Hands-On Learning

Simulate real-world teaching and institutional scenarios with guided AI prompts, case studies and interactive activities.

Education-Focused Insights

Explore best practices and real examples of AI integration in pedagogy, curriculum design and student support.

Flexible, Self-Paced Learning

Work through structured, on-demand modules at your own pace, with resources tailored to your teaching and leadership goals.

About the Course

Explore how AI can transform assessment workflows by guiding you through generative design of aligned assessment formats, AI-crafted rubrics and marking guides, and automating feedforward and personalised feedback. Learn to leverage diagnostic and formative AI-driven tools to support learners in real time, while critically evaluating the validity and integrity of AI-generated assessment instruments. By the end of this module, you will be equipped to design, personalise and safeguard robust assessment and feedback processes that enhance learning outcomes and maintain academic rigour.

Meet Your Instructor

I'm Jonathan Wong, Founder of CloudPedagogy, Lead AI Educator, and research and innovation strategist with over 20 years in higher education and research, specialising in data science, AI, and advanced cloud technologies. My work focuses on applying generative AI to enhance teaching, research, and institutional operations, build intelligent AI systems, and advance ethical AI governance. In this course, I share practical frameworks, hands-on tools, and sector-specific examples developed through applied work in higher education and research. Whether you're starting your AI journey or aiming to deepen your expertise, you'll gain the skills and strategies to apply AI effectively, creatively, and responsibly in your professional context.

Curriculum

9 Chapters · 66 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

    3.1 Designing Assessments with Generative AI: Formats and Alignment

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      3.1.1 Aligning Assessments with Learning Outcomes Using Generative AI

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      3.1.2 Expanding the Range of Assessment Formats with Generative AI

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      3.1.3 Scenario-Based and Authentic Assessment Design with Generative AI

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      3.1.4 Formative Feedback and Low-Stakes Assessment Loops with Generative AI

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      3.1.5 Personalised Task Generation and Learner Choice with Generative AI

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      3.1.6 Key Takeaways

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      3.1.A Practical Use Cases

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      3.1.B Learning Activities

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      3.1.C Prompt Templates

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      3.1.D Innovative Use Cases

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      3.1.E Reflection & Discussion

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      3.1.F Productivity Tips

  3. 3

    3.2 Creating Rubrics and Marking Guides Using AI

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      3.2.1 From Learning Outcomes to Rubric Criteria: Co-Designing with AI

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      3.2.2 Designing for Inclusion, Accessibility, and Multilingual Use

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      3.2.3 Generating Performance-Level Examples: Making Rubrics Transparent

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      3.2.4 Integrating Student Voice Through AI-Supported Co-Development

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      3.2.5 Auditing Rubrics for Bias and Alignment with Regulatory Standards

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      3.2.6 Key Takeaways

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      3.2.A Practical Use Cases

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      3.2.B Learning Activities

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      3.2.C Prompt Templates

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      3.2.D Innovative Use Cases

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      3.2.E Reflection & Discussion

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      3.2.F Productivity Tips

  4. 4

    3.3 Generating Feedback: Feedforward, Personalisation, and Automation

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      3.3.1 Rethinking Feedback as Feedforward

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      3.3.2 Personalisation for Inclusion and Motivation

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      3.3.3 Streamlining and Scaling with AI Feedback Libraries

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      3.3.4 Maintaining Quality and Consistency with Feedback Audits

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      3.3.5 Feedback Across Time: Longitudinal Tracking and Growth

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      3.3.6 Key Takeaways

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      3.3.A Practical Use Cases

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      3.3.B Learning Activities

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      3.3.C Prompt Templates

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      3.3.D Innovative Use Cases

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      3.3.E Reflection & Discussion

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      3.3.F Productivity Tips

  5. 5

    3.4 Diagnostic and Formative Assessments: AI for In-Course Support

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      3.4.1 Foundations of Formative Assessment in the AI Era

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      3.4.2 AI-Driven Diagnostic Tools for Early Risk Identification

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      3.4.3 Conversational Check-ins and Chat-Based Self-Assessment

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      3.4.4 Simulated Failure and Adaptive Feedback in Safe Environments

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      3.4.5 Student Dashboards and Formative Data Visualisation

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      3.4.6 Key Takeaways

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      3.4.A Practical Use Cases

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      3.4.B Learning Activities

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      3.4.C Prompt Templates

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      3.4.D Innovative Use Cases

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      3.4.E Reflection & Discussion

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      3.4.F Productivity Tips

  6. 6

    3.5 Evaluating the Validity and Integrity of AI-Generated Assessment Tools

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      3.5.1 Understanding Validity in AI-Generated Assessments

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      3.5.2 Auditing AI-Generated Tasks: Clarity, Bias, and Accessibility

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      3.5.3 Student Involvement in Assessment Integrity

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      3.5.4 Embedding AI in Meta-Assessment and Reflection

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      3.5.5 Building Institutional Frameworks for AI Assessment Integrity

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      3.5.6 Key Takeaways

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      3.5.A Practical Use Cases

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      3.5.B Learning Activities

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      3.5.C Prompt Templates

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      3.5.D Innovative Use Cases

    11. (Included in full purchase)

      3.5.E Reflection & Discussion

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      3.5.F Productivity Tips

  7. 7

    Knowledge Check

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      Knowledge Check: Quiz

  8. 8

    Final Lesson Message

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      Course Complete: Share Your Feedback

  9. 9

    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.

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