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Transforming Digital Education

Leveraging Artificial Intelligence for Smart School Facilities
Edited by Amit Kumar Tyagi
Expected Pub Date: 2026  |  Hardcover  |  
776 pages

One Line Description
Discover how artificial intelligence is transforming traditional classrooms into responsive, intelligent ecosystems to shape the future of education.

Audience
Educational Tech researchers and policymakers, educators, and professionals researching new ways to create individualized learning plans for student success.

Description
In the rapidly evolving landscape of the 21st century, education stands at a transformative crossroads. Traditional classrooms, long reliant on conventional teaching methods and static infrastructure, are now being reimagined through the lens of innovation, technology, and artificial intelligence. This book explores this dynamic shift and envisions a future where schools are not just centers of learning, but intelligent, responsive ecosystems that adapt to the needs of every student and educator. This book recognizes that the challenges faced by modern educational institutions can no longer be addressed using outdated models. Artificial intelligence offers unprecedented opportunities to enhance how Universities and schools function, teach, and evolve. From intelligent classroom assistants and predictive analytics to energy-efficient systems and student behavior monitoring, AI is already reshaping the foundation of academic environments. In this book, we consider not only technological advancements but also the ethical implications, implementation challenges, and future influence of this emerging technology. This essential resource inspires educators, policymakers, researchers, and technologists to collaborate in building educational spaces that are inclusive, innovative, and intelligent.
Readers will find the volume:
• Discuss the potential of artificial intelligence that can help in increasing the effectiveness of instructors through various AI applications such as text translation systems, real-time message-to-speech, and automation of mundane and repeated jobs;
• Details how AI helps to eliminate manual administration work in schools, colleges, and universities and deliver personalized learning and intelligent tutoring to students;
• Demonstrates how AI-powered educational technology encompasses tools for teachers, students, and administrators such as educational games, adaptive learning platforms, chatbots and intelligent tutoring systems.
• Offers real-world uses of latest technologies in different AI-based systems to further study its influence in innovative teaching and learning initiatives.

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Author / Editor Details
Amit Kumar Tyagi, PhD is an Assistant Professor at the School of Law, Forensic Justice and Policy Studies, National Forensic Sciences University, Gandhinagar, Gujarat, India. He has published over 350 papers in refereed international journals, conferences, authored five books and edited more than 40 books. He has also filed more than 15 patents (nationally and internationally) in the areas of deep learning, Internet of Things, cyber-physical systems and computer vision.

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Table of Contents
Preface
Acknowledgments
Part 1: Fundamentals
1. Introduction to AI-Driven Smart Facilities for Modern Schools
Atchaya M., B. Nandhitha, Praveen Joe I. R. and Vijay John
1.1 Introduction
1.1.1 The Rise of Smart Facilities in Education
1.1.2 Understanding AI-Driven Smart Facilities
1.1.3 Why Schools Need AI-Driven Smart Facilities
1.1.4 Challenges in Traditional School Infrastructure
1.1.5 How AI Solves These Challenges
1.2 Historical Context of Technology in Education
1.2.1 Early Technologies in Education
1.2.2 The Blackboards and Chalkboards
1.2.3 The Advent of Film and Projection Technologies
1.2.4 The Rise of Radio and Early Broadcasting in Education
