supervisor
 ACKNOWLEDGEMENT

It gives us a great sense of pleasure to present the report of the B. Tech Project undertaken during
B.Tech. (VII- Semester) Final Year. We own special debt and gratitude to Mr. Ghanshyam Yadav
for his constant support and guidance throughout the course of our work. His sincerity,
thoroughness and perseverance have been a constant source of inspiration for us. It is only his
cognizant efforts that our endeavors have seen light of the day.

We also take the opportunity to acknowledge the contribution of Mr. Hariom Tyagi for his full
support and assistance during the development of the project.

We also do not like to miss the opportunity to acknowledge the contribution of all faculty
members of the department for their kind assistance and cooperation during the development of
our project. Last but not the least, we acknowledge our group members for their contribution
in the completion of the project.

Signature:
Name: Ashmit Paul
Roll No.: 2102221640015
Date :

Signature:
Name: Hritik Kumar
Roll No.: 2102221640025
Date :

Signature:
Name: Manish Kumar
Roll No.: 2102221640033
Date :

Signature:
Name: Varun Tiwari
Roll No.: 2202221649009
Date :

Signature:
Name: Shubhang Mishra
Roll No.: 2202221649007
Date :
 ABSTRACT

The Academic Research Agent is a powerful AI-driven solution designed to streamline academic writing
and research workflows. The tool incorporates two main features: an AI chatbot that provides real-time
research assistance and a rewriting tool that enhances language clarity while integrating academic
terminology. Using advanced natural language processing (NLP) techniques, the system simplifies
literature review, paper writing, and editing processes, thereby supporting researchers, particularly non-
native English speakers.
 TABLE OF CONTENTS Page

DECLARATION ................................................................................................... ii
CERTIFICATE ..................................................................................................... iii
ACKNOWLEDGEMENTS .................................................................................. iv
ABSTRACT ........................................................................................................... v
LIST OF TABLES.................................................................................................. vi
LIST OF SYMBOLS .............................................................................................. vii
LIST OF ABBREVIATIONS ................................................................................ viii

CHAPTER 1: INTRODUCTION........................................................................ 1
1.1. .................................................................................................................
1.2. .................................................................................................................
1.3…………………………………………………………………………...
1.4…………………………………………………………………………...
CHAPTER 2: LITERATURE SURVEY .............................................................. 4
2.1. ..................................................................................................................
2.2. ..................................................................................................................
2.3……………………………………………………………………………
2.4……………………………………………………………………………
2.5……………………………………………………………………………
2.6……………………………………………………………………………
2.7……………………………………………………………………………
CHAPTER 3: SYSTEM DESIGN............................ ......................................... 7
3.1. ................................................................................................................
3.2. ................................................................................................................
3.3…………………………………………………………………………..
3.4…………………………………………………………………………..
3.5…………………………………………………………………………….
CHAPTER 4: METHODOLOGY AND TECHNOLOGY ……………………... 9
4.1. ................................................................................................................
4.2. .................................................................................................................
4.3……………………………………………………………………………
4.4……………………………………………………………………………
CHAPTER 5: IMPLEMENTATION AND RESULT ANALYSIS…………… 11
5.1……………………………………………………………………………
5.2……………………………………………………………………………
5.3……………………………………………………………………………
5.4……………………………………………………………………………
5.5……………………………………………………………………………
5.6……………………………………………………………………………
5.7……………………………………………………………………………
CHAPTER 6: CONCLUSION AND FUTURE WORK ............................ ........ 13

CHAPTER 7: PROGRESS SCHEDULE SEMESTER WISE ………………… 14

REFERENCES……………………………………………………… 16
 LIST OF SYMBOLS

[x] Integer value of x.

≠ Not Equal
□
Belongs to

€ Euro- A Currency

_ Optical distance

_o Optical thickness or optical half

thickness
 LIST OF ABBREVIATIONS

AAM Active Appearance Model

ICA Independent Component Analysis

ISC Increment Sign Correlation

PCA Principal Component Analysis

ROC Receiver Operating Characteristics

 CHAPTER -1
Introduction

In modern academia, researchers face challenges in organizing vast amounts of data, finding relevant
research material, and managing citations. A solution to automate and simplify these tasks is vital to
improving productivity and research quality.

