Search

Browse Subject Areas

For Authors

Submit a Proposal

Artificial Intelligence and IoT in Online Education Systems

Edited by Ramanujam E. and Chandan Chakraborty
Copyright: 2025   |   Expected Pub Date:2025/08/30
ISBN: 9781394302635  |  Hardcover  |  
558 pages

One Line Description
Design the future of digital education with this essential book that provides a comprehensive guide to leveraging AI and IoT to create dynamic, inclusive virtual learning environments and effectively implement advanced online proctoring solutions.

Audience
Educational researchers and policymakers, as well as computer scientists working in AI, machine learning, data science, deep learning, computer vision, and statistics.

Description
The rapid development of online learning environments and virtual classrooms, coupled with the need for scalable, personalized education systems, has positioned AI as a key enabler of modern education. The advent of these technologies promises to reshape how we deliver, monitor, assess, and evaluate online learning. This book explores these critical intersections of technology and education, emphasizing the potential of AI and IoT not only to optimize outcomes but also to create more dynamic, responsive, and inclusive virtual learning environments. Focusing on problems that can be solved through computer vision, video and audio streaming, class imbalance data, audio-to-text processes, multi-modal and bi-modal aspects, hand-written strokes, text similarity, biomedical ethics, and advancements in machine and deep learning algorithms, this book comprehensively explores the effectiveness of these technologies in online proctoring. This essential guide will equip educators, technologists, administrators, and policymakers with the knowledge and perspective necessary to leverage these technologies effectively.
Readers will find the book:
• Explores various AI tools and techniques adopted for online proctoring examination systems;
• Covers critical analytical aspects of AI-assisted systems;
• Describes a variety of experiments leading to uni- and multi-modal systems and IoT-based architecture using computer vision, machine learning, and deep learning algorithms;
• Discusses the quality assurance and psychological aspects to preserve ethics during examinations.

Back to Top
Author / Editor Details
Ramanujam E., PhD is an Assistant Professor in the Department of Computer Science and Engineering at the National Institute of Technology Silchar, Assam, India. He has contributed significantly to human activity recognition, especially online education and examinations, spam detection, and feature engineering across various domains. His areas of expertise include artificial intelligence, machine learning, deep learning, and ambient intelligence.

Chandan Chakraborty, PhD is a Professor in the Department of Computer Science and Engineering at the National Institute of Technical Teachers’ Training and Research, Kolkata, West Bengal, India. He has more than 100 peer-reviewed publications in top journals and holds several patents. He specializes in artificial intelligence, machine learning, deep learning, and biomedical engineering.

