To study the Role of Artificial Intelligence in
Indian Education system
SYNOPSIS
For the Degree of
Doctor of Philosophy
In
COMPUTER SCIENCE AND ENGINEERING
Submitted to
MEWAR UNIVERSITY GANGRAR,
CHITTORGARH (RAJASTHAN)
Research Supervisor:
Research Scholar:
Dr. Ashok Kumar Jetawat
Purushottam Lal
2014
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1. Introduction
Artificial Intelligence (AI) is a branch of the computational intelligence of computer and
information science. Expert systems and knowledge base systems are work on the concept of
Artificial Intelligence. AI mainly focuses on developing hardware and software systems, that
systems are used for solve problems and accomplish tasks that if accomplished by humans would
be considered a display of intelligence. Some machines such as robots, automatic pilots for
airplanes and space ships, and “smart” military weapons are developing and studying using field
of Artificial Intelligence.
The theory and practice of Artificial Intelligence is leading to the development of a wide range of
educational tools. These tools, sometimes working under the guidance of a human and
sometimes without external guidance, are able to solve problems. Over the past 50 years,
Artificial Intelligence has produced many results that are important to purpose of students,
teachers, our overall educational system and for our society.
Computers have been used in education system for over 20 years. Computer-Based Training
(CBT) and Computer Aided Instruction (CAI) were the first such systems used as an attempt to
teach using computers in education system. In these types of systems, the instruction was not
individually used to the learner's needs. Today new technologies are used like Classpad which is
an android tablet for education in educational system, where teachers and students connection
gets maximum strength and achieving better the targets of better education. Each person has their
own ideas on what constitutes appropriate goals for education. Thus, this topic can lead to highly
debate and is currently a largest political issue. Some controversial issues are curriculum content,
assessment and instructional processes.
In brief summary, Artificial Intelligence is concerned with developing computer systems. They
can store knowledge and effectively use the knowledge to help solve problems and perform
tasks. This statement is very biggest issue like one of the commonly accepted goals in the
education of humans. We want students to learn (gain knowledge) and this knowledge to help
solve problems and accomplish tasks for purpose of learning students.
Some sources of an Artificial Intelligence system’s knowledge are:
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
Human knowledge that can be converted into a format suitable for use by an AI system.

Knowledge generated by an Artificial Intelligence system, using gathering data and
information, and by using analyzing data, information, and knowledge very useful.
2. Literature Survey
Artificial Intelligence is the technology in the knowledge base system and problem solving
world. It is becoming a best choice as an adoptable technology for much education system.
This is an excellent alternative solution for higher educational institutes in India. Artificial
Intelligence is the best term in biological study also but it comes from the past technologies
like Reasoning and knowledge based system etc. Artificial Intelligence is giving better features
and advantages for learners and they have learn more new techniques for solving problems.
This study refers to a variety of materials in order to carry out a thorough and comprehensive
literature review in relation to Artificial Intelligence. Resources are mainly used from books,
Research papers of international publications, academic journals, magazines, information on
World Wide Web etc.
Review of work already done in this field is as follow:
Stanley and Mudge (1995) present the application of a real valued multi-objective genetic
algorithm on an asynchronous parallel workstation network as an optimization approach well
suited to solve combinative complexity problem. Cache memory design problem of
microprocessor is optimized with the genetic algorithm.
Flockharta and Radcliffe (1996) presents an approach which as well as being useful for such
directed data mining, can also be applied to the further tasks of undirected data mining and
hypothesis refinement. Parallel genetic algorithms as the search mechanism and seeks to evolve
explicit “rules” for maximum comprehensibility by the using this approach.
