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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.

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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

I'm Jonathan Wong, a Lead AI Scientist and researcher specialising in medical diagnostics, data science, and advanced research technologies. My work focuses on applying generative AI and machine learning to improve healthcare outcomes and support complex research workflows. In this course, I share practical tools, ethical strategies, and real-world insights developed through applied experience in research and development. Whether you're just beginning or looking to deepen your skills, you'll find structured guidance to help you use AI effectively, creatively, and responsibly in your research practice.

Curriculum

8 Chapters · 75 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

    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 GenAI Framework

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    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

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      9.2.H Applying the GenAI Framework

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    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

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      9.3.G Prompt Library

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      9.3.H Applying the GenAI Framework

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    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

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      9.4.F Productivity Tips

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      9.4.G Prompt Library

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      9.4.H Applying the GenAI 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

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      9.5.D Innovative Use Cases

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      9.5.E Reflection & Discussion

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      9.5.F Productivity Tips

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      9.5.G Prompt Library

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      9.5.H Applying the GenAI Framework

  7. 7

    Final Lesson Message

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      Course Complete: Share Your Feedback

  8. 8

    Feedback

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      End of Course Survey

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