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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.
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.
Audience
Educational researchers and policymakers, as well as computer scientists working in AI, machine learning, data science, deep learning, computer vision, and statistics.
Contents
Preface xix
Part 1: Introduction to AI Tools for Online Proctoring 1
1 AI Literacy and Online Proctoring: Educational Perspectives and Strategies 3
Prihana Vasishta, Gitanjaly Chhabra and Noosha Mehdian
1.1 Introduction 4
1.2 AI in Education — Theoretical Framework 6
1.3 AI-Assisted Educational Practices 8
1.3.1 Hyper Sentient Syllabus 8
1.3.2 Role of AI in Redesigning Assessment Strategies 9
1.3.3 Framework for Adopting and Implementing AI-Assisted Online Proctoring Systems 11
1.3.4 Ethical Implications of AI in Online Proctoring 19
1.4 Strengthening Teacher Preparation for AI Literacy in Higher Education Curricula 19
1.4.1 Updating Educator's Knowledge of AI Concepts 19
1.4.2 Utilizing AI-Enhanced Technologies for Personalized Learning 20
1.4.3 Integrating AI Literacy Education with the TPACK Framework 20
1.5 Conclusion and Implications 21
References 22
2 Next-Generation Online Education Integrating AI and IoT for Superior Management and Evaluation 27
Aniket Kumar, Rajesh Kumar, Akshay Kumar, Prashant D. Yelpale and Aman Thakur
2.1 Introduction 28
2.2 AI and IoT in Online Education Systems 31
2.2.1 Overview 31
2.2.2 Smart Online Education Model 33
2.2.3 Smart Online Classroom 34
2.2.4 Smart Online Labs 36
2.2.5 Smart Online Tutoring 36
2.2.6 Smart Simulation 37
2.2.7 Smart Online Evaluation 38
2.2.8 Smart Online Security and Content Adaptation 38
2.2.9 Application and Infrastructure Levels 41
2.3 Functional Structure of IoT System 41
2.3.1 Online Exam Management 42
2.3.2 Automated Correction of Exam Papers 43
2.3.3 Student's Performance Calculation 44
2.4 Emerging Technologies in the Online Education System 45
2.4.1 AI Technologies 45
2.4.2 AR, VR 48
2.4.3 Big Data Technology 49
2.4.4 Robotics and IoT Labs 49
2.4.5 Cloud Computing Technology 50
2.4.6 Machine Learning Technology 50
2.4.7 Deep Learning Technology 51
2.4.8 IoT Technology 51
2.4.9 5G Technology 52
2.4.10 Learning Management System (LMS) 52
2.5 Challenges of AI and IoT in Online Education System 54
2.6 The Future Vision of AI and IoT in Online Education Systems 56
2.6.1 Emerging Trends and Future Applications 57
2.7 Conclusion 58
References 59
Part 2: Ethics of Using AI Tools in Education 63
3 Ethical Integrity in Educational Contexts 65
C. Santhiya, Ravi Prasath S., Suriya Navaneetha Krishnan K. and Kannappan R.
3.1 Introduction 66
3.2 Types and Methods of Fake Credentials 67
3.2.1 Counterfeit Diploma and Degrees 67
3.2.2 Fake Transcripts 68
3.2.3 Misrepresentation of Professional Licenses and Certifications 68
3.2.4 Online Credential Verification Scams 69
3.2.5 Impersonation of Genuine Graduates 70
3.2.6 Use of Photoshop and Graphic Design Software 71
3.3 Consequences of Fake Credentials 72
3.3.1 Legal Consequences 73
3.3.2 Educational and Professional Consequences 75
3.3.3 Financial Consequences 76
3.3.4 Loss of Trust 77
3.3.5 Long-Term Implications 80
3.3.6 Ethical and Psychological Consequences 81
3.3.7 Public Shame 83
3.4 Challenges in Detecting and Verifying Fake Credentials 84
3.5 Role of Technology in Facilitating and Combating Fake Credentials 86
3.6 Impact on Organizational Reputation and Public Trust 90
3.7 Multi-Layered Approach to Tackling the Problem 91
3.8 Innovative Solutions and Technologies 94
3.9 Promoting Awareness and Education 96
3.10 Future Trends and Strategies 99
3.11 Conclusion 99
References 100
4 Psychological and Ethical Aspects of Using Intelligent Systems in Online Proctoring 103
Mukesh Chaware and Sreejith Alathur
4.1 Introduction 104
4.1.1 Importance of Proctoring in Online Examination 107
4.1.2 Briefing on Intelligent Systems (AI, BD, IoT) 108
4.1.3 Relevance of Intelligent Systems to Online Proctoring 111
4.2 The Advent of AI in Online Proctoring 112