Brusilovsky and Peylo (2003) mentioned that the Adaptive and Intelligent Web-Based
Educational Systems (AIWBES) attempt to be more adaptive by building a model of the
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targets, preferences and knowledge of each individual student and throughout the interaction
with the student in order to adapt to the needs of that student by the using this model. They also
attempt to be more intelligent by performing some activities traditionally executed by a human
teacher - such as diagnosing misconceptions of students. The biggest reason behind this is
interest to provide distance education over the Web. It provides an overview of known
AIWBES technologies classified by the field term of their origin. It also made to distill the new
design paradigm and to compare this paradigm with a traditional design paradigm.
Devedzic (2004) capitalized on Web Intelligence (WI) in the context of Artificial Intelligence
in Education (AIED). Web Intelligence play the fundamental roles as well as practical impacts
of Artificial Intelligence and advanced Information Technology on the next generation of Webrelated systems, products, activities, and services. As an instruction for scientific research and
development, Web Intelligence system can be most beneficial for the field of Artificial
Intelligence in Education system. It mainly focuses on issues in Web Intelligence, such as
intelligent Web services methods, semantic markup, and Web mining, and It is also an
important fact that how to use them as the basis for tackling new and challenging research
problems in Artificial Intelligence in Education System.
Kapur and Mehta (2004) want examines the political economy of Indian higher (tertiary)
education. According to them, the education system remains suspended between overregulation by the state on the one side, and a discretionary privatization that is unable to
mobilize private capital in productive ways. Sub-optimal structuring of higher education is the
positive result. The most important role of this is a secession of the middle class—ironically
the very class whose interests these institutions were supposed to serve—from a stake in public
institutions.
Saxena (2005) described a procedure to synthesize the optimal topology, size, and shape of
compliant continua for a given nonlinear output path. The topology synthesis problem is
addressed in its original binary form in that the corresponding design variables are only allowed
to assume values of “0” for no material and “1” for the material present at a site in the design
region. Continuous functions are used for modeling of Shape and size design variables. A
notable advantage of a genetic algorithm over its gradient based counterparts is the implicit
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circumvention of non convergence in the large displacement analysis, which is also a reason why
a genetic algorithm is chosen for optimization.
Duch (2006) gives a brief survey of the scope of Computational Intelligence with Artificial
Intelligence. Computational Intelligence is an umbrella for three core technologies (neural, fuzzy
and evolutionary), their applications, and selected fashionable methods. A part of computer
science devoted to solution of non-algorithmizable problems defines with Computational
Intelligence. Artificial Intelligence is a part of Computational Intelligence focused on problems
related to higher cognitive functions.
Blanchard and Volfson (2009) define that the development an Artificial Intelligence in
Education system that could detect and then intelligently manage the affective dimension of the
learner. Scherer’s framework as an extremely useful tool to disambiguate the definitions of
various affective phenomena as well as their interrelations with each other is introduced by
them. They found that mood has the strongest role in learning, while other affective
phenomena have a smaller but still important relationship with learning. Emotions and moods
play an important role in learning according to them.
Ganesan, Venkatesh, Rama and Palani, (2010) says that the Artificial Neural Network is a part of
Artificial intelligence, has been accepted as a new and powerful technology in computer science.
Neural Networks are currently a much better research area in medicine, particularly in the terms
of urology, cardiology, oncology, radiology and etc. It is also used in many areas such as
medical, engineering, education, business and manufacturing. Neural network mostly work on
the concept of decision support system. According to them the main aim of research in medical
diagnostics is to develop more cost-effective and make easy to use systems, methods and
procedures for supporting clinicians.
Thielscher (2011) define that the general problems are solved by the using general game
playing technique. It provides a unique approach to teaching a number of different topics such
as problem solving by searching method, logic, logic programming and planning. The inherent
competitive facts also can be used as a great motivator for students to design and implement
their own Artificial Intelligence systems.
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Sasikumar (2012) mention some of the challenges as Artificial Intelligence meets Education
system. To represent relevant knowledge and efficient algorithms to make use of that knowledge
for solving problems, Artificial Intelligence is an efficient way.
Nicholas (2012) suggested that successful induction of Human-Level Artificial Intelligence.
