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Dissertation Topics In Artificial Intelligence

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Dissertations for Artificial Intelligence

  • Arroyo, Ivon M.

    Quantitative evaluation of gender differences, cognitive development differences and software effectiveness for an elementary mathematics intelligent tutoring system

    Ph.D. thesis, University of Massachusetts Amherst.

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  • Beitel, Sharon Epple

    Applying artificial intelligence data mining tools to the challenges of program evaluation

    Ph.D. thesis, University of Connecticut.

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  • Cao, Lilian

    Designing Web-based adaptive learning environment: DISTILS as an example

    Ph.D. thesis, New Jersey Institute of Technology.

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  • Chen, Cong

    Intelligent coaching agents for enhancing helping behavior in human teamwork

    Ph.D. thesis, The Pennsylvania State University.

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  • Chen, Dongyu

    Knowledge base system in DiscMath II

    Master's thesis, University of New Brunswick .

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  • Colon, David L.

    Ubiquitous learning laboratory for pediatric nursing: A cultural algorithm approach

    Master's thesis, Wayne State University.

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  • Daigle, Alain

    Systeme de formation d'operateurs de reseaux par realite virtuelle

    Master's thesis, .

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  • Dalkir, Kimiz Lutfiye

    Improving user modeling via the integration of learner characteristics and learner behaviors

    Ph.D. thesis, Concordia University .

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  • Erkunt, Hamdi

    Computers as cognitive tools in musical composition

    Ph.D. thesis, Boston University.

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  • Fleming, Susan Clark

    A comparison of artificial intelligence-based asynchronous Internet instruction and traditional instruction in community college developmental algebra

    Ph.D. thesis, University of Virginia.

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  • Floryan, Mark

    Evolving expert knowledge bases: Applications of crowdsourcing and serious gaming to advance knowledge development for intelligent tutoring systems

    Ph.D. thesis, University of Massachusetts Amherst.

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  • Gibson, Ronald H.

    Comparison of training hard copy and computer job-aids: Using expert object technology

    Ph.D. thesis, The University of Tennessee.

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  • Glenn, Susan Germaine

    The effects of a situated approach to musical performance education on student achievement: Practicing with an artificially intelligent computer accompanist

    Ph.D. thesis, University of Georgia.

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  • Grimes, Douglas C.

    Middle school use of automated writing evaluation: A multi-site case study

    Ph.D. thesis, University of California, Irvine.

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  • Hence, Bernadette McAfee

    Design and evaluation of a knowledge-based expert system as a decision support tool in selecting statistical methods

    Ph.D. thesis, University of Houston.

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  • Hennigan, Thomas Anthony

    Multiple intelligence and artificial intelligence: Educational implications of computers for learning interacting with multiple intelligences

    Ph.D. thesis, University of Idaho.

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  • Howard, Hugh H.

    Development of an expert system for cartographic design education

    Ph.D. thesis, University of Kansas.

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  • Huang, Mu-Jung

    Integrating fuzzy Hasse diagram with multistrategy learning for student modeling in intelligent tutoring systems

    Ph.D. thesis, Stevens Institute of Technology.

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  • Hurst, Karen Cecile

    Artificial neural network analysis of student problem-solving performances in microbiology and immunology

    Doctor of Philosophy thesis, University of California, Los Angeles.

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  • Jeng, Cheng-Chang

    Chinese readability analysis using artificial neural networks

    Doctor of Education thesis, Northern Illinois University.

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Artificial Intelligence is the latest trending field in computer science. This technology aims to create such machines that can act, work and think like human beings. The idea of artificial intelligence is achieved by deep research and study on how human brain thinks, work and make decisions while solving computational problems. The result of this study is the base of artificial intelligence. Artificial Intelligence is a good topic for an M.Tech thesis. Apart from M.Tech thesis, this topic can also be chosen for computer science projects and seminars. Many of the students are not aware of this relatively new field. You can choose this field as your M.Tech thesis topic. If you really want to choose this field, then here is the beginner’s guide for you.

