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Adoption of AI-Chatbots to Enhance Student
Learning Experience in Higher Education in India
Nitirajsingh Sandu
School of Engineering and Technology
CQUniversity
Sydney, Australia
r.sandu@cqu.edu.auErgun Gide
Ergun Gide
School of Engineering and Technology
CQUniversity
Sydney, Australia
e.gide@cqu.edu.au
Abstract—Today, every organisation depends on
Information and Communication Technology (ICT) for the
efficient service delivery and cost-effective application of
technological resources. With growing preference towards
faster services and acceptance of Artificial Intelligence (AI)
based tools in business operations globally as well as in India,
the global Chatbot market is going to accelerate in the next
decade. In the era of AI, the Chatbot market is witnessing
extraordinary growth with the increased demand for
smartphones and increased use of messaging applications. In
the past few years, the food delivery business, finance and the E-
commerce industry have embraced Chatbot technology.
One of the industries which can really benefit from using
this technology is the educational sector. Education can benefit
from Chatbot development. It can improve productivity,
communication, learning, efficient teaching assistance, and
minimize ambiguity from interaction. A new education platform
can solve next-level problems in education using this technology
as the engagement tool.
The aim of this research paper is to find out the factors
which affect the adoption of Chatbot technology in order to
enhance the student learning experience in the Indian higher
education sector. In this research, a Quantitative method is used
through data collection from surveys of some of the prominent
higher education institutes using Chatbot technology in India. It
is expected that the research outcome will help Chatbot
developers and higher education providers to better understand
the requirements of students while providing an interactive
learning and communication platform for them.
Keywords— Chatbot, Artificial Intelligence, Student-centred
learning, higher education, India
I. INTRODUCTION
“Education 4.0” involves the integration of AI in the
learner-centred education system [1]. The learner-centred
education system was an upgrade from the traditional
education system that was tutor centred [2]. Advancement in
the education sector is essential to accommodate evolving
lifestyles, economy, technology and student’s needs. Also, the
increased scarcity of teachers in the education system have
made the integration of advanced technology in our education
system essential. Research indicates that Chatbots will assist
in solving some of the current challenges facing the education
sector [3]. This paper discusses the integration of AI centred
Chatbots in the education system. The paper, through the
literature review and data analysis, discusses the current state
of Chatbot usage in India and addresses the differences
between the traditional education system and the AI-Chatbot
education system.
II. LITERATURE REVIEW
A. Artificial Intelligence
Current technological advancements have placed artificial
intelligence (AI) at the focal point of research and innovation
[1]. The integration of AI in our lives requires the distinction
between weak AI and Artificial General Intelligence (AGI).
Weak AI refers to computer programs developed to solve
specific problems like playing chess or conducting facial
recognition. The programs in weak AI employ AI techniques
such as data mining and machine learning. AGI refers to
flexible machines that can provide solutions to problems just
like a human being [4]. The majority of current AI inventions
mostly dwell on weak AI and a few on AGI. Georgescu [5]
observes that the adoption of AGI is still in the initial stages
but much advancement is expected within the next two
decades. Integration of AI in the education system requires the
use of AGI. However, the main questions that require more
attention are how to address the diverse ethical and cultural
backgrounds, and how to develop programs that are less
directive but allow students to be in control instead of vice
versa.
A Chatbot is an artificial intelligence (AI) based software
program that is able to simulate a conversation with the user
using natural language through messaging platforms, phone
applications and websites [6] [7]. Users interact with Chatbots
that have a conversational user interface (CUI), which allows
users to interact with the bot. This means that the users do not
have to download any applications onto their devices or
launch any specific applications [8]. CUI are intuitive and
easy to use.
B. Chatbots
Chatbots are classified using different parameters such as
the knowledge domain, service provided, goals, and responses
generated [9]. The knowledge domain is based on the
knowledge the Chatbot accesses. There are two types of
domains; the open and the closed domains. The open domain
Chatbots address general topics and respond appropriately to
general questions. The bots in the closed domain address
specific knowledge domains and may fail to respond to
questions from other domains [10]. The service-based
Chatbots are categorized into those that offer interpersonal,
intrapersonal and inter-agent services [7]. The goal-based
Chatbots are further categorized under the informative,
conversational and task-based Chatbots. The last category
includes the Chatbots based on the input method and the
responses generated [11] . There are Chatbots that accept
input, and process and generate output in natural language,
and others that are rule-based as they process input based on
rules. Other Chatbots in this category are a hybrid as they use
natural understanding and rules to process input and generate
output [10].
