Section outline

• Select section Generative AI for Staff in Higher Education

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  Generative AI for Staff in Higher Education

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  Course Format

  • Duration: Approx. 2–3 hours
  • Delivery: Self-paced online
  • Materials: Videos, readings, interactive tools
  • Assessment: Prompt practice, ethical reflection, application task

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  Core Resources

  UNESCO — Guidance for Generative AI in Education & Research.
  OECD — AI Principles & AI Policy Observatory.
  IEEE — Ethically Aligned Design.
  ISO/IEC — 23894 (AI risk management), 42001 (AI management systems).
  UDL (CAST) and WCAG 2.2 for accessibility.

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  Accessibility, Integrity & Privacy Guard-rails (apply across all modules)

  Accessibility (UDL/WCAG 2.2): Provide text alternatives for media; offer plain‑language versions; ensure colour contrast; caption videos; provide transcripts; allow alternative submission formats.
  Academic integrity: Prefer process‑evidence and authentic tasks; avoid AI‑detector tools as determinative evidence; use vivas/portfolios where appropriate.
  Privacy & data: Do not upload confidential or sensitive data; anonymise where feasible; follow institutional policies; disclose AI use transparently.
• Select section Module 1: What is AI?

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  Module 1: What is AI?

  Learning Objectives

  Understand the definition and scope of AI.

  Identify key categories: Machine Learning (ML), Deep Learning (DL), Generative AI (GenAI) and Large Language Models (LLMs).

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      Content

      • Select activity Videos

        Video 1: Generative AI Explained in 5 Minutes | What Is GenAI?

        • Duration: 5:02
        • Description: A concise introduction to generative AI, explaining what it is, how it works, and its key applications across industries, including education. Perfect for learners who need a foundational understanding before diving deeper into its implications for teaching and learning.

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        Video 2: Generative AI in Education: The Future of Teaching and Learning

        • Duration: 3:13
        • Description: This video provides a quick overview of how generative AI is reshaping education. It highlights practical benefits such as personalized learning experiences, reducing administrative burdens for educators, and enhancing teaching roles rather than replacing them. Ideal for sparking discussion on the positive potential of AI in higher education.

      • Select activity An Overview of AI and Its Evolution

        

        An Overview of AI and Its Evolution Page

        Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think, learn, and make decisions. AI systems can perform tasks such as problem-solving, pattern recognition, and language understanding—often with speed and scale beyond human capability.

      • Select activity AI Categories and Examples

        AI Categories and Examples

        Reactive Machines Basic AI systems that respond to inputs (e.g., chess-playing programs).		Limited Memory AI that learns from historical data (e.g., self-driving cars).
        Theory of Mind Future AI aiming to understand emotions and intentions.		Self-aware Hypothetical AI with consciousness.

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      Activities

      • Select activity AI Terminology Quiz

        

        AI Terminology Quiz
      • Select activity Concept Mapping Exercise

        

        Concept Mapping Exercise Page
• Select section Module 2: Exploring GenAI and LLMs

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  Module 2: Exploring GenAI and LLMs

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      Learning Objectives

      Understand how Generative AI and LLMs work.

      Explore popular tools: ChatGPT, Copilot, Claude, Gemini.
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      Content

      Tutorial: How LLMs Generate Text.

      Reading: Use cases in Higher Education.

      Examples: Chatbot vs. Assistant vs. Agent.

      • Select activity Tutorial: How LLMs Generate Text

        

        Tutorial: How LLMs Generate Text H5P
      • Select activity Reading: Use Cases in Higher Education

        

        Reading: Use Cases in Higher Education H5P
      • Select activity Examples: Chatbot vs. Assistant vs. Agent

        

        Examples: Chatbot vs. Assistant vs. Agent H5P
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      Activities

      • Select activity Activity: Tool Exploration - Try 2–3 GenAI platforms.

        

        Activity: Tool Exploration - Try 2–3 GenAI platforms. H5P
      • Select activity Activity: Prompt Crafting Workshop

        

        Activity: Prompt Crafting Workshop Page
• Select section Module 3: AI in HE Practice

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  Module 3: AI in HE Practice

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      Learning Objectives

      Apply AI tools in teaching, learning, and admin tasks.

      Design AI-enhanced activities and workflows.
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      Content

      • Select activity Reading: Pedagogical Applications of AI

        

        Reading: Pedagogical Applications of AI H5P
      • Select activity Video: Teacher to Teacher: Is AI reshaping education for the better?

        

        Video: Teacher to Teacher: Is AI reshaping education for the better? H5P
      • Select activity Template Examples: Lesson planning, Feedback, Admin Automation

        

        Template Examples: Lesson planning, Feedback, Admin Automation Page
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      Activities

      AI-Enhanced Lesson Builder.

      Feedback Simulation.

      Admin Task Planner.

      • Select activity Activities

        

        Activities H5P
• Select section Module 4: Ethical and Environmental Considerations

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  Module 4: Ethical and Environmental Considerations

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      Learning Objectives

      Recognise ethical risks: bias, misinformation, privacy.

      Understand the environmental impact of AI.
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      Content

      Video: "Ethics and Sustainability in AI"

      Reading: Responsible AI use in education.

      Case Studies: Ethical dilemmas and solutions.

      • Select activity Reading: Responsible AI Use in Education

        Reading: Responsible AI Use in Education

        • AI introduces ethical challenges such as bias, misinformation, and privacy concerns. Educators must ensure transparency, fairness, and accountability when integrating AI tools.
        • Key principles:
          • Bias Mitigation: AI models can perpetuate stereotypes. Validate outputs and diversify training data sources.
          • Privacy Protection: Avoid sharing sensitive student data with third-party tools.
          • Academic Integrity: Establish clear guidelines for AI-assisted work.
          • Environmental Impact: Large AI models consume significant energy. Opt for efficient tools and limit unnecessary queries.
        • Frameworks like UNESCO’s AI Ethics Guidelines and institutional policies should guide practice.
      • Select activity Video: AI Ethics 101: What Educators and Students ...

        Video: AI Ethics 101: What Educators and Students Should Know

        Duration: 5:53
        License: Creative Commons
        Overview: Covers fairness, transparency, accountability, privacy, and responsible AI use in education. Includes practical questions for reflection.

      • Select activity Case Studies

        Case Studies:

        • Example 1: AI-generated feedback introducing bias.

        • Example 2: Carbon footprint of large-scale AI deployments.

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      Activities

      Bias Spotting Challenge: Review AI-generated text for biased language.

      Privacy Policy Review: Compare two AI tool policies and identify risks.

      Carbon Footprint Estimator: Calculate energy use for AI queries and discuss sustainability strategies.
• Select section Module 5: Risks, Limitations, and Critical Use

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  Module 5: Risks, Limitations, and Critical Use

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      Learning Objectives

      Identify limitations of GenAI and LLMs.

      Develop critical thinking when using AI outputs.
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      Content

      Reading: Common errors and hallucinations.

      Video: "Why AI Gets It Wrong"

      Checklist: Evaluating AI-generated content.