What is Artificial Intelligence?

In simple language, Artificial Intelligence is the science of creating intelligent machines and intelligent computer programs that can think and act like human beings. The idea of artificial intelligence is based on the human philosophy that whether a machine can be as intelligent as the human. The term ‘artificial intelligence’ was coined by an American scientist named John McCarthy in 1956.

Goals of Artificial Intelligence

  • To create a system that is expert in every form – behavior, learning, demonstration, explanation and can give valuable advice to its users.

  • To extend and implement human thinking and intelligence in machines.

Types of Artificial Intelligence

Following are the types of artificial intelligence implemented:

  • Expert Systems

  • Fuzzy Logic

  • Neural Networks

  • Robotics

Expert Systems

An expert system is a powerful system that imitates the human behavior like decision making, reasoning. Expert System solve complex problems using the concept of if-then rules. It consists of the following elements:

  • User Interface – It acts as the intermediate to the user of the expert system and the expert system.

  • Inference engine – It is a component of an expert system that applies logical rules and procedures to the knowledge base to provide a solution to a problem and to deduce new information.

  • Knowledge base – It is a collection of high quality and precise knowledge related to a specific domain to execute intelligence. In other words, a knowledge base is a collection of facts and rules to exhibit intelligence.

Fuzzy Logic

Fuzzy Logic is a concept of reasoning similar to human reasoning. It emulates the way human make decisions involving possibilities of YES/NO or True/False. In this system, a logical block takes an input and produces a definite output in the form of True or False. The fuzzy system works on possibilities. Fuzzy Logic system has four main parts:

  • Fuzzification Module

  • Knowledge Base

  • Inference Engine

  • Defuzzification Module

Neural Networks

Artificial Neural Networks imitates the real neural network of human beings. In simple terms, Artificial Neural Network(ANN) mimics the working of the human brain. A human brain consists of millions of nerve cells known as neurons. These neurons are connected to other neurons by axon. The dendrites accept stimuli from the external environment and produce electrical impulse. One neuron can send information to other neurons.

In ANN, different nodes imitate biological neurons. The nodes are connected to each other by links for interaction. A node accepts input data and perform operations on it to produce an output known as node value. A particular weight is associated with each link.


Robotics is a branch of artificial intelligence that deals with the creation of intelligent and systematic agents known as robots. A robot is an artificial agent that work in real world environment by perceiving its surroundings. A robot can see by employing the concept of computer vision. Through computer vision, a robot can extract valuable information from a single picture. The main aim of robotics is to minimize the manpower employed in manufacturing and construction. Robots take input in the form of analog signals like speech and images. Components of a robot are:

  • Power Supply

  • Actuators

  • Electric Motors

  • Sensors

  • Muscle Wires

  • Ultrasonic Motors

These were the types of artificial intelligence systems.

Natural Language Processing in AI

Natural Language is a branch of artificial intelligence that deals with the interaction of computers with the human language. In other words, it is the ability of computer programs to understand human language. Following are the two components of Natural Language Processing:

  • Natural Language Understanding – Understanding human language

  • Natural Language Generation – Generating human language

Natural Language Processing is based on the following steps:

  • Signal Processing

  • Syntactic Analysis

  • Semantic Analysis

  • Linguistics

Agents and environments in Artificial Intelligence

An agent is something that can sense its environment through sensors and work in that environment through effectors. Following are the types of agents in artificial intelligence:

  • Simple Reflex Agents

  • Model Based Reflex Agents

  • Goal Based Agents

  • Utility Based Agents

Following are the types of environment:

  • Discrete/Continuous

  • Static/Dynamic

  • Deterministic/Nondeterministic

Bayesian Network in Artificial Intelligence

It is also known as belief network and belong to the class of probabilistic graphical models. In Bayesian Network, there are nodes and edges. A node represents a random variable while edges represent the probabilistic dependencies between other nodes. Bayesian network is based on inference and learning. It is a type of Directed Acyclic Graph(DAG).