4.2.1 The Need for AI in Online Proctoring 112
4.2.2 Evolution and Current State of AI Applications in Online Proctoring 114
4.3 The Prevailing Situation 116
4.4 Psychological Aspects 119
4.4.1 User Perceptions of AI-Driven Proctoring 119
4.4.2 Impact on Test Taker Stress and Performance 121
4.4.3 Privacy Concerns and their Psychological Implications 122
4.5 Ethical Aspects 124
4.5.1 Ethical Implications of Using AI for Surveillance 125
4.5.2 Potential for Bias and Discrimination in AI Proctoring 126
4.6 Discussion and Recommendations 127
4.6.1 Strategies for Ethically Implementing AI in Online Proctoring 127
4.6.2 Recommendations for Addressing Psychological Concerns 128
4.7 Conclusion 128
Acknowledgments 131
References 131
Part 3: State-of-the-Art AI Tools and Techniques for Online Proctoring 137
5 A Comprehensive Review of Deep Learning Models on Detecting Student Emotions in Online Education 139
Thangavel Murugan, A.M. Abirami and P. Karthikeyan
5.1 Introduction 140
5.1.1 Research Overview 140
5.1.2 Importance of Detecting Student Emotions in Online Education 141
5.1.3 Purpose of the Literature Review 141
5.2 Understanding Student Emotions 142
5.2.1 Definition of Emotions 142
5.2.2 The Role of Emotions in Learning 142
5.2.3 Significance of Detecting Student Emotions in Online Education 143
5.3 Overview of Deep Learning 143
5.3.1 Definition of Deep Learning 143
5.3.2 Benefits of Deep Learning in Educational Research 143
5.3.3 Applications of Deep Learning in Detecting Emotions 144
5.4 Literature Review 144
5.4.1 Studies on Detecting Student Emotions in Online Education 145
5.4.1.1 Methods Used for Emotion Detection 146
5.4.1.2 Effectiveness of Different Approaches 147
5.4.2 Applications of Deep Learning in Emotion Detection 148
5.4.2.1 Algorithms Used in Deep Learning for Emotion Recognition 149
5.4.2.2 Success Stories and Challenges Faced in Using Deep Learning 150
5.4.2.3 Proposed Model for Student Behavior Analysis in Classroom 151
5.4.2.4 Literature Summary and Analysis 153
5.5 Challenges in Detecting Student Emotions 153
5.5.1 Technical Challenges 156
5.5.1.1 Data Collection and Processing 156
5.5.1.2 Model Accuracy and Reliability 156
5.5.2 Ethical Considerations 157
5.5.2.1 Privacy Concerns 157
5.5.2.2 Bias in Emotion Detection Algorithms 157
5.6 Future Directions and Recommendations 158
5.7 Conclusion 159
References 160
6 Deep Learning Models for Monitoring Student's Emotion During the Class: A Comprehensive Survey 165
Vamshi Krishna B., N. Padmavathy and Ajeet Kumar
6.1 Introduction 166
6.2 Literature Survey 168
6.2.1 Deep Learning Approach 169
6.2.2 Transfer Learning 171
6.3 Research Background 178
6.3.1 Computer Vision 178
6.3.2 Internet of Things (IoT) 181
6.3.3 Deep Learning Architectures 183
6.3.3.1 ConvNet 184
6.3.3.2 Recurrent Neural Network 186
6.3.4 Pre-Trained Models 189
6.4 Prediction Models for Tracking and Monitoring Students 193
6.4.1 Emotion Recognition Models 194
6.4.2 Learning Engagement Models 196
6.5 Conclusion 197
References 198
7 Comparative Analysis of Head Pose Estimation and Eye Gaze Tracking with Machine Learning Classifiers for Proctored Online Examination 203
Rajarajeswari P., Shivagangatharani B. and Karthikeyan Jothikumar
7.1 Introduction 204
7.1.1 Head Pose Estimation 204
7.1.2 Eye Gaze Tracking 204
7.1.3 Relevance of Head Pose Estimation and Eye Gaze Tracking in Online Proctored Exams 206
7.2 Benchmark Datasets for Head Pose and Eye Gaze Tracking 207
7.3 Apparatus for Estimating Head Pose and Tracking Eye Gaze 214
7.4 Models for Head Pose Estimation and Eye Gaze Tracking 217
7.4.1 Geometrical Method Based on Interest Points 217
7.4.2 Gradient Boosting Regression 218
7.4.3 Genetic Algorithm 219
7.4.4 Linear Discriminant Analysis (LDA) and Discrete Wavelet Transform (DWT) 220
7.4.5 Aff Net 221
7.4.6 FSA-Net 223
7.4.7 Multi-Modal Convolutional Neural Network 223
7.5 Comparison of Models for Head Pose Estimation and Eye Gaze Tracking 224
7.6 Conclusion 226
References 227
8 Uni- and Multi-Modal Aspects in the Online Proctoring System: Survey 231
Diana Moses and Dainty M.
