A

MINI PROJECT REPORT

On
“CHATBOT FOR COLLEGE FAQS”
Submitted in partial fulfilment of the requirements for the
award of the degree of

BACHELOR OF TECHNOLOGY
IN
COMPUTER SCIENCE AND ENGINEERING

Submitted
by

K.Mounika 22UP1A0581
P.Vaishnavi 22UP1A05C7
N.Baby Harshitha 22UP1A05C0

Under the Guidance

of
Mrs. B. Mamatha
Assistant Professor

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

VIGNAN’S INSTITUTE OF MANAGEMENT AND TECHNOLOGY FOR WOMEN
(An Autonomous Institution)
(Affiliated to Jawaharlal Nehru Technological University Hyderabad, Accredited by NBA, NAAC with A+)
Kondapur (Village), Ghatkesar (Mandal), Medchal (Dist.)
Telangana-501301
(2022-2026)
 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

CERTIFICATE

This is to certify that the project work entitled “CHATBOT FOR COLLEGE
FAQS” submitted by K. Mounika (22UP1A0581), P. Vaishnavi
(22UP1A05C7), N.Baby Harshitha (22UP1A05C0) in the partial fulfilment of
the requirements for the award of the degree of Bachelor of Technology in
Computer Science and Engineering, Vignan’s Institute of Management
and Technology for Women is a record of bonafide work carried by them
under my guidance and supervision. The results embodied in this project report
have not been submitted to any other University or institute for the award of
any degree.

PROJECT GUIDE THE HEAD OF DEPARTMENT

Mrs. B. Mamatha Mrs. M. Parimala
(Assistant Professor) (Associate Professor)

(External Examiner)
 DECLARATION

We, hereby declare that the results embodied in this project entitled
“CHATBOT FOR COLLEGE FAQS” is carried out by us during the year 2024-
2025 in partial fulfilment of the award of Bachelor of Technology in Computer
Science and Engineering from Vignan’s Institute of Management and
Technology for Women is an authentic record of our work under the guidance
of Mrs. B. Mamatha. We have not submitted the same to any other university or
organization for the award of any other degree.

K.Mounika (22UP1A0581)
P.Vaishnavi(22UP1A05C7)
N.Baby Harshitha(22UP1A05C0)
 ACKNOWLEDGMENT

We would like to express sincere gratitude to Dr G. APPARAO NAIDU, Principal,

Vignan’s Institute of Management and Technology for Women for his timely
suggestions whichhelped us to complete the project in time.

We would also like to thank our madam Mrs. M. Parimala, Head of the
Department and Associate Professor, Computer Science and Engineering for
providing us with constant encouragement and resources which helped us to
complete the project in time.

We would also like to thank our Project guide Mrs. B. Mamatha Assistant
Professor, Computer Science and Engineering, for providing us with constant
encouragement and resources which helped us to complete the project in time with
his valuable suggestions throughout the project. We are indebted to him for the
opportunity given to work under his guidance.
Our sincere thanks to all the teaching and non-teaching staff of Department of
Computer Science and Engineering for their support throughout our project work.

K.Mounika (22UP1A0581)
P.Vaishnavi(22UP1A05C7)
N.Baby Harshitha(22UP1A05C0)
 INDEX
S.NO TOPIC PAGE
NO
0 ABSTRACT 1
1 INTRODUCTION 2-8
1.1 Overview 2
1.2 Objectives 4
1.3 Existing System 5
1.3.1 Limitations Of The Existing System 6
1.4 Proposed System 7
1.4.1 Benefits Of The Proposed System 7
2 LITERATURE SURVEY 9-11
3 SYSTEM ANALYSIS 12-17
3.1 Purpose Of College FAQs Chatbot 12
3.2 Goals Of The College FAQs 13
3.3 Feasibility Study 13
3.3.1 Economic Feasibility 13
3.3.2 Operational Feasibility 13
3.3.3 Ethical Feasibility 14
3.3.4 Social Feasibility 14
3.4 Requirement Analysis 14
3.4.1 Functional Requirements 14
3.4.2 Non-Functional Requirements 15
3.5 Requirment Specification 16
3.5.1 Hardware Requirements 16
3.5.2 Software Requirements 17
4 SYSTEM DESIGN 18-24
4.1 System Architecture 18
4.2 Description 18
4.3 Uml Diagrams 20
 4.3.1 Use Case Diagrams 20
4.3.2 Class Diagram 21
4.3.3 Sequence Diagram 22
4.3.4 Activity Diagram 23
5 IMPLEMENTATION 25-30
5.1 Source Code 25
6 SYSTEM TESTING 31-35
6.1. Objectives Of System Testing 31
6.2. System Components Under Test 31
6.3. Types Of System Testing 32
6.3.1 Unit Testing 32
6.3.2 Integration Testing 32
6.3.3 Performance Testing 32
6.3.4 Usability Testing 33
6.3.5 Error Handling Testing 33
6.3.6 Functional Testing 33
6.3.7 Key Findings 33
6.3.8 System Testing Scenarios And Cases 34
6.3.9 Tools Used For Testing 34

7 SCREEN SHOTS 36-39

8 CASE STUDIES 40-41
9 CONCLUSION 42
10 FUTURE SCOPE 43
11 BIBILOGRAPHY 44-45
11.1 References 44
11.2 Websites 45
 LIST OF FIGURES
FIGURE NAME PAGE NO

Figure 1 4

Figure 2 6

Figure 3 8

Figure 4 16

Figure 5: System Architecture 18

Figure 6: Use Case Diagram 20

Figure 7: Class Diagram 21

Figure 8: Sequence Diagram 22

Figure 9: Activity Diagram 23

Figure 10a: Home Page 36

Figure 10b: Initial Greeting 36

Figure 10 c: Admission Details 37

Figure 10 d : Course Information 38

Figure 10 e : Detailed Fee Structure And Scholorship 39

 ABSTRACT

In educational institutions, students, faculty, and prospective applicants

frequently seek information regarding admissions, courses,fee structures,
examinations, placements, campus facilities, and other academic services.
Traditional inquiry methods, such as administrative help desks, emails, and
phone calls, often result in delayed responses, increased workload for staff,
and in consistent information. To address these challenges, we propose an
AI-powered chat-bot designed to automate and streamline the process of
answering college-related FAQs efficiently and accurately.This chat-bot
leverages Natural Language Processing (NLP) and Machine Learning (ML) to
understand and interpret user queries in a conversational manner. It is
trained on a comprehensive data-set of college FAQs and can provide
instant, accurate responses to student queries. The chat-bot will be
integrated into multiple platforms,including the college website, Whats-app,
Telegram, and mobile applications, ensuring 24/7 availability.The chat-bot
offers advanced features such as voice-based query support, multilingual
responses, personalized interactions based on user type (student, faculty,
applicant), live chat escalation to human support, and an analytics
dashboard for administrators. These enhancements ensure a seamless user
experience, reduce manual workload, and improve accessibility for students.

