Hands-On Learning

Simulate real-world teaching and institutional scenarios with guided AI prompts, case studies and interactive activities.

Education-Focused Insights

Explore best practices and real examples of AI integration in pedagogy, curriculum design and student support.

Flexible, Self-Paced Learning

Work through structured, on-demand modules at your own pace, with resources tailored to your teaching and leadership goals.

About the Course

Discover the fundamentals of generative AI and its application within higher education. This course explores core concepts and capabilities, the inner workings of language models, and their training data and limitations. You’ll examine real-world use cases—from teaching innovations to administrative efficiencies—while assessing opportunities and risks such as creativity, bias and misinformation. Finally, you’ll learn prompt-design basics to craft effective inputs tailored to educational contexts, equipping you with the knowledge to integrate AI responsibly into academic practice.

Meet Your Instructor

I'm Jonathan Wong, Founder of CloudPedagogy, Lead AI Educator, and research and innovation strategist with over 20 years in higher education and research, specialising in data science, AI, and advanced cloud technologies. My work focuses on applying generative AI to enhance teaching, research, and institutional operations, build intelligent AI systems, and advance ethical AI governance. In this course, I share practical frameworks, hands-on tools, and sector-specific examples developed through applied work in higher education and research. Whether you're starting your AI journey or aiming to deepen your expertise, you'll gain the skills and strategies to apply AI effectively, creatively, and responsibly in your professional context.

Curriculum

9 Chapters · 66 Lessons

  1. 1

    Introduction

    1. Welcome to the Course Free preview
    2. How This Course Works Free preview
    3. Legal Disclaimer & Terms of Use Free preview
  2. 2

    1.1 Demystifying Generative AI: A Conceptual Overview

    1. 1.1.1 What is Generative AI? Free preview
    2. 1.1.2 Multimodal Generative AI in Practice Free preview
    3. 1.1.3 Foundation Models vs Fine-Tuned Educational Tools Free preview
    4. 1.1.4 The Generative AI Ecosystem in Higher Education Free preview
    5. 1.1.5 Everyday Interactions and Human-AI Collaboration Free preview
    6. 1.1.6 Key Takeaways Free preview
    7. 1.1.A Practical Use Cases Free preview
    8. 1.1.B Learning Activities Free preview
    9. 1.1.C Prompt Templates Free preview
    10. 1.1.D Innovative Use Cases Free preview
    11. 1.1.E Reflection & Discussion Free preview
    12. 1.1.F Productivity Tips Free preview
  3. 3

    1.2 How Generative AI Works: Language Models, Training Data, and Limitations

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      1.2.1 Foundations: How Language Models Work

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      1.2.2 Data Pipelines and the Origins of Bias

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      1.2.3 Behavioural Traits of Generative AI

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      1.2.4 Teaching with Transparency: Visualising AI Workflows

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      1.2.5 Crafting Prompts That Expose Model Limits

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      1.2.6 Key Takeaways

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      1.2.A Practical Use Cases

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      1.2.B Learning Activities

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      1.2.C Prompt Templates

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      1.2.D Innovative Use Cases

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      1.2.E Reflection & Discussion

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      1.2.F Productivity Tips

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    1.3 Use Cases in Higher Education: From Teaching to Admin

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      1.3.1 Enhancing Teaching Preparation and Learning Resource Development

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      1.3.2 Supporting Student Engagement and Personalised Learning

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      1.3.3 Enhancing Assessment Design, Feedback, and Academic Integrity

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      1.3.4 Transforming Academic Advising, Retention, and Wellbeing Support

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      1.3.5 Enabling Strategic Foresight and Institutional Planning

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      1.3.6 Key Takeaways

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      1.3.A Practical Use Cases

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      1.3.B Learning Activities

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      1.3.C Prompt Templates

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      1.3.D Innovative Use Cases

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      1.3.E Reflection & Discussion

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      1.3.F Productivity Tips

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    1.4 Opportunities and Risks: Creativity, Bias, and Misinformation

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      1.4.1 Creative Potential of Generative AI in Higher Education

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      1.4.2 Unpacking Bias in Generative AI Outputs

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      1.4.3 Misinformation and the Hallucination Problem

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      1.4.4 Navigating Ethical Grey Areas: Agency and De-Skilling

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      1.4.5 Building Critical Capacity through Student-led Risk Mitigation

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      1.4.6 Key Takeaways

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      1.4.A Practical Use Cases

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      1.4.B Learning Activities

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      1.4.C Prompt Templates

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      1.4.D Innovative Use Cases

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      1.4.E Reflection & Discussion

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      1.4.F Productivity Tips

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    1.5 Prompt Design Basics: Crafting Effective Inputs for Educational Contexts

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      1.5.1 Understanding Prompt Engineering in Higher Education Contexts

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      1.5.2 The Anatomy of an Effective Prompt

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      1.5.3 Building and Sharing Prompt Libraries

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      1.5.4 Iteration Through Peer Review and Prompt Feedback

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      1.5.5 The Prompt Maturity Model: A Framework for Progress

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      1.5.6 Key Takeaways

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      1.5.A Practical Use Cases

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      1.5.B Learning Activities

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      1.5.C Prompt Templates

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      1.5.D Innovative Use Cases

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      1.5.E Reflection & Discussion

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      1.5.F Productivity Tips

  7. 7

    Knowledge Check

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      Knowledge Check: Quiz

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    Final Lesson Message

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      Course Complete: Share Your Feedback

  9. 9

    Feedback

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      End of Course Survey

Unlock Your Genai Potential

Join our course today and unlock the full potential of Generative AI in your research projects. Empower yourself with the knowledge and skills to drive innovation and make a significant impact in your field.

£97.00

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