8.1 Introduction 232
8.1.1 Online Proctoring Techniques 235
8.1.2 Concerns in Online Proctoring Systems 237
8.2 AI-Based Online Proctoring System 240
8.2.1 Online Proctoring Process 240
8.2.1.1 Proctoring Prior to Examination 241
8.2.1.2 Proctoring During Examination 243
8.2.1.2(a) Examinee Behavior Screening 244
8.2.1.2(b) Examinee System Screening 247
8.2.1.2(c) Examinee Environment Screening 248
8.3 Existing AI-Based Online Proctoring Frameworks 249
8.4 Challenges in AI-Based Online Proctoring Frameworks 253
8.5 Future Scope of AI-Based Proctoring Frameworks 256
8.6 Conclusion 259
References 260
9 Advancing Academic Integrity: AI and IoT in Enhancing Monitoring for Online Examination Systems 265
J. Shanthalakshmi Revathy and J. Mangaiyarkkarasi
9.1 Introduction 266
9.2 Predictive Analysis of Student Performance 267
9.2.1 Data Collection 269
9.2.2 Data Preprocessing 269
9.2.3 Feature Engineering 270
9.2.4 Model Selection and Training 270
9.2.5 Model Evaluation 270
9.2.6 Model Deployment 271
9.3 Authentication of Students 271
9.4 Supervision of Examination 272
9.4.1 Plagiarism Detection 273
9.4.2 Fraud Detection and Malpractice Prevention 275
9.4.3 Multiple Account Detection 275
9.4.4 E-Cheating Intelligence Agents 276
9.4.5 Detection of Liveliness Spoofs 277
9.4.6 Anomaly Detection 277
9.5 Challenges in Monitoring 279
9.5.1 Privacy Concerns 281
9.5.2 Security Challenges 281
9.5.3 Fairness Consideration 282
9.6 Conclusion 283
References 284
10 Optimizing Academic Excellence: Leveraging Advanced AI Tools for Assessment and Evaluation in Modern Online Examination Systems 287
Manikandakumar M., Karthikeyan P., Senthamarai Kannan K., Arul V. and Vigneshwaran T.
10.1 Introduction 288
10.2 Role of AI in Online Examination Systems 289
10.2.1 Benefits of AI in Assessments 290
10.2.2 Personalization of Assessments 290
10.2.3 Efficiency and Time-Saving 291
10.2.4 Fairness and Objectivity 291
10.2.5 Scalability and Accessibility 291
10.2.6 Enhanced Security and Integrity 291
10.2.7 Data-Driven Insights 292
10.2.8 Continuous Learning and Improvement 292
10.3 Advanced AI Tools for Assessment 293
10.3.1 Knewton 293
10.3.2 DreamBox 295
10.3.3 Edpuzzle 297
10.3.4 Squirrel AI 300
10.3.5 ProctorU 301
10.3.6 Smart Sparrow 303
10.3.7 MoodleNet 304
10.3.8 Canvas by Instructure 306
10.4 Implementing AI Tools in Online Examination Systems 308
10.4.1 Needs for AI in Online Examination Systems 308
10.4.2 Steps for Implementing AI Tools 309
10.4.3 Advantages of AI in Online Examinations 309
10.4.4 Challenges of Implementing AI 310
10.5 Future Trends 310
10.6 Conclusion 311
References 311
Part 4: Case Studies: AI and IoT in Education, Online Proctoring 315
11 Evaluation of Web Design Deficiency and Anxiety Constructs, with Computer-Based Test: Use Case in India 317
Juby Thomas, Ashique Ali K.A., Vishnu Achutha Menon, Sateesh Kumar T.K. and Lijo P. Thomas
11.1 Introduction 318
11.2 Review of Literature 319
11.3 Methodology 325
11.4 Results 328
11.4.1 Structural Model 331
11.5 Discussions 335
11.6 Conclusion 338
Acknowledgment 339
References 339
12 AI for Learners' Emotions — A Perspective Approach of Analysis During Online Assessments 343
S.J. Sheeba Sharon, R. Mary Sophia Chitra and C. Santhiya
12.1 Introduction 344
12.2 Literature Survey 346
12.3 Role of Emotions in Learning 349
12.4 Challenges in Online Assessments 350
12.5 The Rise of AI in Education 351
