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, assessors, and academic leaders navigate the opportunities and challenges of AI-supported grading. From the history of automated marking to emerging GenAI practices, you’ll gain both critical insight and hands-on strategies for integrating AI responsibly into assessment systems. Whether you aim to improve marking efficiency, ensure fairness, or align with institutional policy, this course provides the tools, reflections, and frameworks needed to lead assessment innovation with confidence and integrity.

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

    2.1 The Evolution of Automated Marking in HE

    1. (Included in full purchase)

      2.1.1 From rubrics to AI: tracing the evolution

    2. (Included in full purchase)

      2.1.2 The promise and limitations of AI-based grading

    3. (Included in full purchase)

      2.1.3 What AI can and cannot evaluate

    4. (Included in full purchase)

      2.1.4 Bias and transparency in automated scoring

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      2.1.5 Human moderation in AI grading workflows

    6. (Included in full purchase)

      2.1.6 Key Takeaways

    7. (Included in full purchase)

      2.1.A Practical Use Cases

    8. (Included in full purchase)

      2.1.B Learning Activities

    9. (Included in full purchase)

      2.1.C Prompt Templates

    10. (Included in full purchase)

      2.1.D Innovative Use Cases

    11. (Included in full purchase)

      2.1.E Reflection & Discussion

    12. (Included in full purchase)

      2.1.F Productivity Tips

    13. (Included in full purchase)

      2.1.G Prompt Library

    14. (Included in full purchase)

      2.1.H Applying the AI Capability Framework

  3. 3

    2.2 Using GenAI to Support Criterion-Referenced Marking

    1. (Included in full purchase)

      2.2.1 Prompting AI to interpret marking criteria

    2. (Included in full purchase)

      2.2.2 Generating annotated feedback aligned with rubrics

    3. (Included in full purchase)

      2.2.3 Using AI to explain grade boundaries

    4. (Included in full purchase)

      2.2.4 Case study: AI-assisted essay grading

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      2.2.5 Training staff to review AI marking outputs

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      2.2.6 Key Takeaways

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      2.2.A Practical Use Cases

    8. (Included in full purchase)

      2.2.B Learning Activities

    9. (Included in full purchase)

      2.2.C Prompt Templates

    10. (Included in full purchase)

      2.2.D Innovative Use Cases

    11. (Included in full purchase)

      2.2.E Reflection & Discussion

    12. (Included in full purchase)

      2.2.F Productivity Tips

    13. (Included in full purchase)

      2.2.G Prompt Library

    14. (Included in full purchase)

      2.2.H Applying the AI Capability Framework

  4. 4

    2.3 Scaling Marking with Efficiency and Integrity

    1. (Included in full purchase)

      2.3.1 Batch-processing assessments using GenAI

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      2.3.2 Tracking and validating AI-generated grades

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      2.3.3 Audit trails and explainability requirements

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      2.3.4 Improving turnaround time while preserving rigour

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      2.3.5 Integrating AI tools with institutional gradebooks

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      2.3.6 Key Takeaways

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      2.3.A Practical Use Cases

    8. (Included in full purchase)

      2.3.B Learning Activities

    9. (Included in full purchase)

      2.3.C Prompt Templates

    10. (Included in full purchase)

      2.3.D Innovative Use Cases

    11. (Included in full purchase)

      2.3.E Reflection & Discussion

    12. (Included in full purchase)

      2.3.F Productivity Tips

    13. (Included in full purchase)

      2.3.G Prompt Library

    14. (Included in full purchase)

      2.3.H Applying the AI Capability Framework

  5. 5

    2.4 Embedding Student Trust in AI-Based Grading

    1. (Included in full purchase)

      2.4.1 Transparency and student communication strategies

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      2.4.2 Student-facing explanations of AI-assisted grades

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      2.4.3 Opportunities for self-review and appeals

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      2.4.4 Designing fair workflows with student agency

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      2.4.5 Balancing algorithmic clarity with pedagogical care

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      2.4.6 Key Takeaways

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      2.4.A Practical Use Cases

    8. (Included in full purchase)

      2.4.B Learning Activities

    9. (Included in full purchase)

      2.4.C Prompt Templates

    10. (Included in full purchase)

      2.4.D Innovative Use Cases

    11. (Included in full purchase)

      2.4.E Reflection & Discussion

    12. (Included in full purchase)

      2.4.F Productivity Tips

    13. (Included in full purchase)

      2.4.G Prompt Library

    14. (Included in full purchase)

      2.4.H Applying the AI Capability Framework

  6. 6

    2.5 Policy, Oversight, and Future Pathways

    1. (Included in full purchase)

      2.5.1 Establishing governance for AI-driven marking

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      2.5.2 Aligning with academic board and QA bodies

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      2.5.3 Building interdisciplinary review panels

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      2.5.4 Roadmaps for ethical adoption and scaling

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      2.5.5 Forecasting the future of grading with AI

    6. (Included in full purchase)

      2.5.6 Key Takeaways

    7. (Included in full purchase)

      2.5.A Practical Use Cases

    8. (Included in full purchase)

      2.5.B Learning Activities

    9. (Included in full purchase)

      2.5.C Prompt Templates

    10. (Included in full purchase)

      2.5.D Innovative Use Cases

    11. (Included in full purchase)

      2.5.E Reflection & Discussion

    12. (Included in full purchase)

      2.5.F Productivity Tips

    13. (Included in full purchase)

      2.5.G Prompt Library

    14. (Included in full purchase)

      2.5.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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