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1.INTRODUCTION
1.1 OVERVIEW

This Application is for college students, staff, and parents. Easy way to
interaction and time consuming. This project is mainly targeted at colleges
and the synchronization of all the sparse and diverse information regarding
regular college schedule. Generally, students face problems in getting
correct notifications at the correct time, sometimes important notices such
as campus interview, training and placement events, holidays, and special
announcements. Smart Campus tries to bridge this gap between students,
teachers, and college administrators. Therefore in the real world scenario,
such as college campus, the information in the form of notices, oral
communication, can be directly communicated through the android devices
and can be made available for the students, teachers directly for their
android devices and the maintenance of application will be easier in later
future because of the use of architectural MVC which separates the major
works in the development of an application such as data management,
mobile user interface display and web service which will be the controller to
make sure for fast and efficient maintenance of application. The College bot
project is built using artificial algorithms that analyses user’s queries and
understand user’s message. This System is a web application which
provides answer to the query of the student. Students just must query
through the bot which is used for chatting. Students can chat using any
format there is no specific format the user has to follow. The System uses
built in artificial intelligence to answer the query. The answers are
appropriate what the user queries. The User can query any college related
activities through the system. The user does not have to personally go to the
college for enquiry. The System analyses the question and then answers to
the user. The system answers to the query as if it is answered by the person.
With the help of artificial intelligence, the system answers the query asked
by the students. The system replies using an effective Graphical user
interface which implies that as if a real person is talking to the user. The
user just must register himself to the 1system and has to login to the

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system. After login user can access to the various helping pages. Various
helping pages has the bot through which the user can chat by asking
queries related to college activities. The system replies to the user with the
help of effective graphical user interface. The user can query about the
college related activities through online with the help of this web application.
The user can query college related activities such as date and timing of
annual day, sports day, and other cultural activities. This system helps the
student to be updated about the college activities. Chat-bot is a computer
program that humans will interact with in natural spoken language and
including artificial intelligence techniques such as NLP (Natural language
processing)that makes the chat-bot more interactive and more reliable.
Based on the epidemiological situation, the increasing demand and reliance
on electronic education has become very difficult to access to the university
due to the curfew imposed, and this has led to limited access to information
for academics at the university. This project aims to build a chat-bot for
Admission and Registration to answer every person who asks about the
university, colleges, majors, and admission policy. Artificial intelligence (AI)
is a branch of computer science that focuses on creating machines that can
perform tasks that typically require human intelligence,such as perception,
reasoning, learning, and decision-making. AI uses a combination of
techniques, including machine learning, natural language processing,
computer vision, and robotics, to enable machines to learn from data and
adapt to new situations. In the context of a college enquiry chat-bot, AI
would allow the chat bot to understand and respond to natural language
queries from students,providing them with relevant information and support.
Artificial intelligence AI) plays a crucial role in the development and
functionality of chat-bots. Chat-bots are computer programs that use
natural language processing (NLP) to interact with humans and simulate
conversation. AI algorithms power the NLP capabilities of chat-bots,
enabling them to understand and respond to users' requests. Here are some
ways in which AI helps in chat-bots

3
 Figure 1

1.2 OBJECTIVES
The primary objectives of developing a chat bot for college FAQs are as
follows:

1. Provide Instant Information Access

To enable students, parents, and prospective applicants to quickly access
essential information about the college, such as admission procedures,
course offerings, fee structures, and important dates.
2. Enhance User Experience
To offer a seamless and interactive user interface that can answer queries
in a conversational manner, improving user engagement and satisfaction.
3. Reduce Administrative Workload
To minimize the burden on administrative and support staff by automating
responses to frequently asked questions, thereby allowing them to focus on
more complex or personalized inquiries.

4
4. Ensure 24/7 Availability
To provide uninterrupted support around the clock, ensuring that users
can get assistance anytime without being restricted by office hours.

Key points:

 Save effort and time for both the admission and registration staff and
students who wish to enroll.
 Provide detailed information about colleges and majors.
 Easy access to information.
 To minimize the time required to solve the queries.
 To give response to the user based on queries.
 To simplify communication between user and machine.

1.3 EXISTING SYSTEM

In the current system, most colleges handle frequently asked questions

through traditional means such as:

1. Manual Responses via Email or Phone

Administrative staff respond to student and parent queries through
email or telephone. This method is time-consuming and may lead to
delayed responses, especially during peak admission periods.
2. Static Website FAQs
Colleges maintain an FAQ page on their websites with answers to
common questions. However, users often find it difficult to locate specific
information, and the content may not always be up to date.
3. In-Person Inquiries
Students and visitors often visit college premises to get their queries
answered. This method is inefficient and creates unnecessary crowding,
especially during admissions.

5
4. Printed Brochures and Handbooks
Colleges distribute printed materials containing general information, but
these are not interactive and may become outdated quickly.

1.3.1 LIMITATIONS OF THE EXISTING SYSTEM

 Lack of real-time and personalized interaction

 High dependency on human resources
 Limited availability (restricted to office hours)
 Inflexibility in handling a large volume of queries
 No support for multilingual communication or smart query handling

Figure 2

6
1.4 PROPOSED SYSTEM

1. Instant, 24/7 Support

Students can get help anytime—no need to wait for business hours,
reducing frustration and improving satisfaction.
2. Eases Staff Workload
By answering common queries automatically, it frees up time for college
staff and administrators to focus on more complex cases and in-person
support.
3. Consistent, High- Quality Responses
The chat bot ensures every student receives the same accurate info,
minimizing human error or inconsistent replies.

1.4.1 BENEFITS OF THE PROPOSED SYSTEM

1. 24/7 Instant Support & Scalability

 Always-on availability, eliminating wait times and supporting
students across time zones — vital for non-traditional and global
learns.
 Handles unlimited conversations concurrently, especially during
peak periods like admissions or registration.