12.6 AI Tools for Monitoring Learner Emotions 351
12.6.1 Facial Expression Analysis Tools 351
12.6.2 Voice Analysis Tools 352
12.6.3 Sentiment Analysis and NLP Tools 352
12.6.4 Physiological Monitoring Tools 352
12.7 Methodology 353
12.7.1 Selection of Appropriate Tools 354
12.7.2 Data Collection and Consent 354
12.7.3 Integration with Assessment Platforms 354
12.7.4 Training for Educators and Administrators 355
12.7.5 Pilot Testing and Evaluation 355
12.7.6 Full Implementation and Ongoing Monitoring 355
12.7.7 Addressing Ethical and Privacy Concerns 355
12.7.8 Feedback and Continuous Improvement 356
12.8 Advantages of Using AI Tools 356
12.9 Possible Implementational Risks 357
12.10 Demerits and Future Scope 358
12.11 Conclusion 359
References 359
13 Implementing Personalized Adaptive Online Assessments through Deep Learning 365
Fawad Naseer, Noreen Sattar, Akhtar Rasool, Kamel Jebreenand Usman Khalid
13.1 Introduction 366
13.1.1 The Need for Adaptive Assessment Systems 366
13.1.2 The Role of DL in Education 367
13.1.3 Research Context and Case Studies 367
13.2 Literature Review 368
13.3 Methodology 370
13.3.1 Description of the DL Algorithms and Models 370
13.3.1.1 Convolutional Neural Networks (CNNs) 371
13.3.1.2 Recurrent Neural Networks (RNNs) 371
13.3.1.3 Long Short-Term Memory (LSTM) Networks 372
13.3.2 Data Collection and Pre-Processing Methods 373
13.3.2.1 Data Collection 373
13.3.2.2 Data Pre-Processing 374
13.3.3 Steps Involved in Developing and Implementing the Adaptive Assessment System 375
13.3.3.1 Model Design and Training 375
13.3.3.2 Adaptive Assessment Generation 376
13.3.3.3 Real-Time Feedback System 376
13.3.3.4 Implementation and Testing 377
13.4 Case Studies 378
13.4.1 Case Study 1: Beaconhouse International College (bic) 378
13.4.1.1 Background and Context 378
13.4.1.2 Implementation Process 378
13.4.1.3 Key Findings 380
13.4.1.4 Challenges and Solutions 381
13.4.2 Case Study 2: Government College University Faisalabad (GCUF) 381
13.4.2.1 Background and Context 381
13.4.2.2 Implementation Process 382
13.4.2.3 Key Findings 382
13.4.2.4 Challenges and Solutions 383
13.5 Results and Discussion 384
13.5.1 Improvement in Learning Outcomes 384
13.5.2 Increase in Engagement Rates 386
13.5.3 Reduction in Exam-Related Anxiety 388
13.5.4 Enhanced Overall Performance 390
13.5.5 Comparative Analysis of the Case Studies 392
13.5.5.1 Similarities 392
13.5.5.2 Differences 392
13.5.6 Future Research Directions 394
13.5.7 Limitations of the Study 395
13.6 Conclusion 395
References 396
14 Generative Artificial Intelligence for Online Education Systems 399
Munmi Dutta and Vinay Kumar Goyal
14.1 Introduction 400
14.2 The Types of GAI Models 401
14.3 Working of GAI 401
14.3.1 Generative Modeling 402
14.3.2 GANs 403
14.3.3 Transformer-Based Models 404
14.4 Use Cases of GAI 406
14.5 The Limitations of GAI 407
14.6 Adaptive Learning Platforms 408
14.7 GAI and Adaptive Learning Intersection 409
14.7.1 Potential Benefits of Integrating GAI and Adaptive Learning 409
14.7.2 Some Examples of Successful Integration 409
14.7.3 Future Trends of GAI and Adaptive Learning 410
14.7.4 Prospective Developments in GAI for the Education Sector 411
14.8 Implications for Educators and Learners 412
14.9 GAI Effect on Workforce 412
14.10 GAI Has Already Transformed Education 413
14.11 Effect on the Participation and Performance of Learners 414