Applications of Artificial Intelligence

Artificial Intelligence finds its application in the following areas:

  • Medical Diagnosis

  • Remote Sensing

  • Air Traffic Control

  • Industries

  • Construction and Manufacturing

  • Telecommunication

  • Engineering

Examples of Artificial Intelligence

Following are some of the real life examples of artificial intelligence:

  • Siri in iPhone and iPad

  • Tesla

  • Cogito

  • Netflix

  • Cortana

  • Online Customer Support

  • Face Recognition

  • Pandora

Turing Test in Artificial Intelligence

Turing Test is a test to developed to determine the intelligence of a machine. In Turing Test, there are three terminals. Two terminals are operated by the humans and the third one is operated by the computer. One human is the questioner and the other two terminals are the respondents. The questioner questions both of them. After some specified duration of time, the questioner tries to determine which terminal is operated by the computer and which by the human. If the questioner makes a correct judgement in half of the case or less, then the computer is considered as intelligent.

This was just the basic introduction of artificial intelligence. It is a vast field and this is just a pinch of it. It is a good area of exploration and a good choice for M.Tech thesis topic along with projects and seminars. Just go for it and make a good thesis report.

Topics in Artificial Intelligence for research and thesis

Following are the current hot topics in Artificial Intelligence for thesis, research and project:

  • Deep Learning
  • Robotics
  • Natural Language Processing
  • Reinforcement Learning
  • Artificial Neural Network
  • Expert Systems
  • Fuzzy Systems
  • Computer Vision

Deep Learning

Deep Learning is a sub field of Machine Learning that uses machine learning algorithms for data representation. Deep Learning is employed in deep neural networks, deep belief network, and recurrent neural networks and finds its application in computer vision, speech recognition, natural language processing and bioinformatics. It is a hot topic for project and thesis in artificial intelligence.


Robotics is a branch of field which is mixture of mechanical, electrical, and computer science engineering and uses machine learning algorithms for designing and working of robots. Robots are replacing the man-power in industries for construction and manufacturing. The application of robotics includes military robots, agriculture robots, domestic robots etc. It is an interesting topic in artificial intelligence.

Natural Language Processing

It is a branch of artificial intelligence that deals with the way computer and human language interact with each other. The main functions in this process include speech recognition, natural language understanding and natural language generation. Statistical language processing is also a part of this. It is the latest technology in artificial intelligence.

Reinforcement Learning

Reinforcement Learning is a part of Artificial Intelligence that determines how an agent should act in an environment in order to maximize its performance. There are various algorithms designed for this purpose. It is different from supervised and unsupervised learning. It is also a very good topic for research.

Artificial Neural Network

Artificial Neural Network imitates the working of a human brain. The nodes represent the biological neuron. The nodes are linked with each other with a value assigned to each node known as node value. The learning strategies in artificial neural networks are supervised learning, unsupervised learning and reinforcement learning.

Expert System

Expert System is a good area for research in artificial intelligence. Expert Systems solve complex computational problems. The main components of an expert system are Knowledge Base, Inference Engine, and User Interface. The knowledge base can be Factual and Heuristic. Forward chaining is also used in expert systems.

Fuzzy Systems

Fuzzy Systems produce a definite output for an indefinite input through fuzzy logic. Fuzzy Logic is a method of reasoning resembling human reasoning. Fuzzy Logic makes decisions in the form of Yes or No. The fuzzy system architecture includes fuzzification module, defuzzification module, knowledge base, and inference engine.

Computer Vision

Computer Vision is a part of artificial intelligence that deals with making computers understand the digital images and videos. It is an important research and thesis area in artificial intelligence. The main tasks performed in computer vision are visualizing, acquiring, and analyzing. The main applications of computer vision are object recognition, motion sensing, image restoration, and pose estimation.

Tags: AI, artificial intelligence, Natural Language Processing, Neural Networks

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