C. Usage of Chatbots in India
Innovation is required in a developing country like India,
in order to develop quality education and create a workforce
to compete globally [12]. India has widely embraced the use
of Chatbots in various sectors and it is a key player in the
Chatbot market. In the banking sector, Chatbots are used to
handle customer queries (and FAQs) and give guidance on
bank services and products [13]. Chatbots in the banking
sector include SIA, iPal and EVA by the State Bank of India,
ICICI Bank and HDFC Banks respectively[14]. In the
insurance sector, Chatbots are assisting customers in filing
claims, getting policies, checking the status of their policies,
and locating providers and their branches, as well as other
service providers [8]. Baja Allianz’s Boing, Birla Sunlife’s bot
and PNB Metlife’s banking applications are among the
commonly used Chatbots in this sector [15].
In the transport sector, Chatbots are used to provide real-
time cab details, flight bookings and verifications, and traffic
analysis [16]. Meru Cab and Yatra.com Chatbots are among
the bots used in the transport sector [17]. In the ecommerce
sector, Chatbots have been used in handling queries, tracking
orders, making payments and raising customer complaints
[14].
D. AI-bot for the educational system
Chatbots have been in use for educational purposes for
quite some time. These Chatbots can be categorized into those
with education intentionality and those without. Chatbots
without education intentionality are used in administrative
tasks such as student guidance and assistance [14]. Chatbots
with education intentionality are used in fostering teaching
and learning. Within this category, there are Chatbots which
provide the framework of the learning process, that is, select
and arrange contents to fit the students’ needs and speed, and
help in reflection and learning motivation. These bots act as a
learning companion which provides dialogue, collaboration
and reflection [1]. Furthermore, there are exercise and practice
Chatbots that present a stimulus in question form, to which the
student provides an answer that is assessed by the Chatbot
which then provides feedback [18].
Chatbots enhance dialogic learning as it is based on a
communicative exchange between the bot and the student.
According to Fleming, the interaction between the Chatbot
and the student consists of the following elements: initiation,
response, and feedback (IRF) [18]. The Chatbot initiates the
conversation by asking questions and based on the response
the student gives, the Chatbot provides feedback. These
Chatbots also provide interaction among students and this
interaction has an additional element of discussion (D) and
thus we have IDRF [19]. The Chatbot initiates a conversation
by asking a question, the students discuss the question and
give a response, and the bot provides feedback.
The Chatbots in education perform different tasks such as
handling FAQs (Frequently Asked Questions), administrative
and management tasks, student mentoring, motivation,
student learning assessments, simulations, training specific
skills and abilities, and providing reflection and metacognitive
strategies [6].
CUE Cardenal Herrera University has a Chatbot for
mentoring students and providing answers to student queries
[20]. The Chatbot acts as a personal assistant for handling
administrative queries, although more enhancements are
being conducted to enable the Chatbot to predict student
behaviour and give advice to them during the learning process.
Based on the Literature review, the differences between the
traditional system and the AI bot system are shown in Table I.
TABLE I. DIFFERENCE BETWEEN THE TRADITIONAL SYSTEM AND THE AI
BOT SYSTEM
Teacher Centred Learning/
Traditional System
AI-Bot System
Focus is on the instructor
The centre focus is on both the
student and tutor
System is built on what the
instructor knows about
language forms and
structures
The system focuses on how the
student will use the language
Depends on passive learning
Incorporates both active and
passive learning styles
Students rely on the teacher
There is interdependence
between the teacher and the
student
The tutor is responsible for
the student’s excellence
The student is solely responsible
for his/her excellence
Online platforms are just
repository areas
Online platforms accommodate
interaction and experimentation
The learning environment is
centred on the curriculum
The learning environment is
centred on the student’s profile,
learning experiences and needs
The student’s behaviour is
not a factor in the
formulation of the
pedagogical model
The student’s behaviour is
modulated to tailor the
pedagogical model to the
learner’s model
The learning environment is
not adaptive
There is implementation of
adaptive learning environments
III. PROBLEM STATEMENT
Technological advancement has enabled organizations to
conduct their daily businesses in an effective and efficient
manner. The information and communication technology
have been adopted to facilitate effective and timely delivery
of services in different public and private sections in India
including higher education. It has been established that
Chatbot development can improve learning, communication,
and productivity, as well as provide efficient teaching
assistance and minimize ambiguity. Consequently, this study
aims at establishing the factors which affect the adoption of
Chatbot technology to enhance the student experience in the
Indian higher education sector.
3.1 Research Questions
This study is guided by the research question: How can
the Indian education sector benefit from adopting Chatbot
technologies?