1.2.5 The Early Beginnings of Educational Television
1.2.6 The Internet and Digital Transformation: 1990s to 2000s
1.2.7 The Rise of Mobile and Cloud Technology: 2010s and Beyond
1.2.8 Current Trends and the Future of Technology in Education
1.3 Digital Transformation in Schools: Challenges and Opportunities
1.3.1 Challenges in Digital Transformation for Schools
1.4 The Role of AI in Modernizing Educational Environments
1.4.1 AI-Powered Learning: The Shift from Traditional to Smart Classrooms
1.4.2 AI in School Administration and Management
1.4.3 AI and Predictive Analytics for Student Performance
1.4.4 AI and Student Safety: Smart Security Systems
1.4.5 The Ethical and Privacy Concerns of AI in Education
1.4.6 The Future of AI in Education
1.5 Overview of Intelligent School Facility Design
1.5.1 The Evolution of School Infrastructure: From Conventional to Intelligent Spaces
1.5.2 Key Components of an AI-Driven Smart School Facility
1.6 Conclusion
References
2. AI-Driven Smart Facilities for Modern Schools in Today’s Era
D. Kalaiabirami, M. Marikkannan, S.D. Prabu Ragavendiran, K. Sumathi and S. Ramesh
2.1 Introduction to AI-Driven Smart Facilities
2.1.1 Historical Context of Technology in Education
2.1.2 Evolution of Educational Technology
2.1.3 Role of AI in Shaping Modern Education
2.1.3.1 Benefits of AI in Education
2.1.3.2 Challenges in Adopting AI
2.1.3.3 Case Studies of Successful AI Integration
2.2 Digital Transformation in Schools
2.2.1 Key Components of Digital Transformation
2.2.1.1 Infrastructure Requirements
2.2.1.2 Teacher Training and Development
2.2.2 Challenges and Solutions for Digital Adoption
2.2.2.1 Addressing Infrastructure Gaps
2.2.2.2 Overcoming Resistance to Change
2.2.2.3 Ensuring Data Privacy and Security
2.2.3 Opportunities for Personalized Learning
2.3 Intelligent School Facility Design
2.3.1 Principles of Intelligent Facility Design
2.3.2 Key Features of Smart School Facilities
2.3.2.1 Smart Classrooms
2.3.2.2 Energy-Efficient Systems
2.3.2.3 Security Systems and Monitoring
2.3.3 Sustainability and Scalability of Smart Facilities
2.3.4 Future Trends in Educational Facility Design
2.4 Components of Modern School
2.5 Integrating Future Work Skills
2.6 Comparison between Traditional and Modern Education Systems
2.7 Conclusion
Bibliography
3. Understanding AI Technologies and Smart Systems
R. Felista Sugirtha Lizy, R. Vadivukarasi, Punit Pathak and Gusti Bagus Yosia Wiryakusuma
3.1 Introduction
3.1.1 AI Transforming Industries: Foundations of Automation and Data-Driven Decision-Making
3.1.2 Sector-Level Integration of AI: Overview across Healthcare, Finance, Manufacturing, and Education
3.2 Related Works
3.2.1 Applications of AI-Driven Automation and Decision-Making across Industrial Sectors
3.2.2 Operational Implementation of AI in Healthcare, Finance, Manufacturing, and Education
3.2.3 Explainable AI (XAI): Principles and Techniques for Transparent Decision-Making
3.2.4 Human–AI Collaboration: Conceptual Framework for Productivity Enhancement
3.2.5 AI and Quantum Computing: Emerging Computational Paradigms
3.3 AI Technologies in Machine Learning
3.3.1 AI Technologies in Natural Language Processing (NLP)
3.4 AI Technologies in Computer Vision
3.4.1 Applications of Computer Vision
3.4.2 Face Recognition and Biometric Authentication
3.4.3 Object Detection for Security and Surveillance
3.5 The Internet of Things (IoT) in Educational Facilities
3.6 Data Analytics and Predictive Modeling in AI and Smart Systems
3.7 Integrating AI with Existing Educational Infrastructure
3.8 Applications of Machine Learning
3.9 Challenges and Future of Machine Learning
3.9.1 Key Challenges in Machine Learning
3.9.2 Data Privacy Concerns in Machine Learning
3.9.3 Bias and Ethical Issues in AI
3.9.4 Computational Power Limitations in AI
3.10 Future Trends in AI and Smart Systems
3.10.1 Industry Applications and Regulatory Importance of Explainable AI
3.10.2 Case-Based Applications of Human–AI Collaboration across Industries
