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 and a digital education specialist with over 20 years of experience across higher education, research and digital innovation. His work focuses on turning emerging technologies into practical approaches to learning, analysis and knowledge creation. Through CloudPedagogy, he develops courses, open-source tools, interactive learning systems and applied projects exploring areas including generative and agentic AI, educational technology and digital learning design. His approach emphasises practical experimentation, transparency, critical judgement and responsible use of technology. CloudPedagogy is built around learning by understanding, building and applying: making complex ideas accessible while creating practical tools and workflows that demonstrate how they can be used in real-world contexts.

Curriculum

8 Chapters · 71 Lessons

    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

    1. 2.1.1 From rubrics to AI: tracing the evolution Free preview
    2. 2.1.2 The promise and limitations of AI-based grading Free preview
    3. 2.1.3 What AI can and cannot evaluate Free preview
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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

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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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      2.3.1 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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      2.4.1 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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      2.5.1 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

    12. (Included in full purchase)

      2.5.F Productivity Tips

    13. (Included in full purchase)

      2.5.G Prompt Library

    1. (Included in full purchase)

      Course Complete: Share Your Feedback

    1. (Included in full purchase)

      End of Course Survey

Unlock Your Genai Potential

Join our course today and unlock the full potential of Generative AI in your research projects. Empower yourself with the knowledge and skills to drive innovation and make a significant impact in your field.

£49.00

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