Artificial Intelligence: A Multidisciplinary Approach towards Teaching and Learning
1st Edition
Published on 19. November 2024
305 pages
978-981-5305-18-0 (ISBN)
Description
Artificial Intelligence: A Multidisciplinary Approach towards Teaching and Learning explores the evolving role of AI in education, covering applications in fields such as bioinformatics, environmental science, physics, chemistry, economics, and language learning. Written by experts, this book provides a comprehensive overview of AI's integration into diverse subjects, offering insights into the future of AI in education and its potential to enhance academic research and pedagogy.
Targeted at faculty, students, and professionals, the book addresses AI's role in blended learning environments and offers practical tools for educators seeking to incorporate AI into their teaching practices.
Key Features:
- Multidisciplinary exploration of AI in teaching and learning.
- Practical tools and methodologies for educators.
- Insights into AI-driven innovations in research.
- Relevant to a broad audience, from students to professionals.
Readership:
Undergraduate/Graduate students, academics, and professionals interested in AI applications in education.
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Tahmeena Khan | Manisha Singh | Saman Raza
Artificial Intelligence
A Multidisciplinary Approach towards Teaching and Learning
Book
11/2024
Bentham Science Publishers
€93.85
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Content
- Cover
- Title
- Copyright
- End User License Agreement
- Contents
- Foreword I
- Foreword II
- Preface
- List of Contributors
- The Evolution of Artificial Intelligence from Philosophy to New Frontier
- Manisha Singh1,*, Arbind K. Jha2, Tahmeena Khan3 and Saman Raza4
- INTRODUCTION
- THE HISTORY OF ARTIFICIAL INTELLIGENCE (AI)
- PHILOSOPHY AND AI: A PHILOSOPHICAL JOURNEY
- PHILOSOPHICAL CONSIDERATION OF AI
- Metaphysics and AI
- Epistemology and AI
- Axiology and AI
- Framework of AI
- HUMAN-MACHINE TEAMING FRAMEWORK
- FORMS OF AI
- Based on Capabilities
- Artificial Narrow Intelligence
- Artificial General Intelligence
- Artificial Super Intelligence
- Generative AI
- Based on Functionality Artificial Intelligence
- Reactive Machines
- Limited AI
- Theory of Mind AI
- Self-aware AI
- Some other forms of AI
- AI AND NEW FRONTIERS
- AI and Medical Science
- AI and Life Science
- AI and Mathematics
- AI and Architecture
- AI and Environmental Science
- AI in Education
- AI in Research
- ChatGPT/Perplexity/GoogleBard
- PDFgear
- Wordvice AI
- Consensus
- Trinka
- QuillBot AI
- Page.AI
- Zotero, EndNote Online, Mendeley, RefWorks, etc
- AI, HUMAN INTELLIGENCE AND HUMAN WISDOM
- CONCLUDING REMARKS
- REFERENCES
- Artificial Intelligence and Bioinformatics: A Powerful Synergy for Drug Design and Discovery
- Chanda Hemantha Manikumar Chakravarthi1, Viswajit Mulpuru1 and Nidhi Mishra2,*
- INTRODUCTION
- Overview of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Importance of Drug Design
- Challenges in Traditional Drug Discovery
- DATA ANALYSIS AND PREPROCESSING
- Utilizing Biological Databases
- Omics Data Integration
- Data Cleaning and Feature Extraction
- Data Cleaning and Pre-processing
- Feature Extraction Techniques
- Handling Imbalanced Datasets
- Oversampling and Undersampling
- Advanced Algorithms for Imbalanced Data
- Addressing Batch Effects
- Definition of Batch Effects