Human-level Artificial Intelligence is must be an extraordinary science because Understanding
and creating human-level intelligence are tremendously important scientific and technological
objectives. The notion of a cognitive substrate motivates a different set of standards and methods
for research towards Human-Level Artificial Intelligence. These include “quasi-formal” research
into linguistic semantics, reductions between reasoning problems in differing domains, the
development of microcosms for evaluating research, and constructing systems that add multiple
abilities.
Kumar and Sharma, (2012) introduce to Neural Computing and Genetic Computing or
Evolutionary Computing. The extended version of Artificial Intelligence by augmenting it with
emotions, and inheritance of neural architecture from parent generation to child generation that
can make an artificial agent to match the intelligence and behavior of a human being and at the
same time it adds the power of two well knows Artificial Intelligence techniques, they are Neural
Computing and Genetic Computing. They gives an idea of an artifact which is supposed to match
the intelligence and behavior of a human being.
Sattikar and Kulkarni, (2012) define that the Artificial Intelligence is a scientific way which tries
to understand human intellectual and cognitive capabilities in order to make them available by
the using information processing systems. Nowadays, some security and privacy problems can
not be easily solved, because their complexity is high. In these situations, artificial intelligence
has proven to be extremely useful and well suitable to solve these problems. Artificial neural
networks, evolutionary computation, clustering, fuzzy sets, multi-agent systems, data mining and
pattern recognition are just a few examples of artificial intelligence techniques that can be
successfully used to solve some relevant privacy and security problems.
Kumar and Thakur (2012) focusing on applications which uses Artificial Neural Networks and
Artificial Intelligence. Some general applications available in neural networks are mostly used
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for problem solving are in pattern recognition, data analysis, control and clustering. The main
point of research work of them is the recent applications of Neural Networks and Artificial
Intelligence and provides an overview of the field, where the Artificial Intelligence and Artificial
Neural Networking used mostly.
Helgason (2013) define about ‘attention’ by the using Artificial Intelligence. In the intelligent
systems, the management of mental resources is basically called attention. Attention exists from
the complex environments and the real-world environments of everyday life. Which information
will be processed and which will be ignored, it is decide by the using General-purpose Artificial
Intelligence (AI) systems operating with limited resources under time-constraints. Even
sufficient resources may be available in rare cases, so the attention could help make better use of
them. All real world tasks have time limits and managing these is an important part of the role of
intelligence. We examine several issues related to attention and resource management by the
using Artificial General Intelligence (AGI) Architectures. Present a design for a general attention
mechanism for AGI systems.
Jouault (2013) define aim to give learners more content-dependent scaffolding in the selfdirected learning of history. A system used by learners to build a concept map containing a
chronology. The system is more able to generate content dependent support adapted to the
needed learners. To start this support, we use a natural language online encyclopedia for built a
semantic open learning space and use the open linked data for semantic information. The support
is provided by the automatically generated questions and documents. The learners request
questions when they need and the system will generate the questions depending on the concept
map of the learner. The generated questions target to leads the learners to new knowledge
deepening their understanding.
Yoo and Kim (2014) point on a fact that many college courses adopt online tools such as
Q&A(Question and Answer) online discussion boards, there is no any easy way to evaluate their
effect on learning. We investigate a predictive relation between characteristics of discussion
contributions and student performance by the using online discussion. Convinced by existing
work on dialogue acts, project based learning system and instructional analysis of studentgenerated text in generating predictive models, we make use of linguistic features, roles of
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dialogue and work patterns. In particular, we model the Q&A(Question and Answer) dialog roles
that participants play, emotional features covered by LIWC (Linguistic Inquiry and Word
Count), cohesiveness of the dialogue and temporal patterns of participation The correlation and
regression analyses indicate that the number of answers provided to others people, the positive
emotion expressions, and how students communicate their problems before the deadline correlate
with project grades.