2. Cost-Efficiency & Resource Optimization

 Reduces staffing costs by automating up to ~80% of routine queries;
staff can focus on high-value tasks .Needs no seasonal hires during
surges, offering predictable operational expenses

3. Faster & Consistent Responses

 Instant replies reduce response time from hours to seconds.
 Ensures uniform, accurate information, eliminating staff
inconsistencies.

4. Personalized Learning & Engagement

7
  Uses NLP and data analytics for tailored responses, related to a
student's prior interactions or academic stage
 Enables virtual tutoring, feedback, and gamified interactions for deeper
engagement

5. Improved Administrative Efficiency

 Automates tasks like enrollment guidance, schedule info, FAQs—

saving staff time

 Reduces human errors in repetitive workflows .

6. Actionable Data & Continuous Learning

 Captures insights on recurring issues, user behavior, and service gaps .

 Uses analytics and machine learning to improve over time, flag
outdated content, and expand coverage .

7. Proven Impact on Student Success

 Georgia State saw its student-advisor ratio decrease from 800:1 to
300:1, led to a 23% increase in 6-year graduation rates and ~$3 M
extra revenue per percentage point—clear ROI

Figure 3

8
 2. LITERATURE SURVEY
Literature Survey for Chatbot for College Faqs
Introduction
A literature survey on chat-bot applications for college FAQs reveals a
growing trend in using chat-bots to automate and enhance communication
between educational institutions and students. These chat bots, powered by
Natural Language Processing (NLP) and Machine Learning (ML), can answer
a wide range of common questions, from basic information about the college
to specific details about courses, fees, and facilities. The literature highlights
the potential of chat-bots to improve student experience by providing 24/7
access to information, streamlining communication, and freeing up staff for
more complex tasks.

Key Findings from the Literature:

 Improved Efficiency and Accessibility:

Chat-bots offer a convenient and efficient way for students to get answers to
their questions, eliminating the need for manual searching on websites or
waiting for responses via email or phone.
 24/7 Availability:
Chat-bots can be accessed anytime, anywhere, providing instant support to
students regardless of their location or the time of day.
 Cost-Effectiveness:
While initial setup and training require investment, chat-bots can
significantly reduce the cost of providing customer support in the long run
by automating repetitive tasks.
 Personalized Interactions:
Some chat-bots are designed to personalize interactions, allowing them to
tailor responses to individual student needs and preferences.

9
 Scalability:
Chat-bots can easily handle large volumes of queries, making them suitable
for handling peak periods like admissions or exam seasons.
 Evolution and Learning:
Chat-bots can be trained using Machine Learning techniques to improve
their accuracy and ability to handle more complex questions over time.
 Integration with Existing Systems:
Chat-bots can be integrated with existing college websites and
communication platforms like WhatsApp, making them easily accessible to
students.

Specific Applications in College FAQs:

 Providing general information: Answering questions about the

college's history, mission, location, and contact information.

 Providing information about courses: Explaining course details,

eligibility criteria, and prerequisites.

 Providing information about fees and scholarships: Answering

questions about tuition fees, payment methods, and scholarship
opportunities.

 Providing information about facilities: Answering questions about

libraries, laboratories, sports facilities, and other amenities.

 Providing information about admission procedures: Explaining the

admission process, application deadlines, and required documents.

 Providing information about exams: Answering questions about

exam schedules, exam patterns, and results.

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Challenges and Limitations:

 Complex Question Handling:

Chat-bots may struggle with complex or nuanced questions that require in-
depth understanding or reasoning.

 Lack of Human Touch:

While chat-bots can mimic human conversation, they may lack the
empathy and emotional intelligence of a human advisor.
 Data Privacy and Security:
It's crucial to ensure that chat-bot systems handle user data responsibly
and securely, complying with privacy regulations.
 Initial Setup and Maintenance:
Developing and maintaining a chat-bot requires technical expertise and
ongoing effort.

The literature survey demonstrates that chat-bots are a valuable tool for
colleges to enhance student support and streamline communication. By
leveraging NLP and ML, chat-bots can effectively answer frequently asked
questions, freeing up staff to focus on more complex student needs and
improving overall student experience. While challenges exist, the benefits of
chat-bots in the college environment are significant and continue to grow as
technology evolves

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3. SYSTEM ANALYSIS
3.1 Purpose of the College FAQs Chatbot

The primary goal is to build a centralized, intelligent chatbot that assists

students, faculty, and staff by promptly answering college-related inquiries
(e.g. admissions, courses, fees, deadlines) using advanced NLP. Specifically,
the system aims to:

i. Empower users with informed decisions

 Provide detailed, accurate responses to student queries.

 Offer proactive alerts (e.g., deadlines) and follow-up guidance.

ii. Ensure accessibility & consistency

 Available 24/7 across devices and platforms.

 Delivers uniform information, preventing conflicting human
responses.

iii.Automate routine support

 Handle FAQs without staff intervention.

 Free up administrative resources for complex tasks.

iv. Leverage intelligent technologies

 Use NLP, RAG, and ML to improve accuracy, personalize answers,

and update knowledge.

v. Enhance user trust and satisfaction

 Provide helpful, reliable answers quickly.

 Respect data privacy and maintain transparency.

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3.2 Goals of the College FAQ Chatbot

i. User-focused Goals

 Minimize student effort in finding information.

 Display clear, intuitive conversational UI across devices.

ii. Technical Goals

 Deliver accurate, up-to-date responses via structured knowledge and

generative NLP.

 Understand varied phrasing with robust intent recognition.

 Learn from interactions and adapt over time.

iii.Institutional Goals

 Track FAQs usage and satisfaction via analytics.

 Escalate complex queries to staff.
 Demonstrate ROI through reduced workload and enhanced student
experience.

3.3 Feasibility Study

3.3.1 Economic Feasibility

 Built with open-source tools (e.g., Rasa) to lower costs

 Minimal hosting expenses; potential with student-led development.

3.3.2 Operational Feasibility

 Reduces repetitive inquiries, allowing staff to focus on high-impact
work.
 Requires periodic content upkeep and monitoring.

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3.3.3 Ethical Feasibility

 Must respect user privacy, data security, and academic integrity;

redact sensitive content.

