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AI+ L&D™ Self-Paced Learning
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Introduction
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Module 1: Introduction to Artificial Intelligence (AI) in Education
Introduction: Introduction to Artificial Intelligence (AI) in Education
Definition and Types of AI
History of AI And Current Trends in AI
Personalized Learning And Automated Administration
Content Delivery and Adaptive Learning
Content Customization and Generation
Language Models in Education
Augmented and Virtual Reality
Automated Grading Systems
Predictive Analytics in Learner Performance
Feedback for Educators
Privacy and Data Security
Bias and Fairness And Future of AI in Education
Summary: Introduction to Artificial Intelligence (AI) in Education
Module 1: E-Book
Quiz
Module 2: Machine Learning Fundamentals
Machine Learning Fundamentals
Definition and Core Concepts
Types of Machine Learning
Applications in L&D
Supervised Learning ( Algorithm Fundamentals ,Training and Testing Models ,Use Cases in L&D )
Unsupervised Learning (Algorithm Fundamentals ,Finding Patterns in Data, Use Cases in L&D )
Basic Principles
Reinforcement Learning (Basic Principles ,Algorithm Examples ,Applications of Reinforcement Learning )
Machine Learning in Practice (Data Preparation and Cleaning,Evaluating Model Performance ,Ethical Considerations and Bias Mitigation )
Summary: Machine Learning Fundamentals
Module 2: E-Book
Quiz
Module 3: Natural Language Processing (NLP) For Educational Content
Natural Language Processing (NLP) For Educational Content
Introduction to NLP
Key NLP Technologies
Language Models
Text Analysis for Learning Materials
Readability and Complexity Assessment
Semantic Content Enrichment
Learner Profiling and Needs Analysis
Adaptive Content Generation
Dialogue Systems and Chatbots for Learning
Automated Essay Scoring
Sentiment Analysis for Feedback Interpretation
Predictive Analytics for Performance Monitoring
Summary Natural Language Processing (NLP) For Educational Content
Module 3: E-Book
Quiz
Module 4: AI-Driven Content Creation and Curation
Introduction - AI-Driven Content Creation and Curation
Automated Content Generation
Customization and Localization
Personalized Learning Paths
Integration with Learning Management Systems (LMS)
Automating Question Creation
Instant Feedback Mechanisms
Content Aggregation and Filtering
Continuous Content Update
Maintaining Accuracy and Reliability
Intellectual Property and Copyright Issues
Summary - AI-Driven Content Creation and Curation
Module 4: E-Book
Quiz
Module 5: Adaptive Learning Systems
Module Intro-Adaptive Learning Systems
Principles of Adaptive Learning
Technologies Behind Adaptive Learning And Benefits and Challenges
Learner Modelling
Content Modelling And Adaptivity and Personalization Mechanisms
Integration with Existing Systems
Scalability and Accessibility And Continuous Improvement and Feedback Loops
Dynamic Assessment Methods
Feedback and Support And Measuring Effectiveness
Data Privacy and Security
Bias and Fairness in AI And Informed Consent and Transparency
Summary - Adaptive Learning Systems
Module 5: E-Book
Quiz
Module 6: Ethics and Bias in AI for L&D
Introduction:Ethics and Bias in AI for L&D
Fundamentals of AI Ethics
Ethical Design and Development
Data Privacy Principles
Consent and Data Control
Impact of Bias on L&D Outcomes
Strategies for Mitigating Bias,Continuous Monitoring and Evaluation
Engaging Learners Ethically
Transparency with AI Tools ,Learner Autonomy and AI
Evolving Ethical Standards
Innovations in Ethical AI,Preparing for an AI-Ethical Future in L&D
Summary - Ethics and Bias in AI for L&D
Module 6: E-Book
Quiz
Module 7: Emerging Technologies and Future Trends
Introduction - Emerging Technologies and Future Trends
AR for Interactive Learning
Practical Applications of AR
Development Tools and Challenges
VR for Immersive Experiences
Curriculum Integration
Hardware and Software Considerations
Adaptive Learning Platforms
Predictive Analytics in Education
Secure Learning Records
Smart Contracts for Education
Natural Language Processing (NLP) Enhancements
Generative AI for Content Creation
Summary - Emerging Technologies and Future Trends
Module 7: E-Book
Quiz
Module 8: Implementation and Best Practices
Implementation and Best Practices
Needs Assessment
Technology Alignment and Stakeholder Engagement
Evaluating AI Solutions
Cost-Benefit Analysis and Vendor Selection and Partnerships
Pilot Programs
Training and Support,Integration with Existing Systems
Performance Metrics
Continuous Feedback Loops
Adaptive Learning and Iteration
Data Privacy and Security
Ethical AI Practices
Regulatory Compliance
Summary - Implementation and Best Practices
Module 8: E-Book
Quiz
Resources
AI+ L&D Tools
AI+ L&D Blueprint
AI+ L&D Detailed Curriculum
AI+ L&D Resources and References
Feedback Survey Form
Survey
System Compatibility test
System Compatibility Test
AI CERTs Exam Guidelines
AI+ L&D Examination
AI+ L&D Examination
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AI+ L&D™ Self-Paced Learning