3. Objectives and Scope/Limitations of the present Investigations/Study
The purpose of this research is to study the Artificial Intelligence with a view to explore the new
techniques and methods in education system. The study mainly caters to analyze the traditional
educational system and potentiality of Artificial Intelligence implementation.
The objectives of our research as follows:
(i) To study the basic concept of Neural Networking, Genetic Algorithms and Human
Intelligence.
(ii) To study the basic concept of Artificial Intelligence and Knowledge Base System.
(iii) To study the role of Artificial Intelligence in Indian Education System.
(iv) To study the future potential trends, issues and suggestions of Artificial Intelligence
in Indian Education System.
Thus the above-mentioned objectives represent the whole of our research work. These objectives
are supposed to lay down the foundation of our research.
Scope of the Proposed study:
Our study examines the issues for implementation of Artificial Intelligence in Education System.
The proposed study is supposed to bring into Artificial intelligence methods perspective for
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future generation. Our study will also try to find out the possibilities of providing higher
education to the working professionals through Artificial Intelligence.
Artificial intelligence can be used for grading, Educational software according to student needs
and it can point out places where courses need to improvement. . It could change the role of
teachers in education system, like AI-driven programs can give students and educators helpful
feedback and interact with information. Artificial Intelligence may change where students learn,
they teaches by whom, and how they acquire basic skills. Data powered by Artificial Intelligence
can change how find schools, teach, and support students.
The key to improving the effectiveness and quality of student learning and making it worthwhile
is to replace existing traditional modes of teaching with more active and engaging learning
opportunities, delivered where appropriate by Artificial Intelligence.
4. Research Methodology/ Requirements of the Research Work
Research is a structured enquiry that utilizes acceptable scientific methodology to solve problems
and create new knowledge that is mostly applicable. Scientific methods are most important and
they consist of systematic observation, interpretation and classification of data. Although we
mostly engage in such process in our life, the difference between our casual day to day
generalization and the conclusions usually recognized as scientific method lies in the degree of
formality, verifiability, rigorousness and general validity of latter.
Artificial intelligence play an important role in today’s Education System
A hypothesis is a proposed explanation for a phenomenon. The hypothesis is an assumption
about the relations between variables. It is a explanation of the research problem or a guess about
the research output.
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Research Design:
A good design is often characterized by adjectives like appropriate, flexible, efficient,
economical and so on. Generally, the design which maximizes the reliability of the data collected
and analyzed is considered a good design. The question of good design is related to the purpose
or objective of the research problem and also with the nature of the problem to be studied.
Designing and planning is most important in all kind of research work. In study of Artificial
Intelligence with Education, we first find out current methods they are used in education and
then design a structure of research for find out a new method.
Sampling Design:
It refers to the technique or the procedure the researcher would adopt in selecting items for the
sample design methods. There are many types of sample designs from which we choose random
sampling in this research work.
Data Collection strategy:
The data will be collected from both primary and secondary sources. For critical analysis and
implementation of Artificial Intelligence with Education we will study current methods to know
about running technologies of Artificial Intelligence with education system and will collect data.
For collection of primary data, survey and questionnaire method will be followed. Apart from
that structured questionnaire we will also be prepared for conducting the survey. Individual and
group interview method shall also be used for collecting primary data. Some sources like
magazines, journals, books, newspapers, internet etc. are sources of secondary data collection.
Planning of analysis of Data:
Various statistical tools shall be used to analyze the data. With the analysis of data the proposed
hypothesis shall be tested for its significance and conclusion shall be drawn out. The detailed
analysis of various primary and secondary data collected, will be done. Potential of Artificial
intelligence with education in India shall be explored. Further with the help of analysis
conclusions shall be drawn.