3.10.3 Industrial and Scientific Applications of Quantum-Enhanced AI Systems
3.11 Conclusion and Future Work
References
4. A Comprehensive System for Multi‑Source Content Extraction and Question Generation with LLM Validation
Suresh Manic K., Ahmed Al-Balushi, Al-Bemani, A.S., Saleh Alaraimi, Balaji G., Asiya Najeeb and Uma Suresh
4.1 Introduction
4.2 Literature Review
4.3 Methodology
4.3.1 Multi-Source Content Extraction and QA Generation
4.3.2 PDF-Based Multiple Choice Questions (MCQs) Generation
4.4 Implementation and Results
4.4.1 Multi-Source Content Extraction and QA Generation
4.4.2 PDF-Based Multiple Choice Questions (MCQs) Generation
4.5 Conclusion
4.6 Future Work
References
5. AI-Driven Analytics for Student Performance and Behavior
Mohan Singh, Seema Singh, Kapil Dev Tyagi and Vaibhav Bhushan Tyagi
5.1 Introduction
5.2 Understanding AI-Driven Analytics in Education
5.3 Applications of AI in Student Performance Analytics
5.4 Artificial Intelligence in Behavioral Analysis for Students
5.5 Data Sources and AI Techniques
5.6 Case Studies and Real-World Implementations
5.7 Challenges and Ethical Considerations
5.8 Future Trends and Innovations
5.9 Conclusion
References
6. Few-Shot Learning and Zero‑Shot Learning Paradigms
Shrikant Tiwari, Kanchan Naithani, Vinay Dwivedi and Ramesh Wadawadagi
6.1 Introduction
6.1.1 Background and Motivation
6.2 Foundations of Machine Learning
6.2.1 Supervised Learning
6.2.2 Unsupervised Learning
6.2.3 Semi-Supervised Learning
6.2.4 Transfer Learning
6.3 Few-Shot Learning
6.3.1 Approaches to Few-Shot Learning
6.3.1.1 Meta-Learning
6.3.1.2 Metric Learning
6.3.1.3 Data Augmentation
6.3.2 Challenges and Future Directions
6.4 Zero-Shot Learning
6.4.1 Problem Statement
6.4.2 Semantic Embeddings
6.4.3 Attribute-Based Methods
6.4.4 Generative Models
6.5 Hybrid Approaches
6.5.1 Transductive Learning
6.5.2 Pros and Cons of Hybrid Approaches
6.5.3 Challenges in Evaluating Few-Shot and Zero-Shot Learning
6.5.4 Novel Evaluation Metrics
6.6 Few-Shot and Zero-Shot Learning in Computer Vision
6.6.1 Few-Shot and Zero-Shot Learning in Healthcare
6.6.2 Ethical and Societal Considerations
6.6.3 Future Directions
6.7 Conclusion
References
7. Future of Human-AI Interaction in Education
Perarasi T., Manoj R., Deepika M., Kishore Bingi and Ramkumar R.
7.1 Introduction
7.2 AI as a Collaborative Partner in Education
7.2.1 Personalized Learning through AI
7.3 Envisioning AI-Enhanced Hybrid Classrooms
7.3.1 AI-Enabled Blended Learning Model
7.3.2 Case Studies of AI-Driven Hybrid Classroom
7.3.3 Benefits of AI in Facilitating Both In-Person and Online Learning
7.3.4 Bridging the Digital Divide
7.3.5 Real-Time Feedback and Performance Tracking
7.4 AI-Driven Virtual and Augmented Reality in Learning
7.4.1 How AR and VR Enhance Engagement in STEM and Other Subjects
7.4.2 Examples of AI-Powered Simulations in Education
7.5 Social and Emotional Dimensions of AI in Learning
7.5.1 AI for Emotional Intelligence and Mental Health Support
7.5.2 How AI Does Catch Emotional and Psychological Distress
7.6 Building Human-AI Trust into the Learning Process
7.6.1 Taming Doubts Regarding AI in Education
7.7 The Impact of AI on Student Collaboration and Peer Learning
7.7.1 How AI Fosters Group Learning Dynamics
7.7.2 AI-Powered Tools for Collaborative Projects
7.7.3 Balancing AI-Driven Guidance with Peer-to-Peer Interactions
7.7.4 AI in Promoting Ethical and Responsible Use of Technology
7.8 AI in Inclusive Education for Special Needs
7.8.1 Personalized Learning for Students with Disabilities
7.8.2 AI Tools for Language and Communication Support
7.9 Conclusion
References
8. Leveraging Generative AI for Computer Science Education
M. K. Abiodun, Amit Kumar Tyagi, J. B. Awotunde, A. E. Adeniyi, D. R. Aremu and S. Ejimogu
8.1 Introduction
8.2 Related Works
8.2.1 Education
8.2.2 Evolution of Education
8.2.3 Artificial Intelligence
8.2.4 Machine Learning: An Overview
8.2.5 Categories of Machine Learning
8.2.6 Generative AI
8.2.7 Evolution of Generative AI