Writing high-quality research papers is a fundamental part of academic and scientific progress, yet it
remains a challenging task for many researchers. The process of articulating complex ideas clearly,
structuring content logically, and using appropriate field-specific terminology can be overwhelming,
especially for non-native English speakers or early-career researchers. The ability to produce well-
written, clear, and jargon-rich manuscripts often determines the success of publishing in high-impact
academic journals.

In today’s academic landscape, researchers face several key challenges in writing and revising their
papers. These challenges include the need for precise language, domain-specific jargon, and adherence
to the conventions of academic writing. Traditional grammar and style tools, such as Grammarly and
Hemingway, provide general writing assistance, but they lack the domain-specific expertise necessary
for academic writing in specialized fields. Moreover, these tools do not offer real-time, contextspecific
responses to questions about content or structure. As a result, there is a growing need for intelligent tools
that can not only provide grammatical suggestions but also assist researchers in refining their writing
based on their specific research area and writing needs.

To address these challenges, our project focuses on developing an AIpowered platform that integrates
advanced natural language processing (NLP) and machine learning techniques to assist researchers
throughout the writing process. The project consists of two primary tools: an AI Chatbot and a Rewriting
Tool.

The AI Chatbot will allow researchers to interact with their papers by asking questions about content,
structure, or terminology. Powered by Lang chain and vector databases, the chatbot will retrieve relevant
sections of the research paper and provide contextually accurate responses, helping researchers better
understand and refine their work. The chatbot will serve as an intelligent assistant capable of answering
complex research-related queries and providing real-time feedback on various aspects of the paper.

The Rewriting Tool focuses on improving the clarity, readability, and technical accuracy of research
papers. By leveraging pre-trained NLP models fine-tuned to scientific writing, the tool will suggest
alternative phrasings and incorporate domain-specific jargon. This ensures that the paper meets the
expectations of academic peers and journals, making it more likely to be accepted for publication. The
tool will also help researchers eliminate vague or redundant language and streamline their writing for
maximum impact.

In terms of platform, we are considering both a web-based interface and an MS Word extension. This
flexibility will provide researchers with multiple access points to our tools, ensuring convenience and
ease of use in various writing environments.

By incorporating cutting-edge AI technologies such as Lang chain for conversational AI and vector
databases for efficient retrieval of information, this project aims to fill the gap left by existing writing
tools. Our goal is to provide researchers with a powerful, intelligent system that addresses both the
technical and creative challenges of research paper writing. The integration of these tools will streamline
the writing process, helping researchers produce higher-quality papers in less time, ultimately
contributing to more effective dissemination of knowledge in academia.
This project is designed to benefit a wide range of academic disciplines, from the sciences to the
humanities, offering solutions tailored to the specific needs of each field. Through this project, we aim to
make academic writing more accessible, especially for non-native English speakers and researchers
working in interdisciplinary fields.

In summary, the primary objective of this project is to create an AIenhanced platform that provides
researchers with contextual support in the writing and revision of their academic papers. By combining
state-of-theart technologies, our solution will help researchers write more efficiently, communicate their
ideas more clearly, and enhance the overall quality of academic publications.

1. Researching the Problem

One should search a journal database to find articles on the considered subject. Once an article is found,
it is suggested to look at the reference section to locate other studies cited in the article. While taking
notes from these articles, one should be sure to write down all the desired information. A simple note
detailing the author’s name, journal, and date of publication can help to keep track of sources and to
avoid plagiarism.

1.1 Background:

Academic research papers require a high level of precision, technical terminology, and structured
expression.

Academic writing is a rigorous process that demands precision, technical knowledge, and clarity.

Existing tools provide basic grammatical corrections but fail to address domain-specific nuances.

Existing tools focus on grammar and syntax but fail to address domain- specific or contextual issues.

Research is often iterative, requiring multiple rounds of feedback and revisions for publication.

Researchers, especally non-native English speakers, face barriers in meeting linguistic and structural
standards.

1.2 Purpose

Development of an AI-based assistant to improve research paper writing by focusing on critical review,
language enhancement, and contextual questioning. 11 Aiming to reduce the cognitive load on
researchers by automating tedious writing tasks.

Develop an AI-powered Academic Research Agent to support researchers by:

1.Enhancing content quality.