- Ensuring Consistency
- PREDICTIVE MODELLING
- Classification Algorithms
- Support Vector Machines (SVM)
- Random Forests
- Neural Networks
- Regression Analysis
- Quantitative Structure-Activity Relationship (QSAR)
- Predicting Molecular Properties
- VIRTUAL SCREENING
- Target Identification and Validation
- Omics Data Integration
- Disease Gene Prediction
- Expression Profiling and Differential Analysis
- Pharmacogenomics
- Text Mining and Literature Analysis
- Validation through High-Throughput Screening (HTS)
- Integration of Structural Biology Data
- Ligand-Based Virtual Screening Techniques
- Molecular Descriptors and Fingerprints
- Quantitative Structure-Activity Relationship (QSAR)
- Machine Learning Classifiers
- Pharmacophore Modeling
- Chemical Similarity Networks
- Ensemble Methods
- Structure-Based Virtual Screening
- Protein-Ligand Docking
- Scoring Functions
- Deep Learning in Binding Affinity Prediction
- Machine Learning Filters
- Consensus Scoring
- Machine Learning for Binding Site Prediction
- Fragment-Based Virtual Screening
- DE NOVO DRUG DESIGN
- Generative Models in Drug Design
- Generative AI in bioinformatics
- Generative AI in Drug Design
- Generative AI revolutionizes Drug Discovery Processes
- Variational Autoencoders (VAEs)
- Generative Adversarial Networks (GANs)
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks
- Transformer-Based Models
- Graph Generative Models
- Conditional Generative Models
- Transfer Learning in Generative Models
- Reinforcement Learning for Molecule Generation
- Objective Function Definition
- Policy Networks
- Action Space Representation
- Monte Carlo Tree Search (MCTS)
- Actor-Critic Models
- Exploration Strategies
- Transfer Learning and Pre-training
- DRUG REPURPOSING
- Identifying New Indications for Existing Drugs
- Biological Data Integration
- Drug Similarity and Similarity Networks
- Disease Similarity and Phenotype Matching
- Text Mining and Literature Analysis
- Predictive Modeling for Drug-Disease Associations
- Network Propagation Algorithms
- Electronic Health Records (EHR) Analysis
- Multi-Omics Data Integration
- Utilizing Machine Learning for Drug Repositioning
- Data Integration and Representation
- Feature Extraction and Engineering
- Predictive Modelling for Drug-Disease Associations
- Network-Based Approaches
- Deep Learning Models
- Text Mining and Literature Analysis
- Clinical Data Mining
- Ensemble Learning
- PHARMACOPHORE MODELLING
- Molecular Interaction Understanding
- Drug Design and Optimization
- Virtual Screening
- Lead Identification and Optimization
- Polypharmacology Analysis
- Structure-Activity Relationship (SAR) Analysis
- Fragment-Based Drug Design
- Target Druggability Assessment
- Pharmacokinetic and Toxicity Prediction
- Adverse Effects Mitigation
- Feature Selection and Descriptor Generation
- Training Data Generation
- Enhanced Pharmacophore Screening
- Predictive Pharmacophore Modeling
- Polypharmacology Prediction
- Druggability Assessment
- Hybrid Approaches
- Pharmacophore Optimization
- Data-Driven Drug Design
- PERSONALIZED MEDICINE
- Tailoring Treatments Based on Individual Genetic Profiles
- Importance and Benefits
- Application of Machine Learning
- Examples of Personalized Medicine Applications
- Ethical and Regulatory Considerations
- Future Directions
- Machine Learning in Patient Stratification
- Key Components of Patient Stratification
- Importance and Benefits