8.2.8 Application of Generative AI to Education
8.2.9 Applications of Generative AI to Enhanced Learning
8.2.10 Applications of Generative AI to Enhanced Learning in Computer Field
8.2.11 Review of Related Works
8.3 Proposed Solution
8.3.1 Research Design Overview
8.3.2 Model Architecture
8.3.3 Overview of the Model
8.3.4 Implementing NLP in a Text Summarization Model
8.3.5 Implementing Transformers in a Text Summarization Model
8.4 Implementation and Results
8.4.1 Introduction
8.4.2 Development Tools
8.4.3 Libraries Used
8.4.4 Measures of Accuracy
8.4.5 Model Functionality and Screenshots
8.4.5.1 Importing Libraries
8.4.5.2 Text Summarization
8.4.5.3 Extracting Text from PDF Function
8.4.5.4 Question Answering
8.4.5.5 Plotting Evaluation Metrics
8.4.5.6 Performance Evaluation
8.4.5.7 Question Answering in PDF Using Different Models
8.4.5.8 Searching the Internet for Answers to Questions
8.4.5.9 Managing Word Processing Documents
8.5 Conclusion and Future Work
References
9. Personalized Learning Environments in AI-Driven Smart Facilities for Modern Schools
Vidish Kumar, Garima Jain, Ankush Jain and Sanny Kumar
9.1 Introduction
9.2 Dynamic Classroom Configurations through AI
9.3 AI-Enabled Adaptive Learning Spaces
9.4 Merging Physical and Virtual Learning: Blended Models
9.5 AI-Driven AR/VR Experiential Learning
9.6 Methodology and Implementation
9.7 Expanded Case Studies and Data Analysis
9.8 Conclusion and Future Directions
References
10. Smart Classroom Using AI‑Experimental Analysis
Swathi Gowroju, S. Sowjanya Chintalapati, B. Suresh Babu and Fateh Mebarek-Oudina
10.1 Introduction
10.2 Literature Survey
10.3 Proposed System
10.4 Proposed Architecture
10.5 Analysis and Results
10.6 Discussion
10.7 Conclusion
Bibliography
Part 2: Methods and Principles
11. Gamification and AI-Driven Engagement Tools
S.S. Sivaraju, B. Shuriya, Prabhu D., P. Vetrivelan, S. Sridharan and Ismail Musirin
11.1 Introduction
11.2 The Role of Gamification in Education
11.2.1 Intrinsic and Extrinsic Motivation
11.2.2 Engagement through Challenges
11.2.3 Instant Feedback
11.2.3.1 Feedback Categories for Gamified Learning
11.2.4 Personalized Learning Paths
11.2.4.1 AI-Based Gamification: Personalized Learning
11.2.4.2 Benefits of Gamified Personalized Learning
11.3 AI-Driven Engagement Tools in Education
11.3.1 Adaptive Learning Platforms
11.3.2 Education AI-Powered Chatbots and Virtual Personal Assistants
11.3.2.1 Advantages of AI-Based Chatbots in Education
11.3.3 AI-Based Gamification Engines: Enhancing Student Engagement and Personalized Learning
11.3.3.1 Conceptualizing AI-Driven Gamification in Education
11.3.3.2 Key Features of AI-Powered Gamification Engines
11.3.3.3 Advantages of AI-Facilitated Gamification in Education
11.3.3.4 Challenges and Future Directions
11.4 Enacting AI-Driven Gamification in Learning Environments
11.4.1 Learning Objectives
11.4.2 Choosing the Appropriate AI Tools
11.4.2.1 Encouraging Cooperation and Competition
11.4.2.2 Greater Interactivity with AI Personalization
11.4.2.3 Effectiveness Determination and Refining Strategy
11.4.3 Encouraging Collaboration and Competition
11.4.4 Enhancing Engagement through AI Personalization
11.4.5 Measuring Effectiveness and Adapting Strategies
11.5 Future Opportunities and Challenges
11.5.1 Challenges
11.6 Conclusion
References
12. AI for Student Engagement and Motivation
Jimit Patel, Meet Patel and Aniket Patel
12.1 Introduction
12.2 Related Works
12.2.1 Gamification and AI-Driven Engagement Tools
12.2.2 Personalizing Rewards and Motivation Strategies
12.2.3 Tracking Student Attention and Focus Levels
12.2.4 Applying AR/VR to Track Levels of Attention and Focus
12.2.5 Utilizing Digital Twins for Levels of Attention and Focus
12.3 Case Studies: Increasing Engagement with AI
12.4 Conclusion and Future Work
References
13. Hardware and Firmware Design of a Smart Classroom Automation System
Naman Tanwar, Monish Kumar H. S., Mohit Banka, Sushmit Sanskar, Vidhya S. and Konguvel E.