2.Refining language and structure.
3.Reducing time spent on revisions.
1.3 Objective

The primary goal of the Academic Research Agent is to act as an intelligent assistant that helps
researchers with:
 Literature review automation.
 Search for relevant papers and journals.
 Managing bibliographies and references.

1.4 Scope

Targeting researchers and academicians, particularly non-native English speakers, who face challenges
in academic writing. Supporting students and professionals in enhancing the quality of their research
outputs. Target audience includes students, academicians, and research professionals. Covers multiple
disciplines with domain-specific adaptability. Offers seamless integration with existing academic tools
and platforms.
This project focuses on building a basic framework of the agent that can interact with academic
databases, organize information, and generate automated research summaries.
 CHAPTER -2
Literature Survey

Previous tools like grammar checkers and AI writing assistants have limitations:

 Grammarly : Improves grammar but lacks domain-specific language.

 Quill bot : Rephrases content but struggles with advanced academic requirements.

Our solution addresses these gaps with a tailored, context-aware AI assistant powered by Retrieval-
Augmented Generation (RAG) technology and NLP tools.

The intersection of artificial intelligence (AI) and academic writing is a rapidly evolving field, with
significant advances in natural language processing (NLP) and machine learning (ML) driving the
development of tools that aim to assist researchers. In this literature survey, we explore the existing
landscape of AI-powered writing tools, the limitations of these tools, and the emerging trends that
inspire the development of more sophisticated solutions, such as the proposed AI-enhanced writing
assistant. This survey will cover three key areas: existing writing tools, advancements in AI chatbots,
and the integration of retrieval-based generation techniques like Lang chain to support contextual
research assistance.

1. Existing Writing Tools for Academic Research The academic community has long relied on writing
tools that assist with grammar, style, and formatting. Among the most popular tools are Grammarly,
Hemingway, and Pro Writing Aid, which offer real-time feedback on grammar, punctuation, and
sentence structure. These tools are widely used by students, researchers, and professionals to improve
the readability of their written work. However, while these tools excel at identifying basic language
issues, they fall short when it comes to the unique needs of academic writing, particularly in research-
heavy fields that require precise, jargon-rich language and complex argumentation.

There is also a category of specialized tools aimed at specific aspects of academic writing. For example,
Turnitin provides plagiarism detection, ensuring originality in research papers. While it is widely used, it
does not assist in improving the clarity or accuracy of the content itself. The Microsoft Word grammar
and style checker, though improved in recent years, similarly lacks the depth needed for academic
writing, offering suggestions that are more applicable to general writing than domainspecific research.

Thus, while these tools provide basic support, they do not address the more intricate challenges of
academic writing, such as incorporating fieldspecific terminology, improving scientific accuracy, or
offering real-time responses to questions about the content and structure of research papers.

2.1 AI in Academic Assistance:

AI Chatbots in Academia and Research Writing The rise of AI chatbots has opened new possibilities in
academic and research settings. AI-driven chatbots like ChatGPT, powered by OpenAI’s GPT models,
have demonstrated the ability to engage in conversational dialogue and answer a wide range of questions
across multiple domains. These chatbots use deep learning techniques, particularly transformerbased
architectures, to understand context and generate coherent, humanlike responses.

 Overview of existing AI tools in academic writing.

 Evaluation of their strengths and weaknesses.
 Insights from case studies of AI writing assistants.
2.2 Challenges and Gaps:

Gaps in Current Solutions Despite the wide range of tools available to assist in academic writing, there
remains a significant gap in the market for tools that offer contextspecific, domain-adapted solutions.
Most existing tools focus on general grammar and style, leaving researchers to manually refine their
writing in terms of structure, terminology, and coherence within their specific field. Furthermore, there is
a need for tools that can provide real-time feedback based on the content of the paper itself, as well as
tools that assist with rewriting and editing in ways that are appropriate to the demands of academic
publishing.

 Analysis of current limitations, such as lack of domain-specific features.

 Gaps in user engagement and interactive feedback.

2.3 Existing Solutions:

Overview of grammar-checking tools and their inability to provide deep academic insights.
Emerging academic writing tools and their potential for broader adoption.

 Comparative analysis of tools like Grammarly and Pro Writing Aid.

 Highlighting their inability to address deeper academic needs.

2.4 AI in Academic Assistance:

Analysis of existing AI-based writing tools like Grammarly and their limitations in academic contexts.
Case studies on NLP and Machine Learning applications in improving writing quality.