Back to Top

Table of Contents
Preface
Part 1: Introduction to AI Tools for Online Proctoring
1. AI Literacy and Online Proctoring: Educational Perspectives and Strategies
Prihana Vasishta, Gitanjaly Chhabra and Noosha Mehdian
1.1 Introduction
1.2 AI in Education — Theoretical Framework
1.3 AI-Assisted Educational Practices
1.3.1 Hyper Sentient Syllabus
1.3.2 Role of AI in Redesigning Assessment Strategies
1.3.3 Framework for Adopting and Implementing AI-Assisted Online Proctoring Systems
1.3.4 Ethical Implications of AI in Online Proctoring
1.4 Strengthening Teacher Preparation for AI Literacy in Higher Education Curricula
1.4.1 Updating Educator’s Knowledge of AI Concepts
1.4.2 Utilizing AI-Enhanced Technologies for Personalized Learning
1.4.3 Integrating AI Literacy Education with the TPACK Framework
1.5 Conclusion and Implications
References
2. Next-Generation Online Education Integrating AI and IoT for Superior Management and Evaluation
Aniket Kumar, Rajesh Kumar, Akshay Kumar, Prashant D. Yelpale and Aman Thakur
2.1 Introduction
2.2 AI and IoT in Online Education Systems
2.2.1 Overview
2.2.2 Smart Online Education Model
2.2.3 Smart Online Classroom
2.2.4 Smart Online Labs
2.2.5 Smart Online Tutoring
2.2.6 Smart Simulation
2.2.7 Smart Online Evaluation
2.2.8 Smart Online Security and Content Adaptation
2.2.9 Application and Infrastructure Levels
2.3 Functional Structure of IoT System
2.3.1 Online Exam Management
2.3.2 Automated Correction of Exam Papers
2.3.3 Student’s Performance Calculation
2.4 Emerging Technologies in the Online Education System
2.4.1 AI Technologies
2.4.2 AR, VR
2.4.3 Big Data Technology
2.4.4 Robotics and IoT Labs
2.4.5 Cloud Computing Technology
2.4.6 Machine Learning Technology
2.4.7 Deep Learning Technology
2.4.8 IoT Technology
2.4.9 5G Technology
2.4.10 Learning Management System (LMS)
2.5 Challenges of AI and IoT in Online Education System
2.6 The Future Vision of AI and IoT in Online Education Systems
2.6.1 Emerging Trends and Future Applications
2.7 Conclusion
References
Part 2: Ethics of Using AI Tools in Education
3. Ethical Integrity in Educational Contexts
C. Santhiya, Ravi Prasath S., Suriya Navaneetha Krishnan K. and Kannappan R.
3.1 Introduction
3.2 Types and Methods of Fake Credentials
3.2.1 Counterfeit Diploma and Degrees
3.2.2 Fake Transcripts
3.2.3 Misrepresentation of Professional Licenses and Certifications
3.2.4 Online Credential Verification Scams
3.2.5 Impersonation of Genuine Graduates
3.2.6 Use of Photoshop and Graphic Design Software
3.3 Consequences of Fake Credentials
3.3.1 Legal Consequences
3.3.2 Educational and Professional Consequences
3.3.3 Financial Consequences
3.3.4 Loss of Trust
3.3.5 Long-Term Implications
3.3.6 Ethical and Psychological Consequences
3.3.7 Public Shame
3.4 Challenges in Detecting and Verifying Fake Credentials
3.5 Role of Technology in Facilitating and Combating Fake Credentials
3.6 Impact on Organizational Reputation and Public Trust
3.7 Multi-Layered Approach to Tackling the Problem
3.8 Innovative Solutions and Technologies
3.9 Promoting Awareness and Education
3.10 Future Trends and Strategies
3.11 Conclusion
References
4. Psychological and Ethical Aspects of Using Intelligent Systems in Online Proctoring
Mukesh Chaware and Sreejith Alathur
4.1 Introduction
4.1.1 Importance of Proctoring in Online Examination
4.1.2 Briefing on Intelligent Systems (AI, BD, IoT)
4.1.3 Relevance of Intelligent Systems to Online Proctoring
4.2 The Advent of AI in Online Proctoring
4.2.1 The Need for AI in Online Proctoring
4.2.2 Evolution and Current State of AI Applications in Online Proctoring
4.3 The Prevailing Situation
4.4 Psychological Aspects
4.4.1 User Perceptions of AI-Driven Proctoring
4.4.2 Impact on Test Taker Stress and Performance
4.4.3 Privacy Concerns and their Psychological Implications
4.5 Ethical Aspects
4.5.1 Ethical Implications of Using AI for Surveillance
4.5.2 Potential for Bias and Discrimination in AI Proctoring
4.6 Discussion and Recommendations
4.6.1 Strategies for Ethically Implementing AI in Online Proctoring
4.6.2 Recommendations for Addressing Psychological Concerns