13.1 Introduction
13.2 Related Works
13.3 Hardware Design
13.3.1 Requirements Planning
13.3.2 Block Diagram
13.3.3 Components Selection
13.3.4 Symbol and Footprint Design
13.3.5 Circuit Design
13.3.6 PCB Layer Stackup
13.3.7 PCB Layout
13.3.8 Layout of Buck Converter
13.3.9 PCB 3D Render, Component Procurement and PCB Fabrication
13.3.10 Circuit Design for Push Buttons and Indicator LEDs
13.4 Firmware Design
13.4.1 IR Transmission to Control the Projector
13.4.2 Direct Push Button Control
13.4.3 Through Webserver Control
13.5 Results and Discussions
13.5.1 Test Setup
13.5.2 Manual Control Test
13.5.3 Webpage Control Test
13.5.4 IR Transmission and Projector Control Test
13.6 Future Improvements
13.7 Conclusion
References
14. Internal Service Quality in Higher Education: Exploring Key Determinants and Implications
Parthiban R. and Shefali Srivastava
14.1 Introduction
14.2 Theoretical Framework and Hypothesis Development
14.2.1 SERVQUAL Model
14.2.2 Work Environment and Internal Service Quality
14.2.3 Leadership Support and Internal Service Quality
14.2.4 Training and Development and Internal Service Quality
14.2.5 Employee Engagement and Internal Service Quality
14.2.6 Job Satisfaction as a Mediator
14.2.7 Conceptual Framework
14.3 Research Methodology
14.3.1 Research Design
14.3.2 Research Setting
14.3.3 Sampling Frame
14.3.4 Data Collection
14.3.5 Variable Measurement
14.3.6 Common Method Variance and Non-Response Bias
14.4 Result
14.4.1 Demographics Profile
14.4.2 Data Analysis
14.4.3 Measurement Model
14.4.4 Predictive Power of the Model
14.4.5 Structural Model
14.4.6 Path Coefficient
14.4.6.1 Direct Effect
14.4.6.2 Indirect Effect
14.5 Discussion
14.6 Conclusion
14.6.1 Theoretical Contribution
14.6.2 Practical and Managerial Implications
14.6.3 Limitations and Future Studies
References
Part 3: Applications
15. Role of Artificial Intelligence in the Development of Education Industry 4.0: A Systematic Review
Aashka Thakkar, Axita Thakkar and Tsegaye Mayhewos
15.1 Introduction
15.2 Bibliometric Literature Review
15.3 The Need if Integration of AI in Higher Education
15.4 Usage of AI Tools for Education
15.5 Increasing Role of AI in Personalized Education
15.6 Challenges of Using AI in Education 4.0
15.7 Why AI and Education Should Go Hand in Hand
Bibliography
16. Generative AI Technologies, Models and Applications
Shruti Mehra and Avinash Kumar Sharma
16.1 Introduction
16.1.1 What is Generative AI?
16.2 Literature Review
16.3 Generative AI Technologies
16.3.1 Generative Adversarial Networks (GANs)
16.3.2 Variational Autoencoders (VAEs)
16.3.3 Diffusion Models
16.4 Transformer-Based Models for Generation
16.4.1 Introduction to Transformers
16.4.2 Text Generation with GPT Models
16.4.3 Cross-Modal Generation
16.5 Applications of Generative AI
16.5.1 Content Creation and Entertainment
16.5.2 Healthcare and Drug Discovery
16.5.3 Virtual Reality and Simulation
16.6 Conclusion
Bibliography
17. Machine Learning and Deep Learning-Based Intelligent Tutoring Systems
Vikram Singh, Sangeeta Rani and Sanjay Singh
17.1 Intelligent Tutoring Systems
17.2 Related Works
17.3 Learner Profiling Model
17.4 Question-Topic Mapping Model
17.5 A Framework for ITS
17.5.1 The Architecture
17.5.1.1 The Basis
17.5.1.2 Extensibility
17.5.1.3 Problem Classifier
17.5.1.4 Personalization
17.5.1.5 Learner Profiling
17.6 Conclusion
References
18. Integrating Digital Twin for Acquiring Smart Classroom Facilities to Enhance Teaching-Learning Process
Ramya Perumal, Kaladevi A.C. and V. Lathika
18.1 Introduction
18.1.1 Field of Study
18.2 General Framework for Digital Twin
18.2.1 Applications of Digital Twin (DT) That Contribute Towards Various Education Domain
18.2.2 Challenges and Their Possible Solutions to Alleviate Risks
18.2.3 Contribution of Digital Twin Especially in Different Streams of Education Domain-A Glimpse
18.3 Creating Digital Twins for School Buildings and Classrooms
18.3.1 Traditional Methodology