14.11.1 Develop Their Expressiveness and Creativity 414
14.11.2 Develop Their Information Literacy and Research Abilities 414
14.11.3 Improve Their Capacity for Self-Control and Metacognition 415
14.12 The Education Sector's Challenges with GAI 415
14.12.1 Challenge Cause Due to Plagiarism 415
14.12.2 Equity 415
14.12.3 Privacy 416
14.12.4 Efficacy 416
14.12.5 Detection 417
14.12.6 Appropriate Use 417
14.12.7 Authorship 417
14.13 Policymakers and Educators Need to Reconsider the Current Educational Paradigm 418
14.14 Access and Equity Comes First 418
14.15 United Nations Educational, Scientific and Cultural Organization's (UNESCO's) Policy for Reshaping Education by Using GAI 419
14.16 Conclusion 420
References 420
15 Level of Academic Misconduct During Online Unproctored Examination with Perception of Engineering Students in India 423
S. Sasikala, G. Vidyasree, C. Selvan and R. Ragunath
15.1 Introduction 424
15.2 Literature Review 425
15.3 Research Methodology 428
15.3.1 Sampling 430
15.3.2 Data Analysis and Findings 432
15.3.3 Relative Importance 436
15.4 Conclusion 439
References 439
16 Student Activity Monitoring Using Hybrid Deep Learning Technique During Online Examinations 443
Devi Naveen, Akshitha Katkeri, Manikantha K., A.K. Sreeja and Satish Kumar V.
16.1 Introduction 444
16.1.1 Motivations 445
16.1.2 Objective and Design 446
16.1.3 Contributions 447
16.2 Related Works 447
16.2.1 Image Information Systems (IIS) 447
16.2.2 Multi-Modal System (MMS) 449
16.2.3 Behavior-Based Analysis 450
16.3 Methodology — The Theoretical Foundation of the Proposed Model 451
16.3.1 Dataset Collection 451
16.4 Experimental Results and Discussion 456
16.5 Conclusion and Future Work 460
References 461
17 Multicue Facial Emotion Expression Using Lightweight Deep Learning Models 465
S. Hemaswathi, P. Rajkumar, N. Mohan Prabhu and R. Dhivya
17.1 Introduction 466
17.1.1 Types of Facial Expression and Its Features 467
17.2 Related Works 469
17.3 Materials and Method 472
17.3.1 Face and Facial Landmark Detection 474
17.3.2 Convolution Neural Network (ConvNEt) Architecture 475
17.3.3 VGG-16 Architecture 476
17.3.4 InceptionV3 Architecture 477
17.3.5 ResNet 50 477
17.4 Experimental Result Analysis 478
17.5 Conclusion 482
References 482
Part 5: Challenges and Future Scope of AI in Online Proctoring 485
18 Machine-Learning-Based Online Assessment of Students' Academic Performance in Moodle Learning Management System 487
Reshma V.K., Nisha A.K., Radhika K. Manjusha, Divya P. and Sundaraselvan S.
18.1 Introduction 488
18.2 Literature Review 491
18.3 Research Methodology 493
18.3.1 Dataset Acquisition 494
18.3.2 Dataset Pre-Processing 495
18.3.3 Data Analysis 495
18.3.4 Linear Regression 496
18.3.5 Correlation 496
18.3.6 Multiple Regression 496
18.3.7 Lasso Regression 496
18.4 Results and Discussion 496
18.4.1 Correlation 497
18.4.2 Scatter Plot 498
18.4.3 Linear Regression 500
18.4.4 Multiple Linear Regression 504
18.4.5 Lasso Regression 508
18.5 Conclusion 509
18.6 Future Research 510
References 511
19 Issues and Challenges of Using Artificial Intelligence Proctoring Tools 515
V. Senthil
19.1 Introduction 515
19.2 Literature Review 517
19.2.1 Features of AI-Based Online Proctoring Tools 520
19.3 Issues and Challenges of Using AI Proctoring Tools 522
19.4 Case Study 524
19.5 Conclusion 527
References 529
Index 533
