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

Assess AI tools for teaching and learning with confidence. This course guides educators through pedagogical, ethical and technical evaluation criteria, developing practical, customised frameworks and rubrics, and conducting pilot studies with contextual feedback loops. Dive into transparency and explainability to grasp AI decision-making processes, and navigate procurement strategies, risk management and institutional fit. By the end, you will be equipped to select and implement AI solutions that align with your educational goals and safeguard learner outcomes.

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 · 65 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

    8.1 Key Evaluation Criteria: Pedagogical, Ethical, and Technical

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      8.1.1 Framing the Three-Lens Evaluation Model

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      8.1.2 Pedagogical Evaluation: Learning Value and Alignment

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      8.1.3 Ethical Evaluation: Fairness, Bias, and Inclusion

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      8.1.4 Technical Evaluation: Usability, Interoperability, and Risk

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      8.1.5 Simulating Tool Performance with AI

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      8.1.6 Key Takeaways

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      8.1.A Practical Use Cases

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      8.1.B Learning Activities

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      8.1.C Prompt Templates

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      8.1.D Innovative Use Cases

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      8.1.E Reflection & Discussion

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      8.1.F Productivity Tips

  3. 3

    8.2 Creating and Using AI Evaluation Frameworks and Rubrics

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      8.2.1 Why Evaluation Matters: Context and Challenges in Higher Education

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      8.2.2 Key Frameworks: AIDE, RRV, and PART Explained

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      8.2.3 Designing Multi-Dimensional Evaluation Rubrics

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      8.2.4 Embedding Evaluation into Academic and Operational Workflows

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      8.2.5 Using AI to Assist in Evaluation: Meta-Evaluation and Matrices

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      8.2.6 Key Takeaways

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      8.2.A Practical Use Cases

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      8.2.B Learning Activities

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      8.2.C Prompt Templates

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      8.2.D Innovative Use Cases

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      8.2.E Reflection & Discussion

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      8.2.F Productivity Tips

  4. 4

    8.3 Testing Tools in Context: Pilot Studies and Feedback Loops

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      8.3.1 Why Pilot Testing Matters in Higher Education AI Adoption

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      8.3.2 Designing Inclusive, Discipline-Aware Pilots

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      8.3.3 Implementing Sandbox Environments and No-Risk Zones

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      8.3.4 Feedback Loops – Human Insight Meets AI Analysis

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      8.3.5 From Pilot to Practice – Embedding Lessons into Strategy

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      8.3.6 Key Takeaways

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      8.3.A Practical Use Cases

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      8.3.B Learning Activities

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      8.3.C Prompt Templates

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      8.3.D Innovative Use Cases

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      8.3.E Reflection & Discussion

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      8.3.F Productivity Tips

  5. 5

    8.4 Transparency and Explainability: What Should Educators Know?

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      8.4.1 What Is Transparency in Generative AI—and Why Does It Matter?

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      8.4.2 Explainability—Making AI Understandable Across Roles

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      8.4.3 Embedding Transparency in Everyday Academic Practice

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      8.4.4 Using Visual Interfaces and Tools to Support Transparency

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      8.4.5 Fostering a Culture of Cross-Functional Transparency

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      8.4.6 Key Takeaways

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      8.4.A Practical Use Cases

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      8.4.B Learning Activities

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      8.4.C Prompt Templates

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      8.4.D Innovative Use Cases

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      8.4.E Reflection & Discussion

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      8.4.F Productivity Tips

  6. 6

    8.5 Procurement, Risk, and Institutional Fit

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      8.5.1 Strategic Alignment – Beyond Technical Fit

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      8.5.2 AI-Assisted Cost-Benefit Modelling and Forecasting

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      8.5.3 Embedding Student and Staff Voice into Procurement

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      8.5.4 Equity-Centred, Ethical Procurement Protocols

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      8.5.5 Simulating Institutional Risk with Generative AI

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      8.5.6 Key Takeaways

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      8.5.A Practical Use Cases

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      8.5.B Learning Activities

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      8.5.C Prompt Templates

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      8.5.D Innovative Use Cases

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      8.5.E Reflection & Discussion

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      8.5.F Productivity Tips

  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

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.

£97.00

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