- Applications of Machine Learning
- Examples of Patient Stratification
- Challenges and Considerations
- Future Directions
- CHALLENGES AND FUTURE DIRECTIONS
- Data Quality and Availability
- Data Quality Issues
- Data Standardization and Integration
- Limited Accessibility
- Small Sample Sizes
- Biological Variability
- Ethical Considerations
- Future Directions
- Advancements in Personalized Medicine
- Ethical and Regulatory Considerations
- Patient Privacy and Informed Consent
- Data Ownership and Sharing
- Bias and Fairness in Models
- Regulatory Compliance
- Inclusivity in Research
- Transparency in AI Decision-Making
- Future Directions
- Emerging Technologies and Trends in Drug Design
- Artificial Intelligence (AI) and Machine Learning
- Quantum Computing
- Structural Biology Advancements
- Immunotherapy and Personalized Medicine
- CRISPR and Gene Editing
- Nanotechnology in Drug Delivery
- Data Integration and Systems Biology
- 3D Printing in Drug Manufacturing
- Blockchain for Data Security
- CONCLUDING REMARKS
- Artificial Intelligence (AI) and Machine Learning
- Quantum Computing
- Immunoinformatics
- CRISPR-Cas9 and Gene Editing
- 3D Bioprinting
- Nanotechnology
- RNA Therapeutics
- Pharmacogenomics
- Virtual Reality (VR) and Augmented Reality (AR)
- Blockchain in Drug Development
- Metabolomics and Systems Biology
- Synthetic Biology
- Potential Impact on the Pharmaceutical Industry
- Acceleration of Drug Discovery
- Revolutionizing Vaccine Development
- Precision Medicine and Personalized Therapies
- Efficient Drug Testing and Development
- Targeted Drug Delivery and Formulation
- Innovations in RNA Therapeutics
- Optimizing Drug Responses
- Immersive Research Environments
- Ensuring Data Integrity and Compliance
- Comprehensive Understanding of Drug Impact
- Biosynthesis and Customized Biological Systems
- REFERENCES
- Artificial Intelligence Assisted Teaching and Learning and Research of Environmental Sciences
- Tahmeena Khan1,*, Priya Mishra2, Kulsum Hashmi2, Saman Raza2, Manisha Singh3, Seema Joshi2 and Abdul Rahman Khan1
- INTRODUCTION
- Generative AI in Education
- AI In Teaching, Learning and Academic Achievement
- AI-Based Tools and Methodologies in Environmental/Geoscience Teaching
- Different AI Techniques Used in Environment and Geosciences-Based Research
- Hazard Identification
- Risk Assessment
- Risk Evaluation
- Decision Making
- Earthquakes
- Volcano
- Landslide
- Rainfall
- Cyclones
- Meteorological Drought
- Wildfire
- Dust storm
- Anthropogenic Air Pollutants
- AI in Biosphere
- Chat GP and Environmental Science
- CHALLENGES IN AI IN ENVIRONMENTAL SCIENCE BASED RESEARCH
- Choosing a Suitable Model
- Training Optimization
- Data Preparation
- Ethical Issues
- CONCLUDING REMARKS
- REFERENCES
- Integrating AI Approaches in Teaching-Learning Associated with the Mitigation of Air Pollution: A Comprehensive Analysis
- Rahila Rahman Khan1,*, Ahmad Faiz Minai2 and Rushda Sharf1
- INTRODUCTION
- OVERVIEW OF THE CURRENT STATE OF AIR POLLUTION AND ITS IMPACT
- APPLICATIONS OF AI IN ENVIRONMENTAL CHALLENGES
- Environmental Monitoring
- Climate Modeling
- Biodiversity Conservation
- Renewable Energy
- POTENTIAL OF AI IN ADDRESSING AIR POLLUTION
- Data Analysis and Prediction
- Source Identification
- Early Warning Systems
- Policy Formulation
- PROBLEMS WITH CONVENTIONAL AIR QUALITY MONITORING TECHNIQUES
- Restricted Coverage
- Temporal Limitations