3.2 Study Hypotheses
• There is no significant statistical difference in the
adoption of Chatbot technology by gender of the
respondent.
• The level of education of the respondent does not affect
the adoption of Chatbot technology in the Indian higher
education sector.
• There is no significant difference in the adoption of
Chatbot technology by age of the respondents.
IV. METHODOLOGY
An empirical research design was used in the study.
Quantitative method was used through data collection from
surveys of some of the prominent higher education institutes
using Chatbot technology to explore the factors that influence
the adoption of Chatbot technology in Indian higher
education. Stratified random sampling was used to select a
sample of 47 students for the study, and questionnaires were
administered.
A series of 10 questions were included in the
questionnaire. The data collected was on demographic
information as well as data on Chatbot use. Information on the
students’ age, gender, and their highest level of education was
collected. Additionally, the respondents gave information on
whether they had communicated with their educational
institute in the past 12 months, the quickest response time by
communication channel by their institute, the most likely
predicted use cases for Chatbots in their educational institute,
the barrier to using Chatbots compared to other methods of
communication, whether they would talk to Chatbots to get
help with their educational issues, and whether they would be
less likely to use other forms of communication if they were
chatting with Chatbot.
V. DATA ANALYSIS RESULTS
Data was recorded in excel and exported to SPSS for
analysis. Descriptive statistics and inferential statistics were
computed to achieve the objectives of the study.
5.1 Descriptive Statistics
The study sample comprised of 24 male and 23 female
respondents as shown in Table II. It shows the age distribution
of the respondents. The majority of the respondents were aged
between 25 and 34 years (53.2%), with 34% of respondents
aged between 18 to 24 years, 6.4% under 18 years, and 6.4%
aged between 35 to 44 years.
TABLE II. GENDER OF THE RESPONDENTS
Gender
Frequency
Valid
Percent
Cumulative
Percent
Male
24
51.1
51.1
Female
23
48.9
100.0
Total
47
100.0
Figure 1 shows the highest level of education for the
respondents, with 38.3% having completed a master’s degree
(M.A., M.B.A.), 36.2% having a bachelor’s degree (B.A,
BSc), 17% having completed high school, and 2.1% having a
doctorate. Results show that most of the people using Chatbots
are educated with a Bachelor or Master level of studies, with
few having a high school level education. The lowest level of
usage was by people with a doctorate degree.
Figure 1: Highest level of education
Additionally, the majority of the students would talk to
Chatbot to get help with their educational issues as shown in
Table III. Furthermore, a majority (93.6%) of the students
would be less likely to use other forms of communication if
they were chatting with Chatbot as shown in Table III.
TABLE III. WOULD YOU BE LESS LIKELY TO USE OTHER FORMS OF
COMMUNICATION IF YOU WERE CHATTING WITH CHATBOT
Frequency
Percent
Valid
Percent
Cumulative
Percent
5
1
2.1
2.1
2.1
7
2
4.3
4.3
6.4
8
7
14.9
14.9
21.3
9
29
61.7
61.7
83.0
10
8
17.0
17.0
100.0
Total
47
100.0
100.0
5.1.1 Students Communication with Their Education Institute in
the Past 12 Months
On the question of how the students have communicated
with their educational institute in the past 12 months, 48.9%
indicated that they used Chatbot while 51.1% used other
communication channels with their institution, as shown in
Table IV.
TABLE IV. USE OF CHATBOT TO COMMUNICATE WITH EDUCATIONAL
INSTITUTE IN THE PAST 12 MONTHS.
Frequency
Percent
Valid
Percent
Cumulative
Percent
no
24
51.1
51.1
51.1
yes
23
48.9
48.9
100.0
Total
47
100.0
100.0
5.1.2 Quickest Response Time by Communication Channel
On the quickest response time by communication channel
by the institute, the majority of the students (60.9%) indicated
Chatbot as the quickest communication channel followed by
online chat with 19%, 8.7% for the telephone, 6.5% for face-
to-face, and 2.2% each for social media and email, as shown
in Figure 2. This indicates that Chatbot technology is
appreciated as having the quickest response time.
Figure 2: The quickest response time by communication channel by
students’ institute
5.1.3 The Most Likely Predicted Use Cases for Chatbots
On the most likely predicted use cases for Chatbots in the
educational institute, 10.6% of the respondents indicated that
Chatbots are used for tutoring, 48.9% for learning feedback,
68.1% for paying fees, 68.1% for being more convenient than
other methods of communication, 76.6% for resolving a
problem, and 51.1% of the students indicated that the most
likely predicted use cases for Chatbots in the educational
institute is for getting a quick answer. The results indicate that
a Chatbot is most likely used by students in resolving a
problem as well as due to its convenience in comparison to
other communication methods, and for paying their fees as
shown in Figure 3.
