Hands-On Learning

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

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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

Our AI-Supported Moderation and Assessment Calibration course is designed to help educators and academic leaders integrate AI responsibly into assessment quality processes. Whether you are new to AI in assessment or seeking to refine your moderation strategies, this course provides a structured curriculum covering principles, workflows, collaboration, fairness, and institutional QA. Through practical examples and ethical guidance, you’ll gain the capability to apply AI critically and creatively in moderation, ensuring your assessments remain fair, transparent, and future-ready.

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

    9.1 Principles of Assessment Moderation in HE

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      9.1.1 The role of moderation in academic standards

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      9.1.2 Common moderation models and practices

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      9.1.3 Challenges in large-scale and remote settings

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      9.1.4 Maintaining consistency across disciplines

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      9.1.5 Introducing AI into moderation workflows

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      9.1.6 Key Takeaways

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      9.1.A Practical Use Cases

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      9.1.B Learning Activities

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      9.1.C Prompt Templates

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      9.1.D Innovative Use Cases

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      9.1.E Reflection & Discussion

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      9.1.F Productivity Tips

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      9.1.G Prompt Library

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      9.1.H Applying the AI Capability Framework

  3. 3

    9.2 Designing AI-Enhanced Moderation Workflows

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      9.2.1 Generating comparative samples with AI

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      9.2.2 Clustering responses for review

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      9.2.3 Flagging anomalies and inconsistencies

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      9.2.4 Training AI models on moderation criteria

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      9.2.5 Monitoring algorithmic suggestions

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      9.2.6 Key Takeaways

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      9.2.A Practical Use Cases

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      9.2.B Learning Activities

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      9.2.C Prompt Templates

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      9.2.D Innovative Use Cases

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      9.2.E Reflection & Discussion

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      9.2.F Productivity Tips

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      9.2.G Prompt Library

    14. (Included in full purchase)

      9.2.H Applying the AI Capability Framework

  4. 4

    9.3 Collaborative and Distributed Moderation Practices

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      9.3.1 Supporting distributed teams with shared AI tools

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      9.3.2 Digital annotation and discussion of scripts

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      9.3.3 Building shared understanding through AI prompts

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      9.3.4 Integrating moderator feedback across locations

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      9.3.5 Consensus building with AI-supported analytics

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      9.3.6 Key Takeaways

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      9.3.A Practical Use Cases

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      9.3.B Learning Activities

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      9.3.C Prompt Templates

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      9.3.D Innovative Use Cases

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      9.3.E Reflection & Discussion

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      9.3.F Productivity Tips

    13. (Included in full purchase)

      9.3.G Prompt Library

    14. (Included in full purchase)

      9.3.H Applying the AI Capability Framework

  5. 5

    9.4 Ensuring Fairness and Objectivity in Moderation

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      9.4.1 Reducing unconscious bias in marking and review

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      9.4.2 Comparing human and AI interpretations

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      9.4.3 Triangulating moderator decisions

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      9.4.4 Auditing fairness in high-stakes assessments

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      9.4.5 Mitigating over-reliance on AI judgements

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      9.4.6 Key Takeaways

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      9.4.A Practical Use Cases

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      9.4.B Learning Activities

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      9.4.C Prompt Templates

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      9.4.D Innovative Use Cases

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      9.4.E Reflection & Discussion

    12. (Included in full purchase)

      9.4.F Productivity Tips

    13. (Included in full purchase)

      9.4.G Prompt Library

    14. (Included in full purchase)

      9.4.H Applying the AI Capability Framework

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    9.5 Embedding Moderation in Institutional QA

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      9.5.1 QA policies and AI in moderation processes

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      9.5.2 Moderation reporting and documentation

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      9.5.3 Scaling with institutional systems

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      9.5.4 Feedback loops from moderation to design

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      9.5.5 Evolving moderator roles in the AI age

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      9.5.6 Key Takeaways

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      9.5.A Practical Use Cases

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      9.5.B Learning Activities

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      9.5.C Prompt Templates

    10. (Included in full purchase)

      9.5.D Innovative Use Cases

    11. (Included in full purchase)

      9.5.E Reflection & Discussion

    12. (Included in full purchase)

      9.5.F Productivity Tips

    13. (Included in full purchase)

      9.5.G Prompt Library

    14. (Included in full purchase)

      9.5.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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