3.3.4 Social Feasibility

 Students appreciate real-time, easy-to-use support; international

students benefit from multilingual interface.
 Adopting chat-bots signals innovation and commitment to student
success.

3.4 Requirement Analysis

3.4.1 Functional Requirements

1. User Interaction

a. Chat interface capturing free-text inputs.

b. Recognize greetings, FAQs (admissions, deadlines, documents, courses,
etc.).
c. Maintain conversational context and allow multi-turn Q&A.

2. Response Generation

a. Retrieval from FAQ database.

b. Generative fallback (RAG) for unseen queries.
c. Escalation mechanism to direct students to staff for sensitive or
unresolved issues.

3. Admin Interface & Analytics

a. Dashboard to review and update FAQs.

b. Monitor query volume, satisfaction, escalation rates.

4. Multi-lingual & Accessibility Support

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 a. Interface adaptable to diverse linguistic needs.
b. Compliant with accessibility standards (WCAG).

3.4.2 Non-Functional Requirements

1. Performance & Reliability

a. Sub-second response times, even with complex queries.

b. Continuous uptime with fallback mechanisms.

2. Scalability

a. Capable of handling peak usage loads (e.g., admission season).

b. Modular architecture supporting expansion of FAQ content.

3. Security & Privacy

a. Use encrypted channels (HTTPS/TLS).

b. Role-based access controls for administrators.
c. Data protection compliant with relevant regulations; no unauthorized
data sharing.

4. Maintainability & Extensibility

a. Modular code design; versioned content stored in repositories.

b. Easily update FAQs and add new modules (e.g., scholarships, events).

5. Compliance

a. Align with university regulations and academic integrity standards.

b. Incorporate design to detect academic misconduct or guide students
responsibly.

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 Figure 4

3.5 REQUIRMENT SPECIFICATION

3.5.1 Hardware Requirements
 Development Machine

 Processor: Intel i5 or equivalent AMD

 RAM: 8 GB or more
 Storage: ≥ 256 GB SSD
 OS: Windows 10 or macOS/Linux

 Server Hardware (Deployment)

 Cloud-based VM
 CPU: ≥ 2 virtual cores
 RAM: ≥ 4 GB (scale up depending on load)
 Storage: ≥ 50 GB SSD
 Network: High-speed, reliable internet connectivity

 Optional Testing Hardware

 Android/iOS smartphone or tablet for responsive/mobile UI testing

These align with minimal requirements seen in academic chat-bot
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 systems (e.g., Pentium IV, 1.1 GHz CPU, 256 MB RAM for basic
prototypes) but updated to reflect modern standards.

3.5.2 Software Requirements

1. Development Tools

 IDE: Visual Studio Code (or equivalents)

 Version Control: Git (GitHub/GitLab/Bitbucket)
 Containerization: Docker (optional for consistent environments)

2. Frontend Stack

 HTML5/CSS3 (with templating if needed)

 UI Framework: Bootstrap for responsive design
 JavaScript (vanilla or frameworks

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 SYSTEM DESIGN

4.1 SYSTEM ARCHITECTURE

Figure 5: System Architecture

4.2 DESCRIPTION

The system architecture of the Chatbot for College FAQs is designed using a
modular and layered approach. It ensures a seamless flow of information
from the user interface to the backend response engine through various
processing layers. The architecture is broadly divided into the following
components:

1. Users and Devices

 User (students, parents, etc.) and Admin (college staff) access the
system through:

 Desktop
 Laptop
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  Mobile Phone

 These devices are connected to the internet via a Modem.

2. Access and Communication

 Devices access the system via the Web, which acts as the Messaging
Platform (e.g., Telegram, web chat).
 The Web forwards user input to the Chatbot and receives responses.
3. Chatbot Core Function

 The Chatbot receives plain text from the user and passes it to the
Machine Learning Layer
 NLU Component: Extracts intents and entities from the input using
Natural Language Understanding.
 NLP Component: Processes the structure of the message and
identifies its meaning.

4. Message Handling
 The Message Generator uses the intent/entity information to
determine the appropriate response.
 It queries the Knowledge Base for existing information.
 If more processing is needed, it accesses data through an API or
requests Human Intervention.
5. Message Generator
 Once the intent and entities are recognized, the Message Generator
determines the correct response.
 It either retrieves the appropriate answer from the knowledge base or
triggers API calls if external data is needed.
6. Knowledge Base & API

 The Knowledge Base stores all FAQ content and response templates.
 The API allows integration with external systems (e.g., database, web
services).

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  If the API or knowledge base lacks the answer, Human Intervention
may be triggered to provide or update responses.

7. Admin Access and Security

 Admins log in to update or maintain the system

 Login requests go through a Security layer that handles
authentication.
 Upon successful login, the admin gains access to the Presentation
Layer to view or modify the knowledge base content.

4.3 UML DIAGRAMS

4.3.1 USE CASE DIAGRAM

Figure 6:Use Case Diagram

The Use Case Diagram for the Chatbot System illustrates the interaction

20
between two primary actors—User and Admin—and the various
functionalities provided by the system. Users interact with the chatbot by
performing two main actions: asking FAQs and receiving answers. This
allows them to get instant responses to their common queries related to
college information, services, and academic details. On the other hand, the
Admin is responsible for managing the backend of the system. The Admin
can train the chatbot to improve its response accuracy, manage users by
controlling access and roles, and update FAQ answers to ensure the
information provided to users is current and relevant. The diagram visually
encapsulates these interactions within a system boundary labeled "Chatbot
System", clearly separating the responsibilities of each actor and the
corresponding system functionalities. This structured representation helps
in understanding the overall scope and functionality of the chatbot
application developed for college FAQs.
4.3.2 CLASS DIAGRAM

Figure 7:Class Diagram

The above UML class diagram represents the structure of a Chatbot for
College FAQs system and its key entities: User, Admin, Chatbot, FAQ, and
Feedback. Each class contains essential attributes and methods that define

21
the behavior and responsibilities of the respective components in the system.
The User class includes attributes such as user_id and name, and methods
like askQuestion(), viewAnswer(), and rateResponse(). This class models the
general users who interact with the chatbot to receive automated answers
for college-related queries and optionally rate the helpfulness of the
responses. The Admin class consists of administrative users responsible for
managing the FAQ content. It includes attributes like admin_id, name, and
email, along with methods such as addFAQ(), updateFAQ(), and deleteFAQ(),
which allow for the creation, modification, and deletion of FAQ entries.
The Chatbot class is central to the system and acts as the core processing
unit. It contains attributes like bot_id and bot_name, and methods including
processQuestion() and fetchAnswer(), which handle user input and retrieve
appropriate responses from the FAQ database. The FAQ class serves as the
knowledge base, storing the faq_id, question, answer, and possibly a
category. It supports methods like editFAQ() and deleteFAQ() to maintain
relevant and up-to-date information.
4.3.3 SEQUENCE DIAGRAM