- High Installation and Maintenance Costs
- Data Timeliness
- AI-BASED AIR QUALITY MONITORING
- Remote Sensing and Satellite Technology
- Integration of Satellite Data
- AI Algorithms for Data Analysis and Interpretation
- Sensor Networks and IoT Devices
- Deployment of Smart Sensors
- Machine Learning for Sensor Data Analysis
- UTILIZING AI FOR TIMELY INFORMATION
- AI TECHNIQUES FOR IDENTIFYING AND QUANTIFYING POLLUTION SOURCES
- Data Fusion and Integration
- Chemical Mass Balance Models
- Source Separation Algorithms
- INCORPORATING AI INSIGHTS INTO CITY PLANNING FOR POLLUTION CONTROL
- Zoning and Land Use Planning
- Traffic Management
- Emission Reduction Strategies
- AI AND POLICY IMPLEMENTATION
- OVERCOMING CHALLENGES IN POLICY IMPLEMENTATION
- PUBLIC AWARENESS AND ENGAGEMENT
- FUTURE INNOVATIONS AND RESEARCH DIRECTIONS
- CONCLUDING REMARKS
- REFERENCES
- Applications of Neural Network in Physics: Cosmology and Molecular Dynamics
- Vivekanand Mohapatra1, Dhruv Agrawal1,* and Shubhamshree Avishek2
- INTRODUCTION TO ML AND NEURAL NETWORK
- MACHINE LEARNING IN 21-CM COSMOLOGY
- Differential Brightness Temperature
- Challenges in Observational Cosmology
- Modelling the Foreground Signal
- Modeling the Differential Brightness Temperature
- Application of ANN in Cosmology
- Basic Architecture of ANN
- Parameter Estimation using ANN
- INTRODUCTION TO MOLECULAR DYNAMICS SIMULATIONS
- Recurrent Neural Networks
- Understanding Sequential Data Processing in RNNs
- Integration of RNNs with Physics
- CONCLUDING REMARKS
- REFERENCES
- Role of Artificial Intelligence in Teaching and Learning Chemical Sciences
- Shahla Tanveer1,*, Mariyam Tanveer2 and Ayesha Tanveer3
- INTRODUCTION
- CHEMICAL REPRESENTATION OF ATOMS AND MOLECULES IN COMPUTER-UNDERSTANDABLE FORMAT
- Molecular Graph Representation
- Simplified Molecular Input Line Entry System (SMILES)
- InChi
- APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN CHEMICAL SCIENCES
- Retrosynthesis
- Reactant Selection
- Template Selection
- Prediction of Reaction Outcomes
- Molecular Designing
- Simulator
- Evaluator
- Constraints
- Specifications
- GP (Genetic Programming)
- Visualizer
- Control Interface
- Properties for Electronic Data
- Pharmaceutical Applications
- Reactive Properties and Catalyst Optimization
- Structure and Docking Ability
- Molecular Property Prediction
- ROLE OF GENERATIVE AI IN CHEMICAL SCIENCES
- Benefits of Integrating Generative AI in Chemistry Learning and Teaching
- Enhanced Student Involvement
- Instantaneous Answers and Assistance
- Customised Learning
- Promotion of Critical Thinking
- Access to Extra Learning Resources
- Facilitation of Continuous Learning
- Reinforcement of Essential Knowledge
- Supplementing Limited Resources
- Role of ChatGPT in Promoting Student Engagement and Active Learning
- Interactive Conversations
- Instant Response and Feedback
- Scaffolded Learning
- Fostering Curiosity and Inquiry
- Exploratory Learning
- Adaptable Learning Environments
- Active Problem-Solving
- Fostering Discussion and Collaboration
- CHALLENGES AND LIMITATIONS
- Underdeveloped Technologies
- Lack of AI Skills
- Inadequate Data
- Trust and Transparency Concerns
- Uncertain ROI
- Data Bias
- Limited Generalisation
- High Computing Requirements
- Ethical Concerns
- Integration Challenges
- FUTURE PROSPECTS
- Accelerated Medication Discovery
- Precision Medicine
- Green Chemistry
- Material Design and Discovery