4.7 Conclusion
Acknowledgments
References
Part 3: State-of-the-Art AI Tools and Techniques for Online Proctoring
5. A Comprehensive Review of Deep Learning Models on Detecting Student Emotions in Online Education
Thangavel Murugan, A.M. Abirami and P. Karthikeyan
5.1 Introduction
5.1.1 Research Overview
5.1.2 Importance of Detecting Student Emotions in Online Education
5.1.3 Purpose of the Literature Review
5.2 Understanding Student Emotions
5.2.1 Definition of Emotions
5.2.2 The Role of Emotions in Learning
5.2.3 Significance of Detecting Student Emotions in Online Education
5.3 Overview of Deep Learning
5.3.1 Definition of Deep Learning
5.3.2 Benefits of Deep Learning in Educational Research
5.3.3 Applications of Deep Learning in Detecting Emotions
5.4 Literature Review
5.4.1 Studies on Detecting Student Emotions in Online Education
5.4.1.1 Methods Used for Emotion Detection
5.4.1.2 Effectiveness of Different Approaches
5.4.2 Applications of Deep Learning in Emotion Detection
5.4.2.1 Algorithms Used in Deep Learning for Emotion Recognition
5.4.2.2 Success Stories and Challenges Faced in Using Deep Learning
5.4.2.3 Proposed Model for Student Behavior Analysis in Classroom
5.4.2.4 Literature Summary and Analysis
5.5 Challenges in Detecting Student Emotions
5.5.1 Technical Challenges
5.5.1.1 Data Collection and Processing
5.5.1.2 Model Accuracy and Reliability
5.5.2 Ethical Considerations
5.5.2.1 Privacy Concerns
5.5.2.2 Bias in Emotion Detection Algorithms
5.6 Future Directions and Recommendations
5.7 Conclusion
References
6. Deep Learning Models for Monitoring Student’s Emotion During the Class: A Comprehensive Survey
Vamshi Krishna B., N. Padmavathy and Ajeet Kumar
6.1 Introduction
6.2 Literature Survey
6.2.1 Deep Learning Approach
6.2.2 Transfer Learning
6.3 Research Background
6.3.1 Computer Vision
6.3.2 Internet of Things (IoT)
6.3.3 Deep Learning Architectures
6.3.3.1 ConvNet
6.3.3.2 Recurrent Neural Network
6.3.4 Pre-Trained Models
6.4 Prediction Models for Tracking and Monitoring Students
6.4.1 Emotion Recognition Models
6.4.2 Learning Engagement Models
6.5 Conclusion
References
7. Comparative Analysis of Head Pose Estimation and Eye Gaze Tracking with Machine Learning Classifiers for Proctored Online Examination
Rajarajeswari P., Shivagangatharani B. and Karthikeyan Jothikumar
7.1 Introduction
7.1.1 Head Pose Estimation
7.1.2 Eye Gaze Tracking
7.1.3 Relevance of Head Pose Estimation and Eye Gaze Tracking in Online Proctored Exams
7.2 Benchmark Datasets for Head Pose and Eye Gaze Tracking
7.3 Apparatus for Estimating Head Pose and Tracking Eye Gaze
7.4 Models for Head Pose Estimation and Eye Gaze Tracking
7.4.1 Geometrical Method Based on Interest Points
7.4.2 Gradient Boosting Regression
7.4.3 Genetic Algorithm
7.4.4 Linear Discriminant Analysis (LDA) and Discrete Wavelet Transform (DWT)
7.4.5 Aff Net
7.4.6 FSA-Net
7.4.7 Multi-Modal Convolutional Neural Network
7.5 Comparison of Models for Head Pose Estimation and Eye Gaze Tracking
7.6 Conclusion
References
8. Uni- and Multi-Modal Aspects in the Online Proctoring System: Survey
Diana Moses and Dainty M.
8.1 Introduction
8.1.1 Online Proctoring Techniques
8.1.2 Concerns in Online Proctoring Systems
8.2 AI-Based Online Proctoring System
8.2.1 Online Proctoring Process
8.2.1.1 Proctoring Prior to Examination
8.2.1.2 Proctoring During Examination
8.2.1.2(a) Examinee Behavior Screening
8.2.1.2(b) Examinee System Screening
8.2.1.2(c) Examinee Environment Screening
8.3 Existing AI-Based Online Proctoring Frameworks
8.4 Challenges in AI-Based Online Proctoring Frameworks
8.5 Future Scope of AI-Based Proctoring Frameworks
8.6 Conclusion
References
9. Advancing Academic Integrity: AI and IoT in Enhancing Monitoring for Online Examination Systems
J. Shanthalakshmi Revathy and J. Mangaiyarkkarasi
9.1 Introduction
9.2 Predictive Analysis of Student Performance
9.2.1 Data Collection
9.2.2 Data Preprocessing
9.2.3 Feature Engineering
9.2.4 Model Selection and Training
9.2.5 Model Evaluation
9.2.6 Model Deployment
9.3 Authentication of Students
9.4 Supervision of Examination
