Hands-On Learning

Gain practical experience through hands-on projects and real-world applications.

Expert Guidance

Learn from industry experts with years of experience in Generative AI research.

Flexible Schedule

Study at your own pace with our online platform, allowing you to balance work and learning effectively.

About the Course

This course is designed to help educators, assessment designers, and academic leaders critically evaluate AI-generated assessments. From foundations of validity and reliability to institutional QA processes and continuous feedback loops, you will gain the knowledge and skills needed to ensure quality, fairness, and integrity in AI-supported assessment systems. Whether you are experimenting with AI in assessment design or seeking to embed it within institutional practice, this course offers structured insights, practical strategies, and ethical guidance to support responsible adoption.

Meet Your Instructor

Jonathan Wong is the founder of CloudPedagogy, where he develops ethical, practical, and future-ready learning systems for Generative, Agentic, and emerging Quantum AI. Drawing on over 20 years of experience in higher education, research, and digital innovation, he specialises in applying AI to real-world contexts — from curriculum design and assessment reform to research acceleration and institutional strategy. His frameworks and courses help educators, researchers, and professionals build the confidence, capability, and critical insight needed to thrive across the evolving Continuum of Intelligence.

Curriculum

8 Chapters · 76 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
    4. (Included in full purchase)

      Download: Universal AI Practice Workbook

  2. 2

    8.1 Foundations of Assessment Validity and Reliability

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      8.1.1 Understanding validity in the HE assessment context

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      8.1.2 Types of reliability and their application

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      8.1.3 Threats to validity in AI-generated assessments

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      8.1.4 Benchmarking and cross-checking AI outputs

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      8.1.5 Aligning AI use with academic quality standards

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      8.1.6 Key Takeaways

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      8.1.A Practical Use Cases

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      8.1.B Learning Activities

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      8.1.C Prompt Templates

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      8.1.D Innovative Use Cases

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      8.1.E Reflection & Discussion

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      8.1.F Productivity Tips

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      8.1.G Prompt Library

    14. (Included in full purchase)

      8.1.H Applying the AI Capability Framework

  3. 3

    8.2 Evaluating AI Output for Assessment Design

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      8.2.1 Rubric-based evaluation of AI-generated assessments

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      8.2.2 Human review and moderation workflows

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      8.2.3 Using AI to self-assess and revise its own outputs

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      8.2.4 Triangulation with subject matter experts

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      8.2.5 Common pitfalls in AI-designed questions

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      8.2.6 Key Takeaways

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      8.2.A Practical Use Cases

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      8.2.B Learning Activities

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      8.2.C Prompt Templates

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      8.2.D Innovative Use Cases

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      8.2.E Reflection & Discussion

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      8.2.F Productivity Tips

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      8.2.G Prompt Library

    14. (Included in full purchase)

      8.2.H Applying the AI Capability Framework

  4. 4

    8.3 Bias, Fairness, and Inclusivity in AI Assessments

    1. (Included in full purchase)

      8.3.1 Detecting and mitigating bias in AI-generated content

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      8.3.2 Representation and equity in question design

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      8.3.3 Language sensitivity and accessibility

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      8.3.4 Evaluating impact on underrepresented groups

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      8.3.5 Audit processes for inclusive assessment design

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      8.3.6 Key Takeaways

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      8.3.A Practical Use Cases

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      8.3.B Learning Activities

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      8.3.C Prompt Templates

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      8.3.D Innovative Use Cases

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      8.3.E Reflection & Discussion

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      8.3.F Productivity Tips

    13. (Included in full purchase)

      8.3.G Prompt Library

    14. (Included in full purchase)

      8.3.H Applying the AI Capability Framework

  5. 5

    8.4 Institutional QA and Approval Pathways

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      8.4.1 Integrating GenAI into QA frameworks

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      8.4.2 Mapping AI workflows to academic approval stages

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      8.4.3 Data handling and compliance issues

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      8.4.4 Collaborative validation panels

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      8.4.5 Documenting transparency and decision-making

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      8.4.6 Key Takeaways

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      8.4.A Practical Use Cases

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      8.4.B Learning Activities

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      8.4.C Prompt Templates

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      8.4.D Innovative Use Cases

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      8.4.E Reflection & Discussion

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      8.4.F Productivity Tips

    13. (Included in full purchase)

      8.4.G Prompt Library

    14. (Included in full purchase)

      8.4.H Applying the AI Capability Framework

  6. 6

    8.5 Continuous Improvement through Feedback Loops

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      8.5.1 Using student input to refine AI-generated tasks

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      8.5.2 Staff review and revision cycles

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      8.5.3 Monitoring AI drift over time

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      8.5.4 Institutional reporting and improvement dashboards

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      8.5.5 Building adaptive and responsive QA practices

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      8.5.6 Key Takeaways

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      8.5.A Practical Use Cases

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      8.5.B Learning Activities

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      8.5.C Prompt Templates

    10. (Included in full purchase)

      8.5.D Innovative Use Cases

    11. (Included in full purchase)

      8.5.E Reflection & Discussion

    12. (Included in full purchase)

      8.5.F Productivity Tips

    13. (Included in full purchase)

      8.5.G Prompt Library

    14. (Included in full purchase)

      8.5.H Applying the AI Capability Framework

  7. 7

    Final Lesson Message

    1. (Included in full purchase)

      Course Complete: Share Your Feedback

  8. 8

    Feedback

    1. (Included in full purchase)

      End of Course Survey

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