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Plan about time scheduling of Research:
The research work shall contain five parts. The research work will get started with an overview
of Artificial Intelligence in Education, followed by detailed description of Artificial Intelligence
tools which play a crucial role in implementation of education environment. There by discussing
the various views of experts on Artificial Intelligence. Further it will be continued to the core of
the study i.e. research methodology. The fourth part shall fulfill the objective of our study, there
by moving to a stage where we can test our hypothesis and draw conclusion.
5. References/Bibliography
(A)Papers and Publications:
Beck, J., Stern, M. and Haugsjaa E.(2005), 'Applications of AI in Education', Applications of AI
in Education.
Moursund, D. (2006), 'Brief Introduction to Educational Implications of Artificial Intelligence',
Version 4/24/06. (Contains minor changes and Web references update from version12/23/05.).
Stanley, J. and Mudge, T. (1995), ‘A Parallel Genetic Algorithm for Multiobjective
Microprocessor Design’, Proceedings of ICGA-95: 6th International Conference on Genetic
Algorithms, July 1995, pp. 597-604.
Flockharta, W. and Radcliffe, J. (1996), ‘A Genetic Algorithm Based Approach to Data Mining’,
Presented at "AAAI: Knowledge Discovery and Data Mining 1996", Portland, Oregon.
Department of Mathematics and Statistics, University of Edinburgh, King’s Buildings, EH9 3JZ,
UK.
Brusilovsky,P. and Peylo, C. (2003), ‘Adaptive and Intelligent Web-based Educational Systems’.
International Journal of Artificial Intelligence in Education 13 (2003), pp. 156–169.
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Devedzic, V. (2004). ‘Web Intelligence and Artificial Intelligence in Education’, Educational
Technology & Society,7 (4), pp. 29- 39.
Kapur, D. and Mehta, B. (2004), ‘Indian Higher Education Reform: From Half-Baked Socialism
to Half-Baked Capitalism’, CID Working Paper No. 108, September 2004, pp. 1-36.
Saxena, A. (2005), ‘Synthesis of Compliant Mechanisms for Path Generation using Genetic
Algorithm’, Mechanical Engineering, Indian Institute of Technology, Kanpur 208016, India.
Journal of Mechanical Design JULY 2005, Vol. 127, pp. 745-752.
Duch, W. (2006), ‘What is Computational Intelligence and what could it become?’, Department
of Informatics, Nicolaus Copernicus University, Grudzia¸dzka 5, Toru´n, Poland, and School of
Computer Engineering, Nanyang Technological University, Singapore.
Blanchard, G. and Volfson, B. (2009), ‘Affective Artificial Intelligence in Education: From
Detection to Adaptation’, ATLAS Laboratory, McGill Faculty of Education, Montréal (QC),
CANADA.
Ganesan, N., Venkatesh, K., Rama, M. and Palani, A. (2010), ‘APPLICATION OF NEURAL
NETWORKS in DIAGNOSING CANCER DISEASE using Demographic Data’, International
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Thielscher, M. (2011), ‘General Game Playing in AI Research and Education’, School of
Computer Science and Engineering The University of New South Wales.
Sasikumar, M. (2012), ‘Role of Artificial Intelligence in Enhancing the Effectiveness of
Education’, Director (R&D), Centre for Development of Advanced Computing. pp. 12-16
Nicholas, L. Cassimatis (2012), ‘Human-Level Artificial Intelligence Must Be an Extraordinary
Science’, Department of Cognitive Science, Rensselaer Polytechnic Institute, Troy, NY 12180
USA, pp. 37-45.
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Kumar, S. and Sharma, M. (2012), ‘Convergence of Artificial Intelligence, Emotional
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and Privacy Issues of Social Networking’, IJCSET |January 2012| Vol 2, Issue 1, pp. 792-795.
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Conference on Computers in Education. Indonesia: Asia-Pacific Society for Computers in
Education.
Yoo, J. and Kim, J. (2014), ‘Can Online Discussion Participation Predict Group Project
Performance? Investigating the Roles of Linguistic Features and Participation Patterns’,
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8-32.
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