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

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

    2.1 The Evolution of Automated Marking in HE

    1. (Included in full purchase)

      2.1.1 From rubrics to AI: tracing the evolution

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      2.1.2 The promise and limitations of AI-based grading

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      2.1.3 What AI can and cannot evaluate

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      2.1.4 Bias and transparency in automated scoring

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      2.1.5 Human moderation in AI grading workflows

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      2.1.6 Key Takeaways

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      2.1.A Practical Use Cases

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      2.1.B Learning Activities

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      2.1.C Prompt Templates

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      2.1.D Innovative Use Cases

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      2.1.E Reflection & Discussion

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      2.1.F Productivity Tips

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      2.1.G Prompt Library

    14. (Included in full purchase)

      2.1.H Applying the GenAI Framework

  3. 3

    2.2 Using GenAI to Support Criterion-Referenced Marking

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      2.2.1 Prompting AI to interpret marking criteria

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      2.2.2 Generating annotated feedback aligned with rubrics

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      2.2.3 Using AI to explain grade boundaries

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      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

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      2.2.B Learning Activities

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      2.2.C Prompt Templates

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      2.2.D Innovative Use Cases

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      2.2.E Reflection & Discussion

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      2.2.F Productivity Tips

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      2.2.G Prompt Library

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      2.2.H Applying the GenAI Framework

  4. 4

    2.3 Scaling Marking with Efficiency and Integrity

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      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

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      2.3.B Learning Activities

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      2.3.C Prompt Templates

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      2.3.D Innovative Use Cases

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      2.3.E Reflection & Discussion

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      2.3.F Productivity Tips

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      2.3.G Prompt Library

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      2.3.H Applying the GenAI Framework

  5. 5

    2.4 Embedding Student Trust in AI-Based Grading

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      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

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      2.4.B Learning Activities

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      2.4.C Prompt Templates

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      2.4.D Innovative Use Cases

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      2.4.E Reflection & Discussion

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      2.4.F Productivity Tips

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      2.4.G Prompt Library

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      2.4.H Applying the GenAI Framework

  6. 6

    2.5 Policy, Oversight, and Future Pathways

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      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

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      2.5.6 Key Takeaways

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      2.5.A Practical Use Cases

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      2.5.B Learning Activities

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      2.5.C Prompt Templates

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      2.5.D Innovative Use Cases

    11. (Included in full purchase)

      2.5.E Reflection & Discussion

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      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 GenAI Framework

  7. 7

    Final Lesson Message

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      Course Complete: Share Your Feedback

  8. 8

    Feedback

    1. (Included in full purchase)

      End of Course Survey

Unlock Your Genai Potential

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