Figure 8: Sequence Diagram

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This sequence diagram represents the interaction process in the "Chatbot
for College FAQs" system. It illustrates the flow of communication between
the primary actors: User, Chatbot, and Admin, showcasing how a typical
FAQ query is handled and followed up within the system.
The interaction begins when the User sends a question to the Chatbot by
invoking the Ask question action. The Chatbot processes this query by
accessing its knowledge base or internal logic to determine the best possible
response. Once the answer is ready, the Chatbot responds by allowing the
User to View answer. This part of the sequence ensures that users receive
immediate responses to their college-related questions.
After viewing the answer, the User has the option to Rate response, which
helps measure the accuracy and usefulness of the chatbot’s reply. This user
rating is an important feedback mechanism for system improvement. The
Chatbot then passes this feedback through a Submit feedback action, which
can be accessed or reviewed later by the Admin. The Admin uses this data to
monitor performance, improve the FAQ content, and make necessary
updates to ensure the system remains helpful and accurate.

4.3.4 ACTIVITY DIAGRAM

Figure 9:Activity Diagram

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This activity diagram visually represents the step-by-step workflow of the
"Chatbot for College FAQs" system, focusing on how a user interacts with
the chatbot from start to finish. It follows a linear, user-centric flow that
models the key operations in delivering college-related information through
a chatbot.
The process begins with the user initiating a query by performing the action
“Ask question.” This input is then passed to the system, where the chatbot
begins to “Process question,” meaning it analyzes the input using its
internal logic or natural language processing techniques.
Once the question is understood, the system proceeds to “Display answer”—
presenting a suitable and relevant response from the FAQ database back to
the user. After reviewing the response, the user has an opportunity to “Rate
response,” providing a quality assessment of how helpful or accurate the
answer was.
Finally, the flow ends with “Submit feedback,” allowing the user's input to
be saved for future improvement and analysis. The diagram concludes at the
terminal node, marking the completion of one full interaction cycle.
This activity diagram simplifies the chatbot’s workflow into clear,
understandable actions, making it ideal for documentation, development,
and stakeholder communication for the Chatbot for College FAQs system.

24
 5.IMPLEMENTATION
5.1 SOURCE CODE

<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-
scale=1.0"/>
<title>VMTW COLLEGE CHATBOT</title>
<style>
body { font-family: Arial, sans-serif; margin:0; padding:20px;
display:flex; justify-content:center; align-items:center;
min-height:100vh; background:linear-gradient(#4b6cb7,#182848); }
.chat-container { background:rgba(255,255,255,0.95); padding:30px;
border-radius:15px; box-shadow:0 4px 15px rgba(0,0,0,0.15);
width:90%; max-width:600px; }
h1 { text-align:center; color:red; margin-bottom:20px; line-height:1.2; }
#chat-box {
border:1px solid #ccc; padding:15px;
min-height:250px; max-height:400px;
overflow-y:auto; margin-bottom:20px;
border-radius:10px; background:#f4f4f4;
}
.user-message { text-align:right; color:#3498db; margin-bottom:10px; }
.bot-message { text-align:left; color:#2c3e50; margin-bottom:10px;
white-space:pre-wrap; }
.input-container { display:flex; gap:10px; margin-bottom:10px; }
input[type="text"] {
flex:1; padding:12px; font-size:16px;
border-radius:8px; border:1px solid #ccc;
}
button {
padding:12px 20px; background-color:#2980b9;
color:white; border:none; border-radius:8px;
font-size:16px; cursor:pointer;
}
button:hover { background-color:#3498db; }
.quick-buttons { display:flex; gap:10px; justify-content:center; margin-
bottom:15px; }
</style>
25
</head>
<body>
<div class="chat-container">
<h1>VMTW COLLEGE CHATBOT<br>FAQS</h1>
<div id="chat-box"></div>
<div class="quick-buttons">
<button onclick="showAbout()">About VMTW</button>
<button onclick="showFee()">Fee Structure</button>
<button onclick="showFacilities()">Facilities</button>
</div>
<div class="input-container">
<input type="text" id="user-input" placeholder="Ask about facilities,
departments..."/>
<button onclick="sendMessage()">Send</button>
</div>
</div>

<script>
const hodCSE = HOD (CSE): Mrs. M. Parimala – Associate Prof & HOD
Email: cse@vmtw.in / parimala@vmtw.in
Mobile: +91‑ 98499‑ 86685

Education:
- Pursuing PhD at SJJTU Jaipur
- M.Tech CSE from Osmania University

Research: AI, ML, Cloud Computing, Automata Theory

Experience:
- HOD CSE at Tirumala Engg (8 yrs)
- 1st-year Coordinator (2 yrs)
- Incharge HOD at CMR Institute (1y4m)

Programs Conducted:
- FDP on Machine Learning – Sept 2018
- FDP on Artificial Intelligence – May 2020
- AICTE FDP on Big Data Analytics – Jan 2020
- Cloud Computing Workshop – Aug 2014
- Technical events coordinator (Kenensence ’06, Avishkar ’09,
Shrushti ’12/’14, Techveda/Reva ’18/’19)

Certifications:
- NPTEL‑ DBMS
26
- Coursera – Introduction to AI`;

const cseFaculty = CSE Faculty List:

1. Dr. G. Apparao Naidu – Professor & Principal (PhD)
2. Mrs. M. Parimala – Associate Prof (M.Tech(PhD))
3. Dr. C. Srinivasa Kumar – Professor (PhD)
4. Dr. Battula Phijik – Professor (PhD)
5. Dr. Rajendra Prasad Panaganti – Associate Prof (PhD)
6. Dr. Gundla Rajesh – Associate Prof (PhD)
7. Dr. Shalima Sulthana – Assistant Prof (PhD)
8. Mr. R. Krishna Naik – Associate Prof (M.Tech(PhD))
... (total 48 faculty; include full list if needed)`;

const departments = {
cse: {
strength: 300,
toppers: ["Anusha K – 9.8", "Rakesh P – 9.7"],
timing: "9:00 AM – 4:30 PM",
extra: hodCSE + "\n\n" + cseFaculty
},
ece: {
strength: 180,
toppers: ["Deepika R – 9.6", "Naveen J – 9.5"],
timing: "9:00 AM – 4:30 PM",
extra: ""
},
it: {
strength: 120,
toppers: ["Sneha M – 9.4", "Harsha N – 9.3"],
timing: "9:30 AM – 5:00 PM",
extra: ""
},
aids: {
strength: 90,
toppers: ["Charan K – 9.5", "Meena P – 9.3"],
timing: "10:00 AM – 5:00 PM",
extra: ""
},
aiml: {
strength: 150,
toppers: ["Lalitha B – 9.6", "Sameer M – 9.5"],
timing: "9:00 AM – 4:00 PM",
extra: AI & ML Dept Started 2020 (60 intake)
27
Labs: 100+ systems
Core areas: AI, ML, DL, NLP, CV, Robotics, BI, HCI, DS...`
},
csd: {
strength: 100,
toppers: ["Reshma T – 9.4", "Ajay K – 9.2"],
timing: "10:00 AM – 5:30 PM",
extra: ""
}
};

const about = *About VMTW*

Vignan's Institute of Management & Technology for Women (VMTW), est.
Aug 2008 by Dr. L. Rathaiah, is in Kondapur(V), Ghatkesar(M),
Medchal(D), Telangana. Affiliated to JNTUH, AICTE-approved, NBA-
accredited (CSE & ECE till June 2025), ISO 14001:2015 & 50001:2018
certified.
Spread across 5 acres of mango groves, the campus includes modern
labs, classrooms, a large library, secure hostel, and a placement cell.
VMTW nurtures students via sports, culture & leadership—shifting them
from "can you help me?" to "can I help you?"`;

const feeStructure = Fee Structure 2024‑ 25:

1. Tuition: Rs.90,000
a) Rs.55,000 (fee-reimbursement)
b) Rs.90,000 (others)
2. Registration: Rs.1,000
3. JNTU Infrastructure: Rs.1,500
4. Hostel Non‑ AC: Rs.95,000
5. Hostel AC: Rs.1,20,000
6. Bus: Depends on boarding point`;

const facilities = Facilities:

• 200 Mbps internet + campus Wi‑ Fi
• UPS & generator backup
• Printers & copiers
• Health center with doctor & first-aid
• Mineral water plant & coolers
• Fire extinguishers
• Spacious cafeteria
• Guest room, waiting hall
• Conference hall (AV-enabled)
• Seminar hall (250 seats)
28
• Central stationery store`;

function getDeptInfo(dept) {
const d = departments[dept];
if (!d) return null;
return Dept: ${dept.toUpperCase()}
Strength: ${d.strength} students
Toppers: ${d.toppers.join(", ")}
Timings: ${d.timing}
{d.extra}`;
}

function getBotResponse(msg) {
const q = msg.toLowerCase().trim();
for (let dept of Object.keys(departments)) {
if (q.includes(dept)) return getDeptInfo(dept);
}
if (q.includes("about")) return about;
if (q.includes("fee")) return feeStructure;
if (q.includes("facility")) return facilities;
if (q.includes("course")) return Courses: B.Tech (CSE, ECE, IT, AI&DS,
AI&ML, CSD); M.Tech (CSE, VLSI);
if (q.includes("admission")) return "Admissions start in March; apply
with form, transcripts & exam scores.";
if (q.includes("deadline")) return "Application deadline is usually
around May 15.";
if (q.includes("hello") || q.includes("hi")) return Hi! How can I assist
with VMTW info?";
if (q.includes("thank")) return "You're welcome!";
if (q.includes("bye")) return "Goodbye! Have a great day!";
return I didn't understand. Try asking about departments (e.g.,
"CSE"), fees, facilities, or "about VMTW".;
}

function appendMessage(sender, text) {

const box = document.getElementById("chat-box");
const div = document.createElement("div");
div.className = sender === "You" ? "user-message" : "bot-message";
div.innerHTML = ${sender}: ${text.replace(/\n/g, "<br>")};
box.appendChild(div);
box.scrollTop = box.scrollHeight;
}

29
 function sendMessage() {
const input = document.getElementById("user-input");
const txt = input.value;
if (!txt.trim()) return;
appendMessage("You", txt);
setTimeout(() => appendMessage("Assistant", getBotResponse(txt)),
300);
input.value = "";
}

function showAbout() { appendMessage("Assistant", about); }

function showFee() { appendMessage("Assistant", feeStructure); }
function showFacilities() { appendMessage("Assistant", facilities); }

document.getElementById("user-input")
.addEventListener("keypress", e => { if (e.key === "Enter")
sendMessage(); });
</script>
</body>
</html>

30
 6.SYSTEM TESTING
System testing is a critical phase in the development life cycle of the Chatbot
for College FAQs. This phase ensures that all components of the system
work together as intended and the chatbot performs accurately, reliably,
and consistently under different scenarios. It involves validating the system
against the specified functional and non-functional requirements.

6.1. OBJECTIVES OF SYSTEM TESTING

 To verify that the chatbot correctly interprets user queries and returns
appropriate responses.
 To ensure integration among UI, backend, NLP processing, and
database is seamless.
 To identify and fix any bugs or logical errors before deployment.
 To validate usability, performance, and robustness of the system under
normal and extreme conditions.
 To ensure fallback mechanisms work when queries are out-of-scope or
invalid.
6.2. SYSTEM COMPONENTS UNDER TEST
User Query Interface Module
Natural Language Processing (NLP) Module
Chatbot Response Engine
FAQ Knowledge Base Module
Message Formatting and Rendering Module
Admin Content Management Console
Keyword Matching and Intent Resolution Logic
Fallback Handling System
Authentication Layer (for Admin Access)
Logging and Monitoring Module

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6.3. TYPES OF SYSTEM TESTING
6.3.1 UNIT TESTING
Unit testing was performed on individual components of the chatbot to
validate their correctness in isolation before integration. Key units tested
include:
 Keyword Extraction Function – Ensured accurate identification of
keywords like "fee", "hostel", "CSE".
 Intent Detection – Verified mapping of user queries to correct
intents like "admission", "placement", etc.
 Database Query Function – Tested correct retrieval of answers
based on keyword or tag match.
 Fallback Handler – Checked for generic response when the input
doesn't match any known intent.
 Response Formatter – Validated structured replies with emojis, line
breaks, and bullet points.