18.3.2 Steps Involved in Designing Digital Twins for School Building and Classroom Environment
18.3.3 Use Cases for School Digital Twin
18.3.4 Utilities and Technologies Associated with Designing Digital Twin
18.4 Predictive Maintenance and Real-Time Facility Management
18.4.1 HVAC System
18.4.2 Real-Time Facility Management in the School Building
18.4.3 Advantages of Digital Twins in Schools
18.4.4 Challenges Associated with Digital Twins in School Buildings
18.5 Simulating Learning Environments for Research and Training
18.5.1 Digital Twin in Learning Environments
18.5.2 Applications in Research and Training Programs for Education Domain
18.5.3 Components of Digital Twin in Learning
18.5.4 Benefits of Digital Twins in Learning
18.5.5 Real-World Applications of Digital Twins
18.5.6 Autodesk Tinkercad Tool
18.5.7 Key Features of Autodesk Tinkercad
18.5.8 Applications of Autodesk Tinkercad
18.5.9 Designing School Layout by Using Digital Twins from Its Toolsuite
18.6 Future Potentials of Using Digital Twins in the Education Domain
18.7 Conclusion
References
19. Bridging Realities with AI-Enabled Software Development: Generative AI, Digital Twins, and the Future of Privacy
Yashendra Rajput, M.H. Razi and Avinash Kumar Sharma
19.1 Introduction
19.1.1 A New Era in Software Development
19.1.2 Why Privacy Matters in this Revolution
19.2 The Rise of AI in Software Development
19.2.1 Transforming Software Development: The Integration and Impact of AI Technologies
19.2.2 The Dawn of Generative AI and Digital Twins
19.3 Generative AI: Powering the Code Revolution
19.3.1 How Generative AI Works
19.3.2 Real-World Applications and Benefits
19.3.3 The Limits of AI-Driven Coding
19.4 Digital Twins: Simulating Reality in Real Time
19.4.1 Understanding Digital Twins
19.4.2 Using Digital Twins to Improve Development
19.4.3 Synergy with Generative AI
19.4.4 Privacy on the Edge: Challenges in an AI World
19.4.5 The Data Dilemma
19.4.6 Privacy Risks in Practice
19.4.7 Ethical Questions and User Trust
19.4.8 Regulatory Frameworks in AI and Privacy
19.4.9 Practical Solutions for Developers
19.4.10 Case Studies in Responsible AI
19.5 The Future of AI-Enhanced Software Development
19.5.1 What’s Next for Generative AI and Digital Twins
19.5.2 Redefining Industries and Society
19.5.3 A Call for Thoughtful Progress
19.6 Conclusion
19.6.1 Tying It All Together
19.6.2 Final Thoughts
Bibliography
Part 4: Challenges and Future Research Directions
20. Beyond the AI Hype: Practical and Ethical Dimensions of AI in Education
Ashu Verma, Anushka Gupta and Amit Kumar Tyagi
20.1 Introduction
20.2 Background Work
20.3 Literature Review
20.4 Problem Definition
20.5 Objectives
20.6 Comparative Analysis of Literatures
20.7 Proposed Solution
20.8 Discussion
20.9 Limitations and Future Work
20.10 Conclusion
References
21. Emotion-Driven Music Recommendations Using Speech Recognition and an Interactive Gradio Interface for Personalized Content Delivery
Rukmani P and Rachana Supriya Nandipati
21.1 Introduction
21.2 Related Work
21.3 Existing Work
21.4 Proposed Work
21.4.1 System Design and Workflow
21.4.1.1 Audio Pre-Processing
21.4.1.2 Emotion Recognition
21.4.1.3 Music Recommendation
21.5 Methodology
21.5.1 Dataset
21.5.2 Exploratory Data Analysis (EDA)
21.5.2.1 Emotion Distribution
21.5.2.2 Feature Analysis
21.5.3 Pre-Processing
21.5.3.1 Audio Validation and Normalization
21.5.3.2 Feature Extraction
21.5.4 Baseline Models for Comparison
21.5.4.1 Random Forest
21.5.4.2 XG Boost
21.5.4.3 Support Vector Machines (SVM)
21.5.4.4 K-Nearest Neighbours (KNN)
21.5.5 Adopted Model – Long Short-Term Memory (LSTM)
21.5.5.1 Model Architecture
21.5.5.2 Training Process
21.5.5.3 Strengths
21.5.5.4 Weaknesses
21.5.6 Music Recommendation Engine
21.5.6.1 Emotion-to-Genre Mapping
21.5.6.2 Integration of Spotify API
21.5.6.3 Enhanced User Experience
21.5.7 User Interface
21.5.7.1 Audio Input
21.5.7.2 Emotion Prediction
