What you'll learn:

• What is AI? Definitions, key terms, and emerging capabilities

• How AI is shaping our everyday tools and workflows

• Overview and comparison of leading chatbot tools including

• ChatGPT, Microsoft Copilot, Claude, Google Gemini, Perplexity, Notion AI, Pi, Hume, Poe, Google NotebookLM

• Strengths, weaknesses, and real-world use cases of each

• Prompting basics: How to talk to a chatbot effectively

• Practical activity: Create your own AI-generated image

• Guided exploration: Trying different chatbots during the session

• Where is AI going in 2025? Key trends and what to watch for
  
  

By the end of this session you will:

• Define generative AI and how it can be used by individuals and businesses in practical, everyday ways

• Explore the major free-to-access AI chatbot tools and how they differ in functionality

• Practice using a range of chatbot tools through guided, hands-on activites

• Explore the types of tasks AI tools can perform and where they can add value

• Explain how AI is being embedded into common tools and platforms

• Gain insight into where AI is headed in 2025 and how to stay ahead of the curve

What you'll learn:

• What is Prompting? Understanding the new language of AI tools

• Comparing chatbots: ChatGPT, Claude, Copilot, Gemini, Perplexity

• Setting up and customizing ChatGPT for better prompting

• Single-shot prompting: When less is not more

• Multi-shot prompting: Building conversations and context

• Multi-modal prompting: Using image and voice inputs

• Prompting for document analysis and summarization

• Voice prompting: Dictate to AI and capture ideas on the move

• Prompting with datasets using tools like NotebookLM

• Prompting frameworks and practical strategies for effective results

  
  
  

By the end of this session you will:

• Explain what prompting is and how it differs across platforms

• Define the difference between single-shot and multi-shot prompting

• Practice adapting and refine your prompts to get better results

• Gain hands-on experience using text, images, and voice inputs

• Practice how to prompt against uploaded documents and datasets

• Explore how different AI tools interpret and respond to the same prompt

What you'll learn:

• Ethical AI use in academic and professional research

• How to cite AI-generated content and declare AI tool usage

• Exploring new research features in Claude, NotebookLM, ChatGPT Projects, and Perplexity Spaces

• Academic research tools: Google Scholar, ResearchRabbit, Elicit, Consensus, Scite, Ai2 PaperFinder

• Deep research using Chain of Thought: Gemini, Claude, ChatGPT

• Voice interfaces and podcast creation: ChatGPT Voice, NotebookLM, ElevenLabs, PlayAI

• Testing AI-generated artefacts and auto-publishing platforms

• Using agents and linking AI tools across datasets and tasks

• What’s coming in 2025: Project Digits, agent ecosystems, voice synthesis, and more
  
  

By the end of this session you will:

• Explain how to use AI tools to support both academic and professional research tasks

• Upload and query documents, reports, and web content to extract useful insights

• Explain how to stay ethical when using AI—especially in sensitive or academic settings

• Identify which tools are safe for sensitive content and how to use them securely

• Generate citations, summaries, and structured outputs to support business or academic goals

• Explore the emerging role of AI agents, multimodal inputs, and automation in research workflows

What you'll learn:

• Key questions educators need to consider about AI and assessment

• Overview of formative, summative, and diagnostic assessments— and how AI can support each

• Using AI to design, critique, and improve assignment briefs

• Leon Furze’s AI Assessment Scale and other frameworks

• Tools for formative feedback, including MagicSchool, POE.AI, SchoolAI, and ChatGPT

• Ethical concerns: bias, data privacy, and over-reliance

• Opportunities and risks in using AI for real-time analytics and adaptive testing

• Creative applications: using AI to personalise learning paths and deepen student engagement
  
  

By the end of this session you will:

• Explain how AI can support diagnostic, formative, and summative assessment

• Explore ethical concerns and reliability issues when integrating AI into grading or feedback

• Practice the use of AI to generate rubrics, critique student work, and design adaptive assessment tasks

• Examine tools that offer real-time feedback, personalized insights, and student performance analytics

• Explore ideas for reworking assessments to embrace AI rather than fight against it

• Leave with inspiration and confidence to experiment with AI-supported assessment in your own teaching context

What you'll learn:

• The educator’s AI-powered course design framework

• Foundations: Backward Design, UDL, Constructive Alignment, and Bloom’s Taxonomy

• Identifying your audience and learning outcomes

• Conducting research with AI: tools like Elicit, Consensus, ResearchRabbit, and NotebookLM

• Scaffolding knowledge and breaking topics into manageable learning blocks

• Structuring a course with 'Hook–Why–How–What–When' design

• Creating multimodal course materials: Notion, Gamma, Napkin, Claude, YouTube, Perplexity Pages

• Using AI to build session structures with intro, key knowledge, activity, reflection, and synthesis

• Designing assessed components, creating rubrics, and aligning with standards using AI

• Practical strategies to integrate synchronous and asynchronous learning formats
  
  

By the end of this session you will:

• Practice the use of multiple AI tools to research and assemble materials for a teaching session

• Explain that importance of checking AI generated material for accuracy & editing material before general use

• Build more confidence in using AI tools to inform and add to the pedagogical outcomes they are seeking to achieve