AI in education book outline

Book Title:
AI in Education: Revolutionizing Learning, Empowering Educators, and Building Ethical Futures

Target Audience:
Educators (K-12 & Higher Ed), Administrators, EdTech Developers, Policymakers, Teacher Trainers, Researchers


Part I: Foundations of AI in Education
1. Introduction: The AI Revolution in Learning

  • Defining AI, Machine Learning (ML), & Generative AI
  • Historical Context: From PLATO to ChatGPT
  • Why Now? Convergence of Data, Compute Power, and Educational Needs
  • Scope & Vision of the Book

2. Key AI Technologies Demystified

  • Machine Learning (Supervised/Unsupervised/Reinforcement)
  • Natural Language Processing (NLP) in Education
  • Computer Vision for Learning Environments
  • Generative AI (LLMs, Image/Video Generators)
  • Adaptive & Predictive Systems

3. The Changing Educational Landscape

  • Global Challenges in Education (Equity, Teacher Shortages, Skills Gap)
  • How AI Addresses Core Educational Problems
  • Limitations: What AI Cannot Replace

Part II: AI in Teaching & Learning Practice
4. Personalized Learning & Adaptive Systems

  • AI-Powered Learning Pathways
  • Intelligent Tutoring Systems (ITS)
  • Real-Time Feedback & Scaffolding
  • Case Studies: Duolingo, Khan Academy, Carnegie Learning

5. Automating Administration & Enhancing Teaching

  • AI for Grading & Assessment (Writing, Math, Coding)
  • Lesson Planning & Resource Curation Assistants
  • Administrative Task Automation (Scheduling, Reporting)
  • Teacher PD with AI Coaches

6. Generative AI in the Classroom

  • Responsible Use of ChatGPT, Gemini, Claude
  • Prompt Engineering for Educators & Students
  • Designing AI-Augmented Assignments
  • Mitigating Plagiarism & Fostering Critical Thinking

7. Inclusive & Assistive AI

  • Accessibility Tools (Speech-to-Text, Language Translation)
  • AI for Special Education (Emotion Recognition, Custom Interventions)
  • Bridging Socioeconomic & Language Divides
  • Ethical Considerations for Equity

Part III: Systemic Transformation & Ethics
8. AI-Driven Assessment & Analytics

  • Beyond Tests: Competency-Based Evaluation
  • Predictive Analytics (Dropout Risk, Skill Gaps)
  • Learning Analytics Dashboards
  • Ethical Use of Student Data

9. Redesigning Institutional Systems

  • AI in Admissions & Enrollment Management
  • Smart Campus Infrastructure
  • Curriculum Design & Future-Proofing Skills
  • Policy Development for AI Integration

10. The Critical Ethics Debate
– Bias & Fairness in Algorithms (Case Studies)
– Data Privacy (GDPR, FERPA, COPPA Compliance)
– Transparency vs. “Black Box” Dilemmas
– Digital Divide & Access Inequality

11. Teacher & Student Agency in the AI Era
– Redefining Educator Roles: From Lecturer to Facilitator
– Developing AI Literacy for Students
– Human-Centered AI: Keeping Relationships Core
– Student Data Ownership & Digital Citizenship


Part IV: Implementation & Future Horizons
12. Building an AI-Ready Institution
– Infrastructure Requirements (Data Systems, Security)
– Change Management Strategies
– Professional Development Frameworks
– Cost-Benefit Analysis & ROI

13. Global Case Studies & Lessons Learned*
– National Strategies (Singapore, Estonia, Finland)
– School District Implementations (Successes & Failures)
– Higher Ed Innovations (Georgia Tech’s Jill Watson, MIT Open Learning)

14. Emerging Trends & Future Visions
– AI + AR/VR: Immersive Learning
– Blockchain for Credentialing
– Neuroadaptive Learning Systems
– Artificial General Intelligence (AGI) in Education: Science Fiction?

15. Conclusion: Co-Creating the Future
– Key Takeaways for Stakeholders
– Call to Action: Ethical Frameworks & Continuous Dialogue
– Envisioning Human-AI Collaborative Classrooms


Appendices:

  • A: Glossary of AI Terms
  • B: AI Tool Evaluation Checklist
  • C: Sample AI Policy Framework for Schools
  • D: Further Reading & Resources (Journals, Conferences, NGOs)

Index


Key Features Integrated Throughout:

  • “Voices from the Field” sidebars (interviews with teachers, admins, students)
  • “Ethical Dilemma” discussion prompts in each chapter
  • “Try This” practical application exercises
  • Research Spotlights (key studies summarized)
  • Future-Proofing Tips for evolving technologies


Comments

Leave a Reply

Your email address will not be published. Required fields are marked *