21.5.7.3 Visualization
21.5.7.4 Music Recommendation
21.6 Evaluation Metrics
21.6.1 Accuracy
21.6.2 Precision
21.6.3 Recall
21.6.4 F1-Score
21.6.5 Confusion Matrix
21.7 Results and Discussion
21.8 Conclusion
21.9 Challenges and Future Scope
Bibliography
22. Transforming Students into Ethical Leaders through AI-Enhanced Pedagogical Practices and a Model for the Future of Education
P. Kiruthika, D. Sivabalaselvamani, D. Selvakarthi and Ranjit Singh Sarban Singh
22.1 Introduction
22.1.1 Outcome-Based Education: A Shift toward Holistic Competencies
22.1.2 The Role of Artificial Intelligence in Enhancing Educational Outcomes
22.1.3 Ethical Leadership Development in the Context of OBE and AI
22.2 Related Works
22.2.1 Incorporating Artificial Intelligence in Education: Advantages and Ethical Issues
22.2.2 AI Impact in Education Sector
22.2.3 Outcome Based Education
22.2.4 Ethical Leadership in Education
22.2.5 Pedagogical Practices That Reflect the Use of AI
22.2.6 Employability Skills
22.2.7 Recent Advancements in AI and OBE Integration
22.2.8 AI-Driven Transformations in Outcome-Based Education
22.2.9 Enhancing Employability through AI and OBE
22.2.10 Promotional Goals of AI Enhanced OBE
22.2.11 Recent Studies
22.3 Transforming Education through AI Enhanced Outcome Based Learning
22.3.1 The Transition from Traditional to AI Enhanced Education
22.3.2 Impact on Student Outcomes and Employability
22.3.3 Integration of Ethical Leadership Development
22.4 Implementation
22.5 Academic Performance
22.5.1 Employability Skills
22.5.2 Ethical Leadership
22.5.3 AI-Enhanced Learning Tools
22.5.4 Student Engagement and Satisfaction
22.5.5 Statistical Analysis Overview
22.5.6 Results Presentation
22.6 Conclusion and Future Work
References
23. Artificial Intelligence - Natural Language Processing Based Business Intelligence
Pooja Bhatt, Shabnam Kumari and Amit Kumar Tyagi
23.1 Introduction to Business Intelligence
23.1.1 Role of NLP in BI
23.1.2 Organization of the Work
23.2 Foundations of NLP in Business Intelligence
23.2.1 Understanding Natural Language Processing and Evolution of NLP in BI
23.2.2 Key Components and Techniques of NLP in BI
23.3 Applications of NLP in Business Intelligence
23.4 NLP Techniques for Data Preparation and Analysis
23.5 Implementation Strategies for NLP-Based BI
23.6 Open Issues and Challenges and Future Directions for NLP Based BI
23.7 Conclusion
References
24. Securing the Modern World Using AI-Based Quantum Computing
D. Kalaiabirami, M. Marikkannan and K. Sumathi
24.1 Introduction to AI-Based Quantum Security
24.1.1 Overview of AI and Quantum Computing
24.1.1.1 Artificial Intelligence (AI) and Machine Learning (ML)
24.1.1.2 Introduction to Quantum Computing
24.1.1.3 Intersection of AI and Quantum Computing
24.2 Fundamentals of Quantum Security
24.2.1 Principles of Quantum Mechanics in Security
24.2.1.1 Superposition and Entanglement
24.2.1.2 Quantum Bits (Qubits) vs. Classical Bits
24.2.2 Quantum Cryptography
24.2.2.1 Quantum Key Distribution (QKD)
24.2.2.2 Post-Quantum Cryptography (PQC)
24.2.2.3 AI-Enhanced Quantum Cryptographic Algorithms
24.3 AI’s Role in Quantum Security
24.3.1 AI-Driven Quantum Machine Learning (QML)
24.3.1.1 Anomaly Detection in Cybersecurity Using QML
24.3.1.2 AI-Optimized Quantum Algorithms for Secure Computing
24.3.2 AI in Threat Detection and Prevention
24.3.2.1 AI Enabled Intrusion Detection System (IDS)
24.3.2.2 Automated AI Based Threat Intelligence
24.4 Applications of AI-Based Quantum Security
24.4.1 Enhancing Cybersecurity with AI and Quantum Computing
24.4.1.1 Secured Data Transmission and Communication
24.4.1.2 AI-Powered Secure Authentication Systems
24.4.2 Financial Security and Fraud Prevention
24.4.2.1 AI-Based Fraud Detection in Banking and Finance
24.4.2.2 Quantum Secured Blockchain Transactions
24.4.3 National Defense and Intelligence Security
24.4.3.1 Quantum-Enhanced Secure Military Communications
24.4.3.2 AI-Powered Threat Analysis and Prevention