- Automation and Robots
- Integration of Big Data
- CONCLUDING REMARKS
- REFERENCES
- AI Tools for Teaching-Learning Chemistry
- Saman Raza1,*, Satya1, Tahmeena Khan2 and Manisha Singh3
- INTRODUCTION
- TYPES OF AI
- GENERATIVE AI
- APPLICATIONS OF AI IN CHEMISTRY
- Prediction of Chemical Reactions
- Drug Design
- Material Design
- Others
- AI POWERED TOOLS AND APPLICATIONS FOR TEACHING AND LEARNING CHEMISTRY
- Tutoring Systems using AI
- Interactive Learning Platforms
- ChatGPT
- Smodin Chemistry Homework Solver
- HyperWrite's Chemistry Assistant
- SorSor
- FormuTodo
- STUDIES TO DETERMINE THE INFLUENCE OF AI IN LEARNING CHEMISTRY
- THE BENEFITS OF USING AI FOR CHEMISTRY EDUCATION
- DRAWBACKS AND CHALLENGES
- CONCLUDING REMARKS
- REFERENCES
- Transformation in the World of Commerce and Economics through AI
- Umang Tandon1,*, Apoorva Tandon2 and Tarang Mehrotra3
- INTRODUCTION
- Predictive Analytics
- Operational Decision-Making
- Strategic Integration
- Operational Efficiency
- Risk Mitigation
- Ethical Imperatives
- OBJECTIVE
- Identifying and Synthesizing Key Findings from Existing Research
- Addressing Gaps in Understanding AI's Impact on Commerce and Economics
- AI IN ANALYTICS AND DECISION-MAKING
- Predictive Analysis
- Descriptive Analytics
- Decision-Making Processes
- ECONOMIC IMPLICATIONS OF AI
- RISK MITIGATION
- Productivity Enhancement
- Labour Dynamics
- ADDRESSING INHERENT BIASES IN AI MODELS
- Gender Bias
- Racial Bias
- Market Bias
- Biases in Labor Markets
- Policy Bias
- RECTIFICATION PROCESSES
- Data Diversification for Holistic Representation
- Algorithmic Transparency: Unveiling the Black Box
- Continuous Model Evaluation: The Lifeline of Bias Rectification
- Stakeholder Collaboration: A Collective Approach
- CONCLUDING REMARKS
- REFERENCES
- Transforming English Pedagogy with Artificial Intelligence: Enroute to Enhanced Language Learning
- Leena Rajak1, Sangeeta Chauhan1,* and Sonu Bara1
- INTRODUCTION
- What is Artificial Intelligence (AI)?
- GENERATIVE ARTIFICIAL INTELLIGENCE (GAI)
- EVOLUTION PROCURED BY GENERATIVE AI IN THE FIELD OF EDUCATION
- English Language Education
- Technology in Language Teaching
- Online Language Learning Platforms
- Language Learning Apps
- Virtual Reality (VR) and Augmented Reality (AR)
- Online Tutoring and Video Conferencing
- Digital Language Resources
- Interactive Whiteboards and Smartboards
- LANGUAGE LEARNING MANAGEMENT SYSTEMS (LMS)
- Speech Recognition Technology
- Educational Software and Apps
- Social Media and Online Communities
- Virtual Assistants for Language Learning
- Intelligent Tutoring Systems
- Natural Language Processing
- Gamification and Interactive Learning
- Accessibility and Inclusivity
- The Role of Teachers
- The Future of English Language Education
- ROLE OF AI IN ENGLISH AND LANGUAGE LEARNING
- Personalized Learning
- Immediate Feedback
- Enhanced Engagement
- Accessibility
- Language Analysis
- Natural Language Processing (NLP)
- Adaptive Assessment
- 24/7 Availability
- Data-Driven Insights
- Language Generation
- CHALLENGES IN THE IMPLEMENTATION OF AI TECHNOLOGY IN LANGUAGE LEARNING
- Access and Equity
- Quality of Content
- Data Privacy and Security
- Lack of Personalization Understanding
- Integration with Traditional Pedagogy
- Ethical Considerations
- User Engagement and Motivation
- Cost of Implementation
- Adaptability and Continuous Improvement
- Overreliance on Technology