9.4.1 Plagiarism Detection
9.4.2 Fraud Detection and Malpractice Prevention
9.4.3 Multiple Account Detection
9.4.4 E-Cheating Intelligence Agents
9.4.5 Detection of Liveliness Spoofs
9.4.6 Anomaly Detection
9.5 Challenges in Monitoring
9.5.1 Privacy Concerns
9.5.2 Security Challenges
9.5.3 Fairness Consideration
9.6 Conclusion
References
10. Optimizing Academic Excellence: Leveraging Advanced AI Tools for Assessment and Evaluation in Modern Online Examination Systems
Manikandakumar M., Karthikeyan P., Senthamarai Kannan K., Arul V. and Vigneshwaran T.
10.1 Introduction
10.2 Role of AI in Online Examination Systems
10.2.1 Benefits of AI in Assessments
10.2.2 Personalization of Assessments
10.2.3 Efficiency and Time-Saving
10.2.4 Fairness and Objectivity
10.2.5 Scalability and Accessibility
10.2.6 Enhanced Security and Integrity
10.2.7 Data-Driven Insights
10.2.8 Continuous Learning and Improvement
10.3 Advanced AI Tools for Assessment
10.3.1 Knewton
10.3.2 DreamBox
10.3.3 Edpuzzle
10.3.4 Squirrel AI
10.3.5 ProctorU
10.3.6 Smart Sparrow
10.3.7 MoodleNet
10.3.8 Canvas by Instructure
10.4 Implementing AI Tools in Online Examination Systems
10.4.1 Needs for AI in Online Examination Systems
10.4.2 Steps for Implementing AI Tools
10.4.3 Advantages of AI in Online Examinations
10.4.4 Challenges of Implementing AI
10.5 Future Trends
10.6 Conclusion
References
Part 4: Case Studies: AI and IoT in Education, Online Proctoring
11. Evaluation of Web Design Deficiency and Anxiety Constructs, with Computer‑Based Test: Use Case in India
Juby Thomas, Ashique Ali K.A., Vishnu Achutha Menon, Sateesh Kumar T.K. and Lijo P. Thomas
11.1 Introduction
11.2 Review of Literature
11.3 Methodology
11.4 Results
11.4.1 Structural Model
11.5 Discussions
11.6 Conclusion
Acknowledgment
References
12. AI for Learners’ Emotions — A Perspective Approach of Analysis During Online Assessments
S.J. Sheeba Sharon, R. Mary Sophia Chitra and C. Santhiya
12.1 Introduction
12.2 Literature Survey
12.3 Role of Emotions in Learning
12.4 Challenges in Online Assessments
12.5 The Rise of AI in Education
12.6 AI Tools for Monitoring Learner Emotions
12.6.1 Facial Expression Analysis Tools
12.6.2 Voice Analysis Tools
12.6.3 Sentiment Analysis and NLP Tools
12.6.4 Physiological Monitoring Tools
12.7 Methodology
12.7.1 Selection of Appropriate Tools
12.7.2 Data Collection and Consent
12.7.3 Integration with Assessment Platforms
12.7.4 Training for Educators and Administrators
12.7.5 Pilot Testing and Evaluation
12.7.6 Full Implementation and Ongoing Monitoring
12.7.7 Addressing Ethical and Privacy Concerns
12.7.8 Feedback and Continuous Improvement
12.8 Advantages of Using AI Tools
12.9 Possible Implementational Risks
12.10 Demerits and Future Scope
12.11 Conclusion
References
13. Implementing Personalized Adaptive Online Assessments through Deep Learning
Fawad Naseer, Noreen Sattar, Akhtar Rasool, Kamel Jebreen and Usman Khalid
13.1 Introduction
13.1.1 The Need for Adaptive Assessment Systems
13.1.2 The Role of DL in Education
13.1.3 Research Context and Case Studies
13.2 Literature Review
13.3 Methodology
13.3.1 Description of the DL Algorithms and Models
13.3.1.1 Convolutional Neural Networks (CNNs)
13.3.1.2 Recurrent Neural Networks (RNNs)
13.3.1.3 Long Short-Term Memory (LSTM) Networks
13.3.2 Data Collection and Pre-Processing Methods
13.3.2.1 Data Collection
13.3.2.2 Data Pre-Processing
13.3.3 Steps Involved in Developing and Implementing the Adaptive Assessment System
13.3.3.1 Model Design and Training
13.3.3.2 Adaptive Assessment Generation
13.3.3.3 Real-Time Feedback System
13.3.3.4 Implementation and Testing
13.4 Case Studies
13.4.1 Case Study 1: Beaconhouse International College (BIC)
13.4.1.1 Background and Context
13.4.1.2 Implementation Process
13.4.1.3 Key Findings
13.4.1.4 Challenges and Solutions
13.4.2 Case Study 2: Government College University Faisalabad (GCUF)
13.4.2.1 Background and Context
13.4.2.2 Implementation Process
13.4.2.3 Key Findings
13.4.2.4 Challenges and Solutions
13.5 Results and Discussion
13.5.1 Improvement in Learning Outcomes
13.5.2 Increase in Engagement Rates