6.3.2 INTEGRATION TESTING

This tested the interaction between components like:
 Telegram/web interface ↔ Flask server
 Flask ↔ NLP logic
 NLP ↔ Database

Example:
User enters “When do admissions start?”
Flask receives input
→ NLP extracts intent “admission_date”
→ Bot fetches response from database
→ Bot sends it back through UI

6.3.3 PERFORMANCE TESTING

 Response time was tested under different loads using simulated
parallel queries.
32
  The chatbot consistently responded within 1.8 seconds, even under
high load (10 concurrent users).

6.3.4 USABILITY TESTING

A group of 10 students and 2 parents were asked to interact with the
chatbot using Telegram and web UI.
Key feedback:
 Easy to use
 Clearly structured answers
 Suggested adding synonyms for better flexibility

6.3.5 ERROR HANDLING/ NEGATIVE TESTING

 Input: "Tell me a joke" → Response: "Sorry, I can only answer
college-related queries."
 Input: Empty/blank message → Handled with a default message.
6.3.6 FUNCTIONAL TESTING
The chatbot was tested against real-time user scenarios to ensure it could:
 Understand different phrasings (e.g., “fees for IT” vs. “tuition cost IT”)
 Return correct responses for known queries
 Redirect to fallback or generic answers when unknown

Tested Questions:
 “When do classes start?”
 “What are the hostel charges?”
 “Is AI course available?”
 “Who is the HOD of CSE?”
 “Is there placement training?”

6.3.7 KEY FINDINGS

 The chatbot successfully answered most user queries.
 All modules integrated and worked smoothly.
 Response time was fast and consistent.
 Fallback messages were shown for unknown inputs.
33
  Users found the interface simple and easy to use.
 Admin was able to update FAQs without issues.
 No errors or crashes were found during testing.
 The system is ready for real-time use and future upgrades.

6.3.8 SYSTEM TESTING SCENARIOS AND CASES

Test Case
Test Scenario Expected Output Status
ID

Ask about admission start ✅

TC001 Show correct timeline
date Pass

✅
TC002 Ask unknown question Show fallback message
Pass

Show fee range with ✅

TC003 Retrieve hostel fee
emoji Pass

Responsive layout ✅
TC004 Access with mobile UI
visible Pass

Prompt user to enter ✅

TC005 Send blank message
valid query Pass

6.3.9 TOOLS USED FOR TESTING

 Chrome DevTools – Used to inspect and debug HTML, CSS, and

JavaScript code directly within the browser during development.
 W3C Markup Validator – Employed to check the validity and
correctness of HTML code based on W3C standards.
 W3C CSS Validator – Used to validate CSS stylesheets and ensure
they adhere to proper syntax and browser compatibility.

34
 JSHint – A static code analysis tool used to detect errors and
potential problems in JavaScript code.
 Responsively App – Used to test how the chatbot layout and
interface appear across multiple device viewports simultaneously.
 BrowserStack – A cross-browser testing tool used to ensure
consistent chatbot performance across different browsers and
operating systems.
 Lighthouse – Utilized to audit the web application’s performance,
accessibility, SEO, and best practices.
 ESLint – Applied to enforce consistent coding style and catch bugs
or issues in JavaScript during development.

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 7.SCREEN SHOTS

Figure 10 a : Home Page

a. The chatbot interface begins with a clean and minimal home screen titled
“VMTW College Chatbot FAQs”. The user is presented with a large blank
chat area, two quick-access buttons — Course Info and Fee Info, and a text
input box with a placeholder hint ("Ask about admissions, departments,
fees..."). This screen invites users to begin their interaction either by clicking
a button or typing a query manually.

Figure 10 b : Initial Greeting

36
b.Upon the user entering a simple greeting like “hi”, the chatbot promptly
responds with a warm welcome message:
“Hello! Welcome to VMTW College FAQ Assistant. How can I help you?”
This confirms that the bot is active and waiting to handle queries, giving the
user confidence that their input will be processed appropriately.

Figure 10 c : Admission Details

c.This captures a functional query-response cycle between the user and the
VMTW College Chatbot FAQs system. After the initial greeting, the user
enters the keyword “admissions”. The chatbot promptly responds with
detailed information:
“ Admissions at VMTW usually start in March. Required: Application form,
transcripts, entrance exam scores.”
This shows that the chatbot successfully interprets user input using NLP
and retrieves the corresponding answer from its knowledge base. The
interaction highlights the core functionality of the project — answering
college-related FAQs in real time with precise, helpful responses. The
interface remains unchanged, with a consistent layout including the chat
box, input field, and action buttons, ensuring a smooth user experience
throughout the conversation.

37
 Figure 10 d: Course Information

d.The VMTW College Chatbot FAQs showcases its

ability to handle multi-part responses. After the user types the query
“admissions”, the chatbot not only provides the admission timeline and
required documents but also automatically supplements the response with
a list of courses offered at VMTW College. These include popular streams
such as:
 CSE (Computer Science Engineering)
 ECE (Electronics & Communication Engineering)
 IT (Information Technology)
 AIDS (Artificial Intelligence & Data Science)
 AIML (AI & Machine Learning)
 CSD (Computer Science & Design)
This comprehensive response demonstrates that the chatbot is equipped
to go beyond basic replies and deliver detailed academic information in a
conversational format. It reflects the chatbot’s design focus on user
convenience, intelligent grouping of related data, and reducing the number
of follow-up questions needed.

38
 Figure 10 e :Detailed Fee Structure and Scholarships
e.This illustrates the chatbot’s ability to deliver comprehensive academic
and financial details in a structured format. Upon the user typing
“departments,” the chatbot responds with a full breakdown of tuition fees
per department, including:
CSE, ECE, IT, AIDS, AIML, and CSD — each listed with a fee of ₹1,30,000
per year.
It also highlights additional key information such as:
 Hostel Fees: ₹35,000 – ₹50,000 per semester, inclusive of food and
accommodation.
 Scholarship Availability: Clearly states that scholarships are
offered based on merit and financial need.
This response reflects the chatbot’s capacity to act as a complete college
information assistant, capable of addressing admissions, academics,
housing, and financial aid — all in one conversation flow. It significantly
simplifies the decision-making process for both students and parents.