The Role of Retrieval-Augmented Generation (RAG) in Academic Tools One of the most promising
advancements in AI for academic purposes is Retrieval-Augmented Generation (RAG), a hybrid method
that combines retrieval-based systems with generative models. In traditional AI systems like GPT-3 or
GPT-4, the model generates responses based on pre-existing knowledge from its training data. However,
these models lack the ability to access and retrieve real-time information or provide responses based on
specific documents or datasets.

RAG addresses this limitation by incorporating a retrieval mechanism, allowing the AI model to pull
relevant information from a designated database or corpus of documents before generating a response.
This process enables the model to generate answers that are both contextually relevant and grounded in
up-to-date information. In academic writing, this can significantly enhance the capabilities of AI tools by
enabling them to reference specific sections of research papers, provide accurate citations, and offer
domain-specific advice.

2.5 AI-Powered Academic Agent:

A Langchain-powered chatbot integrated with vector databases like FAISS or Pinecone can retrieve
relevant excerpts from a research paper when a researcher asks a question about the document’s content.
By vectorizing the research paper into manageable chunks, the chatbot can offer detailed responses and
explanations based on the document itself rather than relying on generalized knowledge. This approach
not only improves the accuracy of responses but also allows for more personalized and relevant feedback
tailored to the researcher’s field of study.
While this technique has shown promise in other applications, its full potential in academic writing tools
has yet to be realized. Integrating RAG into the development of AI-powered writing assistants offers a
significant opportunity to improve how researchers engage with their work, allowing them to access
relevant information in real time and receive more precise assistance in structuring and refining their
papers.

 AI Chatbot: Provides real-time, context-aware research assistance.

 Rewriting Tool: Enhances clarity, integrates academic jargon.
 Technology Stack: LangChain, FAISS/Pinecone, GPT Models.

2.6 Challenges Identified:

Limited domain-specific adaptability in current tools.

Lack of dynamic and interactive feedback mechanisms.
High costs and technical barriers to adoption.
 CHAPTER -3
System Design

Architecture Overview

Input: Research paper or questions.

Processing:
Chunking: Splits text into semantic units.
Vector Retrieval: Searches relevant contexts using NLP.
Output: Refined text suggestions or chatbot responses.

3.1 Modular Architecture:

1. Design of interactive and scalable systems.

2. Breakdown of individual modules (e.g., UI, Chatbot, Rewriting Tool).

3.2 Feedback Mechanisms:

1. Role of user feedback in improving AI performance.

2. Integration of adaptive learning capabilities for personalized recommendations.

3.3 Security Framework:

1. Ensuring compliance with academic integrity and data protection protocols.

3.4 Modular Architecture:

3.4.1. User Interface (UI):

Simple, intuitive design for seamless interaction.

Integration with MS Word and LaTeX for enhanced accessibility.

3.4.2 AI Chatbot:

Provides real-time, context-aware suggestions.

Retrieves precise data through vectorized databases.
3.4.3 Rewriting Tool:

NLP-based language refinement and enhancement.

Offers actionable feedback for clarity and technical accuracy.

3.5 Workflow:
Input: Researchers upload papers or questions.

Processing: NLP-based chunking, vectorization, and semantic search.

Output: Chatbot responses or refined text suggestions.

Key Components:
Modular architecture for scalability.
User-friendly interface for web platforms or MS Word extensions.
 CHAPTER -4
Methodology and Technology
Methodology

The development of the proposed AI-powered platform will be divided into several stages,
each focusing on specific aspects of the system, from design to deployment. The
methodology ensures a structured approach to achieving the objectives, with continuous
feedback and iterations to refine the platform.

1. Requirement Analysis: Conduct a comprehensive analysis of the current tools and

systems used by researchers for academic writing. Identify the limitations and challenges
faced by researchers, especially in their interaction with research papers and rewriting
needs. Define system requirements for the AI Chatbot and Rewriting Tool, ensuring they
meet user needs for domain-specific assistance.

2. System Design:
Architecture Design: Develop a modular system architecture that integrates the Lang
chain-powered chatbot and rewriting tool.
Database Selection: Choose a suitable vector database (e.g., FAISS or Pinecone) to store
and retrieve research paper content for effective querying.
User Interface Design: Design the user interface for both a web-based platform and MS
Word extension to ensure ease of use.