13.5.3 Reduction in Exam-Related Anxiety
13.5.4 Enhanced Overall Performance
13.5.5 Comparative Analysis of the Case Studies
13.5.5.1 Similarities
13.5.5.2 Differences
13.5.6 Future Research Directions
13.5.7 Limitations of the Study
13.6 Conclusion
References
14. Generative Artificial Intelligence for Online Education Systems
Munmi Dutta and Vinay Kumar Goyal
14.1 Introduction
14.2 The Types of GAI Models
14.3 Working of GAI
14.3.1 Generative Modeling
14.3.2 GANs
14.3.3 Transformer-Based Models
14.4 Use Cases of GAI
14.5 The Limitations of GAI
14.6 Adaptive Learning Platforms
14.7 GAI and Adaptive Learning Intersection
14.7.1 Potential Benefits of Integrating GAI and Adaptive Learning
14.7.2 Some Examples of Successful Integration
14.7.3 Future Trends of GAI and Adaptive Learning
14.7.4 Prospective Developments in GAI for the Education Sector
14.8 Implications for Educators and Learners
14.9 GAI Effect on Workforce
14.10 GAI Has Already Transformed Education
14.11 Effect on the Participation and Performance of Learners
14.11.1 Develop Their Expressiveness and Creativity
14.11.2 Develop Their Information Literacy and Research Abilities
14.11.3 Improve Their Capacity for Self-Control and Metacognition
14.12 The Education Sector’s Challenges with GAI
14.12.1 Challenge Cause Due to Plagiarism
14.12.2 Equity
14.12.3 Privacy
14.12.4 Efficacy
14.12.5 Detection
14.12.6 Appropriate Use
14.12.7 Authorship
14.13 Policymakers and Educators Need to Reconsider the Current Educational Paradigm
14.14 Access and Equity Comes First
14.15 United Nations Educational, Scientific and Cultural Organization’s (UNESCO’s) Policy for Reshaping Education by Using GAI
14.16 Conclusion
References
15. Level of Academic Misconduct During Online Unproctored Examination with Perception of Engineering Students in India
S. Sasikala, G. Vidyasree, C. Selvan and R. Ragunath
15.1 Introduction
15.2 Literature Review
15.3 Research Methodology
15.3.1 Sampling
15.3.2 Data Analysis and Findings
15.3.3 Relative Importance
15.4 Conclusion
References
16. Student Activity Monitoring Using Hybrid Deep Learning Technique During Online Examinations
Devi Naveen, Akshitha Katkeri, Manikantha K., A.K. Sreeja and Satish Kumar V.
16.1 Introduction
16.1.1 Motivations
16.1.2 Objective and Design
16.1.3 Contributions
16.2 Related Works
16.2.1 Image Information Systems (IIS)
16.2.2 Multi-Modal System (MMS)
16.2.3 Behavior-Based Analysis
16.3 Methodology — The Theoretical Foundation of the Proposed Model
16.3.1 Dataset Collection
16.4 Experimental Results and Discussion
16.5 Conclusion and Future Work
References
17. Multicue Facial Emotion Expression Using Lightweight Deep Learning Models
S. Hemaswathi, P. Rajkumar, N. Mohan Prabhu and R. Dhivya
17.1 Introduction
17.1.1 Types of Facial Expression and Its Features
17.2 Related Works
17.3 Materials and Method
17.3.1 Face and Facial Landmark Detection
17.3.2 Convolution Neural Network (ConvNEt) Architecture
17.3.3 VGG-16 Architecture
17.3.4 InceptionV3 Architecture
17.3.5 ResNet50
17.4 Experimental Result Analysis
17.5 Conclusion
References
Part 5: Challenges and Future Scope of AI in Online Proctoring
18. Machine-Learning-Based Online Assessment of Students’ Academic Performance in Moodle Learning Management System
Reshma V.K., Nisha A.K., Radhika K. Manjusha, Divya P. and Sundaraselvan S.
18.1 Introduction
18.2 Literature Review
18.3 Research Methodology
18.3.1 Dataset Acquisition
18.3.2 Dataset Pre-Processing
18.3.3 Data Analysis
18.3.4 Linear Regression
18.3.5 Correlation
18.3.6 Multiple Regression
18.3.7 Lasso Regression
18.4 Results and Discussion
18.4.1 Correlation
18.4.2 Scatter Plot
18.4.3 Linear Regression
18.4.4 Multiple Linear Regression
18.4.5 Lasso Regression
18.5 Conclusion
18.6 Future Research
References
19. Issues and Challenges of Using Artificial Intelligence Proctoring Tools
V. Senthil
19.1 Introduction
19.2 Literature Review
19.2.1 Features of AI-Based Online Proctoring Tools
19.3 Issues and Challenges of Using AI Proctoring Tools
19.4 Case Study
19.5 Conclusion
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page