Figure 3. Most likely predicted use cases for Chatbots
5.1.4 Barriers to Using Chatbots as Compared to Other Methods
On the barriers to using Chatbots compared to other
methods of communication, 21.3% of the students indicated
that Chatbots had limited intelligence and hence were not
suitable for solving issues, 63.8% preferred to use a normal
website as it is easier to use than Chatbots, 55.3% preferred to
deal with real assistants (personal Touch), 66% risked losing
personal information (privacy issues), and 77.8% were
worried about receiving incorrect advice. The foregoing
results indicate that the highest number of students (77.8%)
were worried about getting incorrect advice by using
Chatbots. The other barrier to using Chatbots, the risk of
losing personal information (privacy issues), also had a higher
percentage of 66%. Additionally, the preference of using a
normal website for its easy usage also had a high number of
students, as shown in Figure 4.
Figure 4. Barriers to using Chatbots compared to other methods of
Communication
5.2 Inferential Statistics
To establish whether there is a relationship between the
adoption of Chatbot technology and gender of the respondent,
the following hypotheses were formulated;
Ho: There is no significant statistical difference in the adoption of
Chatbot technology by gender of the respondent.
Ha: There is a significant statistical difference in the adoption of
Chatbot technology by gender of the respondent.
Chi-Square tests were conducted at 0.05 level of
significance. The p-value for Pearson Chi-Square was 0.188,
which is greater than 0.05. The null hypothesis was not
rejected. It is concluded that gender does not affect the
adoption of Chatbot technology, as shown in Table V.
Table V: Chi-Square Test results for gender
Value
df
Asymptotic
Significance
(2-sided)
Exact
Sig.
(2-
sided)
Exact
Sig.
(1-
sided)
Pearson
Chi-Square
1.733a
1
.188
Continuity
Correction
1.050
1
.306
Likelihood
Ratio
1.744
1
.187
Fisher's
Exact Test
.248
.153
N of Valid
Cases
47
The relationship between age and adoption of Chatbot
technology was tested using the Chi-Square test, and the
results showed a Pearson Chi-Square p-value of 0.226, which
is greater than 0.05. The null hypothesis was not rejected, and
it is therefore concluded that the age of the respondents has no
significant effect on the adoption of Chatbot technology in the
Indian higher education sector. Furthermore, the level of
education of the respondent does not affect the adoption of
Chatbot technology in the Indian higher education sector as
indicated by a Pearson p-value of 0.388 (greater than 0.05
level of significance).
VI. DISCUSSION AND CONCLUSION
The aim of this research paper was to establish the factors
which affect the adoption of Chatbot technology to enhance
the student experience in the Indian higher education sector.
A Quantitative method was used through data collection from
surveys of some of the prominent higher education institutes
using Chatbot technology. The study sample comprised of 24
male and 23 female respondents. Descriptive statistics
established that the majority of the students would talk to
Chatbot to get help with their educational issues and they were
less likely use other forms of communication if they would be
chatting with Chatbot.
On the question on how the students have communicated
with their educational institute in the past 12 months, 48.9%
indicated that they used Chatbot. Furthermore, the majority of
the students indicated Chatbot as the quickest communication
channel. The results further indicate that Chatbots are most
likely used by students in resolving problems, due to its
convenience in comparison to other communication methods,
and for paying their fees. However, results indicate that the
biggest concerns for students involved getting incorrect
advice by using Chatbot, and risking losing personal
information (privacy issues).
The Pearson Chi-Square tests established that there was no
relationship between gender, age and level of education of the
students, and the adoption of Chatbot technology in the Indian
higher education sector.
The integration of AI-Chatbot in the education sector will
facilitate the achievement of student-centered learning. The
above research indicates that students are embracing use of
Chatbots and immense benefits have been achieved. Chatbots
can assist students to communicate well, conduct research and
mark online exams.
VII. FUTURE STUDIES
Student-centered education focuses on the student. The
introduction of AI-Chatbots to replace teachers means that
students will interact with the Chatbots more often than with
teachers. Therefore, more research needs to be conducted
regarding the negative effect of using technology, such as
addiction. Strategies needs to be set aside to ensure that AI
does not end up controlling students. While this study is based
on the Quantitative method, future research can also use
Qualitative or a mixed method approach for more insight on
students’ experiences using Chatbots in the educational
institute.
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