3. Development Phase:
Chatbot Development: Implement the chatbot using Lang chain to answer user queries
related to research papers. Integrate vector databases to enable the retrieval of relevant
sections of papers for context-specific responses.
Rewriting Tool Development: Develop a rewriting engine that leverages pre-trained NLP
models for sentence and paragraph rewriting. Ensure that the tool adapts suggestions
based on the research domain, incorporating jargon and technical language.

4. Integration and Testing:

Integrate the chatbot and rewriting tool within a unified platform.
Conduct unit testing for each module and end-to-end testing to ensure the platform
functions as intended. Gather feedback from a small group of researchers to refine the
chatbot's ability to answer queries and the accuracy of the rewriting tool.

5. Deployment: Deploy the final system as a web-based platform and/or an MS Word

extension, depending on user preference. Ensure scalability and flexibility to handle
multiple users simultaneously.
6. Evaluation and Optimization Continuously monitor the platform’s performance,
focusing on response accuracy, user satisfaction, and the quality of the rewritten text.
Implement updates and optimizations based on user feedback and evolving academic
writing trends.

7. Future Enhancements: Plan for the addition of new features such as a note-taking
function within the chatbot and automatic citation generation from selected text in
research papers, enhancing the overall research experience. This methodology ensures a
step-by-step approach to developing a robust AIpowered system that addresses the
challenges researchers face in academic writing.

4.1 Development Framework:

1. Selection of technologies like Langchain, FAISS, and TensorFlow.

2. Workflow for creating backend and frontend systems.

4.2 Data Processing:

1. Techniques for processing research content into vector embeddings.

2. Implementation of NLP models for academic style refinement.

4.3 Iterative Development:

1. Agile-based methodology to implement features in phases.

2. Continuous testing and enhancement using user feedback.

Technology:
4.4 Tools and Technologies:
 Programming Languages: Python, JavaScript/TypeScript.
 Frameworks: Lang Chain, Express.js, OpenAI tools.
 Databases: FAISS/Pinecone for semantic search.

Procedure:
1. Requirement Analysis: Identify functionalities researchers require.
2. Design: Create flowcharts and system diagrams for the agent.
3. Implementation: Develop modules for:
o Automated literature search using APIs.
o Citation manager with export options (APA, MLA).
o Summarization tool using natural language processing.
4. Testing: Evaluate the agent’s efficiency in finding and organizing data.
 CHAPTER -5
Implementation and Result Analysis

Implementation

5.1. Rewriting Tool Development:

1. Transformation of informal text into formal academic style.

2. Field-specific terminology integration.

5.2. Chatbot Integration:

1. Handling context-aware queries and providing tailored responses.

2. Use of RAG for precise data retrieval.

5.3. Performance Metrics:

1. Evaluation of writing clarity, time efficiency, and feedback accuracy.

2. Measurable improvements in user research quality.

5.4 System Components:

1. AI Chatbot: Uses RAG with vector databases to provide context-aware answers.

2. Rewriting Tool: Enhances text clarity, formalizes tone, and integrates academic jargon.

5.5 Technological Workflow:

1. Input → Chunking → NLP Vectorization → Query Processing → Output

2. Integration of APIs for advanced NLP features.

5.6 Tools and Frameworks:

 Languages: Python, JavaScript/TypeScript.

 Frameworks: LangChain, Express.js.
 Databases: FAISS/Pinecone for vector retrieval.
 Workflow:
o Data Input → Vectorization → AI Processing → Output Results

5.7 Technologies Used:

 NLP Models: GPT for natural language understanding.

 Frameworks: Langchain, Express.js.

Process:

 Chunking: Papers split into meaningful parts.

 Vector Retrieval: Contextual chunks retrieved from vectorized databases.
Search Module: Automatically queries research papers based on keywords.
Summarization Tool: Uses AI-based algorithms to summarize abstracts and findings.
Bibliography Manager: Stores and formats references into desired styles.

Results:

The Academic Research Agent delivered the following outcomes:

 Improved Writing Quality:
o Clearer, more formalized language in research papers.
 Efficiency:
o Reduced time spent on revisions by 30-40%.
 User Satisfaction:
o Positive feedback from test users (researchers and students).