- FUTURE SCOPE
- CONCLUDING REMARKS
- REFERENCES
- Revolutionizing Learning Landscapes: Unleashing the Potential of AI in the Realm of Academic Research
- Waseem Zahra1,* and Gunjan Rautela2
- INTRODUCTION
- ACADEMIC RESEARCH
- THE ADVANCEMENT OF AI IN ACADEMIC RESEARCH IN THE 21ST CENTURY
- ROLE OF AI IN REVOLUTIONIZING ACADEMIC RESEARCH
- Using AI Techniques to Review the Literature and Gain Research Knowledge
- AI in Writing Research Hypothesis
- AI in Academic Writing
- Applying AI to Data Analysis
- Recommendation System
- NAVIGATING THE RESEARCH JOURNEY WITH ARTIFICIAL INTELLIGENCE: ESSENTIAL STEPS
- Define Research Objectives
- Literature Review
- Formulate Research Hypotheses or Questions
- Data Collection
- Data Preprocessing
- Feature Engineering
- Model Selection
- Training and Validation
- Evaluation
- Analysis and Interpretation
- Documentation and Reporting
- Peer Review and Feedback
- GENERATIVE AI (GENAI) IN ACADEMIC RESEARCH
- KEY ARTIFICIAL INTELLIGENCE TECHNIQUES EMPLOYED IN DATA ANALYSIS AND ANALYTICS
- Natural Language Processing (NLP)
- Machine Learning
- Computer Vision
- Deep Learning
- Predictive Analytics
- Reinforcement Learning
- Clustering and Classification
- Blockchain for Research Integrity
- DIVERSE AI TOOLS FOR EMPOWERING ACADEMIC RESEARCH
- NLTK (Natural Language Toolkit)
- SpaCy
- ChatGPT and GPT-3
- TensorFlow and PyTorch
- Scikit-learn
- Zotero and Mendeley
- Slack and Microsoft Teams
- Matplotlib and Seaborn
- Google Scholar
- EXPLORING THE ADVANTAGES OF AI IN ACADEMIC RESEARCH
- Data Analysis and Pattern Recognition
- Accelerated Hypothesis Generation
- Automation of Repetitive Tasks
- Predictive Modelling and Forecasting
- Enhancing Personalized Learning
- Improving Educational Outcomes
- Addressing Educational Inequality
- A COMPREHENSIVE EXAMINATION OF CHALLENGES IN INTEGRATING ARTIFICIAL INTELLIGENCE INTO ACADEMIC RESEARCH
- Data Quality and Availability
- Interpretability
- High Computational Costs
- Lack of Standardization
- Lack of Technical Expertise
- Ethical Considerations in Using AI in Academic Research
- Data Privacy
- Algorithmic Bias
- Equity and Access
- CONCLUDING REMARKS
- REFERENCES
- Future Trends and Innovations in Artificial Intelligence
- Samiya Farooq1,* and Pooja Mishra2
- INTRODUCTION
- Stages of Artificial Intelligence
- THEORETICAL BACKGROUND
- AI and Education
- Education for Understanding AI
- The Use of AI in Education
- Model Framework of Educational Landscape
- REASONS TO ADDRESS ARTIFICIAL INTELLIGENCE IN EDUCATION
- E-LEARNING TRENDS
- Google Classroom
- Collaborative Learning
- MOOCs
- Blended Learning
- Gamification
- TECHNOLOGIES WITH AI
- Chatbots
- Virtual Reality
- Learning Management System
- FUTURE TRENDS OF AI
- Personalized Learning
- Adaptive Learning Systems
- Chatbots and Virtual Assistants
- Gamification and AI
- AI in Grading and Assessment
- Predictive Analytics for Student Success
- AI AS A PROMISING TECHNOLOGY TO SUPPORT THE EDUCATIONAL PROCESS
- POLICIES FOR AI IN EDUCATION
- AI ENABLES ADAPTIVITY IN LEARNING
- INDIAN EDUCATION SYSTEM AND ARTIFICIAL INTELLIGENCE
- ARTIFICIAL INTELLIGENCE: PROMISING APPLICATIONS AND POTENTIAL EFFECTIVENESS
- Personalized Learning Opportunity
- Delivery of Quality Content
- Remote Learning
- Curriculum Upgradation
- Droupouts Management
- Assessment Grading
- Research Activities
- CONCLUDING REMARKS
- REFERENCES
- Subject Index
- Back Cover