39
 8. CASE STUDIES
� Case Study 1: New Admission Inquiry by Prospective Student

Background:

Anjali, a 12th-grade student, is planning to apply for a B.Tech in Computer

Science. She wants to know the eligibility criteria, fee structure, and hostel
availability.

Problem:

The college website is confusing, and the information is scattered across

different pages. Phone lines are busy due to high admission season.

Chatbot Solution:

 Anjali opens the Telegram chatbot and asks:

 “What is the eligibility for CSE?”
 “How much is the fee for CSE?”
 “Is hostel available for girls?”

Outcome:

She receives instant and accurate answers without waiting or searching

through the website. Her parent also uses the bot to get principal contact
details. The interaction builds confidence in the institution’s tech-driven
support.

� Case Study 2: Internal Student Queries During Semester

Background:

Rohit, a second-year ECE student, is unsure about the date of mid-term

exams and how to apply for a bus pass.

Problem:

Class representatives are unavailable and office hours are over.

40
Chatbot Solution:

 Rohit types:
 “When are mid exams?”
 “Bus pass procedure?”

Outcome:

The chatbot retrieves data from the academic calendar and admin FAQs,
giving him instant clarification. Rohit uses it again later to ask “Who is
the HOD of ECE?”

� Case Study 3: Parent Seeks Placement Record

Background:

A parent attending counseling wants to know the placement record and

recruiters for the IT department.

Problem:

Website data is outdated and staff is busy.

Chatbot Solution:

 He uses the bot to ask:

 “What is the placement percentage in IT?”
 “Top recruiters?”

Outcome:

The parent is satisfied with the clear, quick information, helping in

decision-making.

41
 9.CONCLUSION

The “Chatbot for College FAQs” project successfully demonstrates the

integration of artificial intelligence with user-interactive systems to provide
automated and accurate responses to frequently asked questions related to
college information. By designing and developing this chatbot, we aimed to
simplify the process of information retrieval for students, guests, and other
users who seek instant answers about college-related queries such as
admission process, course offerings, schedules, fees, facilities, and more.
The system leverages natural language processing (NLP) to understand user
questions and match them with relevant answers stored in the FAQ
database. Users can interact with the chatbot through a user-friendly
interface, ask questions, view responses, and even provide feedback and rate
the accuracy of responses. This feedback mechanism helps in continuously
improving the system based on real-time user interactions.
Moreover, the admin panel allows college authorities to manage FAQs
effectively. Admins can add new questions, update existing answers, and
monitor feedback received from users. This ensures that the information
remains up-to-date and relevant, maintaining the credibility and usefulness
of the system.
Various UML diagrams such as use case, class, sequence, and activity
diagrams were developed to represent the functional and structural aspects
of the system, aiding in better planning, implementation, and future
enhancements.
This project provides a practical solution to reduce human dependency for
routine college queries, ensures fast access to information, and enhances
user satisfaction. It serves as a valuable digital assistant, especially in
educational institutions that deal with a high volume of repetitive student
queries. With further development, the system could be extended to support
voice-based queries, multilingual support, and integration with college
management systems.

42
 10.FUTURE SCOPE
The chatbot system is currently designed for handling FAQ-based queries.
However, several enhancements can be made in future versions:
1. Voice Interaction
Integrate speech-to-text APIs to support voice-based questions.
Beneficial for users with accessibility needs.
2. Multilingual Support
Add support for regional languages such as Hindi, Telugu, Tamil, etc.
Expands usability to a wider user base.
3. Advanced Machine Learning
Implement deep learning techniques like intent classification (using BERT or
GPT fine-tuning).
Enables the bot to handle complex or multi-intent queries.
4. Analytics Dashboard
Admin dashboard for visualizing common questions, chatbot usage patterns,
and feedback.
Helps identify content gaps and improve accuracy.
5. Notifications & Alerts
Bot can push real-time updates about exam dates, results, holidays, or
events to subscribed users.
6. Secure Admin Interface
A full-featured web panel where staff can log in to manage, update, and
monitor chatbot data.

By incorporating these future enhancements, the system can evolve into a

smart campus assistant capable of addressing not only FAQs but also
performing personalized academic services.

43
 11.BIBILOGRAPHY
11.1 REFERENCES

 W3Schools – HTML, CSS, and JavaScript Tutorials

Website: https://www.w3schools.com
Description: Comprehensive tutorials and references for HTML, CSS, and
JavaScript which helped in designing the chatbot interface and styling
the web elements.
 MDN Web Docs (by Mozilla)
Website: https://developer.mozilla.org
Description: Official and in-depth documentation of web technologies,
useful for understanding JavaScript events, DOM manipulation, and
frontend scripting for chatbot interactions.
 GeeksforGeeks – JavaScript Chatbot Development Guide
Website: https://www.geeksforgeeks.org
Description: Reference material for developing simple rule-based
chatbots and logic-building using JavaScript.
 Stack Overflow
Website: https://stackoverflow.com
Description: Community-driven platform that helped resolve specific
coding issues during chatbot implementation.
 FreeCodeCamp – Web Development Tutorials
Website: https://www.freecodecamp.org
Description: Provided foundational tutorials and project-based learning
content to build and debug the chatbot.
 YouTube – Chatbot Frontend Tutorials
Example Channel: Traversy Media, CodeWithHarry
Description: Videos explaining how to create chatbot UIs using HTML,
CSS, and JavaScript logic.
 Font Awesome
Website: https://fontawesome.com
Description: Used for incorporating chatbot icons and improving UI
aesthetics.
44
11.2 WEBSITES

 W3Schools – HTML, CSS, JavaScript tutorials

https://www.w3schools.com

 MDN Web Docs – Detailed documentation for web technologies

https://developer.mozilla.org

 GeeksforGeeks – JavaScript logic and web development

https://www.geeksforgeeks.org

 Stack Overflow – Community help and coding solutions

https://stackoverflow.com

 FreeCodeCamp – Project-based tutorials in web development

https://www.freecodecamp.org

 Font Awesome – Icons for UI

https://fontawesome.com

 CodePen – Frontend prototyping and chatbot UI ideas

https://codepen.io

 Chatbot Tutorials (YouTube) – e.g., Traversy Media

https://www.youtube.com/c/TraversyMedia

45