Parameter Before Tool After Tool

Writing Time 5 hours 3 hours
Language Clarity Moderate High
Jargon Integration Limited Advanced

Impact of the Solution

 Improved Writing Quality:
Enhances structure.
Enhanced clarity and organized presentation.

 Efficiency:
Speeds up revisions with instant feedback.
Faster revisions with instant chatbot feedback.

 Accessibility:
Helps non-native researchers improve their language.
Inclusivity for non-native English speakers with intuitive UI.

 The agent successfully automated searches across research platforms.

 It generated accurate summaries for selected papers.
• The citation manager effectively organized references into a single repository .
 CHAPTER -6
Conclusions and Future Work
Conclusion:

• The Academic Research Agent addresses key challenges faced by researchers

by automating literature searches, managing references, and summarizing
research papers.
• The tool has significant potential for further development and can be scaled to
include additional features like plagiarism detection, collaborative tools, and
advanced analytics.
• The Academic Research Agent successfully addresses key pain points in the
academic writing process.
• By integrating an AI chatbot and rewriting tools, the system improves writing
quality, reduces revision time, and supports researchers in achieving higher-
quality academic papers.
• Future developments can expand the tool’s capabilities to support multiple
languages and advanced research workflows.

1. Summarization of the tool's impact on academic writing efficiency and clarity.

2. Contribution to increasing inclusivity for diverse research demographics.

The AI-powered solution addresses critical challenges in academic writing by:

1. Streamlining the writing process.
2. Enhancing clarity with formal, field-specific language.
3. Increasing efficiency and accessibility for diverse researchers.

Future Work:
1. Advanced features like note-taking, citation generation, and real-time collaboration.
2. Expansion to multilingual capabilities and mobile accessibility.
3. It includes multilingual support, citation tools, analytics, and real-time collaboration
features.
 CHAPTER -7
Progress Schedule Semester Wise

VII Semester

1. Project Ideation and Problem Identification:

o Brainstorm ideas to identify a relevant problem in academic workflows,
such as the challenges of language barriers and lack of domain-specific
writing tools.
o Finalize the project topic: Academic Research Agent.

2. Literature Survey and Related Work Analysis:

o Conduct a detailed review of existing tools like Grammarly, Quillbot, and
others.
o Identify gaps that the project will address, such as integrating domain-
specific terminology and offering real-time academic assistance.

3. Proposal Submission and Approval:

o Draft and submit the project proposal to faculty/mentors for approval,
outlining objectives, problem statement, methodology, and expected
outcomes.

4. System Design and Requirement Analysis:

o Design the architecture of the tool, specifying components like the AI
chatbot, rewriting tool, and vector databases.
o Create technical diagrams such as flowcharts and process outlines.

5. Prototype Development:
o Develop an initial version of the system with basic features like text
rewriting and context-aware chatbot responses.
o Test the prototype for functionality and gather preliminary feedback.
VIII Semester

1. Full System Implementation:

o Complete the development of the AI chatbot using RAG technology to provide real-time,
context-aware academic assistance.
o Implement the rewriting tool for language refinement and domain-specific jargon
integration.

2. Testing and Evaluation:

o Conduct extensive testing to identify bugs and performance issues.
o Gather user feedback from researchers and students to evaluate tool usability and
effectiveness.

3. Documentation and Final Report Writing:

o Document the project’s progress, including methodology, system design, testing results,
and future scope.
o Include visuals such as flowcharts, tables, and screenshots in the report.

4. Integration of Feedback and Improvements:

o Address issues identified during testing by refining features and improving performance.

5. Final Submission and Presentation:

o Submit the final report and present the project to evaluators, including a live
demonstration of the tool.
o Prepare a slide deck to showcase key findings, system features, and project outcomes.
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●OpenAI. (2023). Introducing ChatGPT.

https://openai.com/chatgpt

●Day, R. A., & Gastel, B. (2016).

How to Write and Publish a Scientific Paper. Cambridge University Press.

●Swales, J. M., & Feak, C. B. (2012).

Academic Writing for Graduate Students: Essential Tasks and Skills. University of
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●Mohd Yousuf , Abdul Wahid.

The role of Artificial Intelligence in Education: Current Trends and Future
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●Langchain. (2023).
Getting Started with Langchain.
https://python.langchain.com/docs/introduction/

●Pinecone. (2023).
Introduction to Vector Databases.
https://www.pinecone.io/learn/vector-database