24.5 Detecting and Responding to Threats Using AI and Quantum Computing
24.5.1 Predictive Threat Modeling with AI
24.5.2 Threat Detection and Reaction in Real Time
24.5.3 Automated Incident Response Driven by AI
24.5.4 Zero-Day Threats with Quantum-Secure AI
24.6 Challenges and Future Directions
24.6.1 Challenges in AI-Based Quantum Security
24.6.1.1 Quantum Hardware Limitations
24.6.1.2 Ethical Considerations in AI-Driven Quantum Systems
24.6.1.3 Potential Quantum Cybersecurity Threats
24.6.2 Future Prospects and Innovations
24.6.2.1 Improving Quantum Cryptography
24.6.2.2 AI-Driven Quantum Cyber Defense Systems
24.6.2.3 Commercialization of Quantum Security Solutions
24.7 Conclusion
Bibliography
25. AI in Security and Safety Management
R. Felista Sugirtha Lizy, R. Vadivukarasi, Punit Pathak and Ir. Bambang Sugiyono Agus Purwono
25.1 Introduction to AI in Security and Safety Management
25.1.1 AI Applications in Security & Safety
25.1.2 Surveillance and Facial Recognition
25.1.3 Intrusion Detection Systems (IDS)
25.1.4 AI-Powered Access Control
25.1.5 Smart Security Cameras
25.2 Literature Review: AI in Security and Safety Management
25.3 AI in Cybersecurity
25.3.1 AI-Driven Threat Detection
25.3.2 Anomaly Detection in Network Security
25.3.3 Automated Incident Response
25.3.4 AI for Fraud Detection
25.4 AI in Physical Security
25.4.1 AI-Enhanced Perimeter Security
25.4.2 Automated Emergency Response Systems
25.4.3 AI in Law Enforcement
25.5 AI in Workplace Safety
25.5.1 AI for Hazard Detection
25.5.2 Predictive Maintenance and Risk Analysis
25.5.3 Smart PPE and Wearable Safety Devices
25.6 Ethical and Legal Challenges of AI in Security and Safety
25.6.1 AI Bias and Privacy Concerns
25.6.2 Regulatory Frameworks for AI in Security
25.6.3 Ethical AI Deployment in Law Enforcement
25.7 Conclusion
References
26. The Future of Intelligent School Facilities: Emerging AI Technologies in Education
S. Umamageswari, L. Sangeetha, V. Shyamala Susan and I Gusti Bagus Yosia Wiryakusuma
26.1 Introduction
26.1.1 Purpose of the Chapter
26.1.2 Structure of the Chapter
26.2 Literature Review: The Evolution of AI in Education
26.2.1 Early Use of AI in Education
26.2.2 AI in Modern Education
26.3 The Role of AR, VR, and XR in Transforming Education
26.3.1 Understanding Augmented Reality (AR) in Education
26.3.2 Virtual Reality (VR) in Education
26.3.3 Extended Reality (XR) in Education
26.4 Benefits of AI and Smart Learning Technologies in Education
26.4.1 Enhanced Student Engagement
26.4.2 Personalized Learning Experiences
26.4.3 Collaborative Learning and Communication
26.5 AI in School Facility Management
26.5.1 Smart Classrooms: Enhancing the Learning Environment with AI
26.5.2 Predictive Maintenance: Preventing Downtime and Reducing Repair Costs
26.5.3 Energy Management and Sustainability in Schools
26.5.4 AI in Facility Security: Enhancing School Safety
26.5.5 Smart Building Management: Integrating AI for Operational Efficiency
26.5.6 AI for Cleaning and Waste Management
26.5.7 AI in Crisis and Emergency Management
26.6 Challenges and Barriers to the Integration of AI and Smart Technologies in Education
26.6.1 High Implementation Costs
26.6.2 Teacher Training and Professional Development
26.6.3 Data Privacy and Security
26.6.4 Limited Infrastructure and Connectivity
26.6.5 Equity and Access to Technology
26.6.5.1 Equity in Access to Smart Technologies
26.6.6 Technological Dependence and Potential Overload
26.7 Future Directions for AI and Smart Learning Technology
26.7.1 Merging with the Internet of Things (IoT)
26.7.2 Hybrid Classrooms: Interweaving Physical and Virtual Learning
26.7.3 AI-Augmented Testing and Personalized Feedback
26.7.4 Advanced Smart Experiences with 5G and Edge Computing
26.7.5 AI-Driven Collaborative Learning
26.7.6 Gamification and AI-Driven Learning Environments
26.8 Conclusion
References
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