Session Summary
Session 1: AI - The Past, Present and Future
Students embark on a journey exploring AI across time.
The lesson opens with a guessing game using AI-generated artifacts (art, music, text) . Students then investigate current AI applications through an interactive picture slideshow, identifying which everyday technologies use AI and exploring why.
The students then is view scenes from "Hulot 2.0," a humorous storyboard depicting a future home filled with helpful but sometimes confused AI systems.
Through facilitated discussion, students evaluate both benefits and challenges of AI integration.
The session balances excitement with critical thinking, helping students recognise AI's presence while developing questions about its future impact.
Students leave empowered to spot AI everywhere and curious about how it works.
Session 2: Getting Hands On - Training Your Own AI
Students transform from AI consumers to AI creators in this empowering hands-on session.
Opening with the "Intelligent Piece of Paper" activity, where a paper plays perfect noughts and crosses, students discover that following algorithms differs from true intelligence.
The core experience involves training their own AI using Google's Teachable Machine, collecting data through webcam, labelling hand gestures, watching the AI train, and testing its accuracy.
Students observe confidence meters fluctuate and deliberately confuse the AI to understand its limitations.
Critical discussion explores how training data quantity and diversity affect performance, introducing concepts of bias and fairness.
The session concludes with students recognising they possess the power to create, customise, and control AI systems for their own purposes, moving beyond passive consumption to active creation.
Session 3: The Mechanics of AI - How Does It Work?
Building on hands-on training experience, students investigate fundamental AI mechanics: data, algorithms, and predictions. The "Fleep and Bloop" classification activity demonstrates supervised learning, students observe labeled alien creatures, identify patterns, and predict new classifications, mirroring AI's learning process.
Through a drawing prediction game, students discover how partial information and prior experience enable predictions, connecting human pattern recognition to AI processes.
Google's Quick, Draw! experience is next, where students draw while AI predicts in real-time, followed by exploring the massive training dataset showing millions of diverse drawings.
Students analyse why AI succeeds or fails, understanding that AI recognises patterns rather than truly thinking.
The session transforms students into AI analysts who critically evaluate how systems work and where they might fail.
Session 4: Generative AI
Students discover AI's creative potential through systems that generate text, images, and music. The session explains Large Language Models as systems trained on vast text datasets that predict likely word sequences based on learned patterns, using the familiar "Twinkle, twinkle, little..." example to illustrate predictive learning.
Teacher-guided generative AI demonstrations let students observe how different prompts generate varied responses, helping them understand prompt-response relationships.
Google's Blob Opera provides musical creativity exploration where students create AI-assisted compositions.
The session balances appreciation for AI's impressive capabilities with critical awareness of its limitations, emphasising that generative AI makes statistical predictions without genuine understanding, emotional experience, or true creativity, positioning it as a collaborative tool rather than a replacement for human imagination.
Session 5: How to Approach AI Effectively and Ethically
This practical session develops dual competencies: technical prompting skills and ethical awareness.
Students learn that effective AI interaction requires specific, contextual prompts with appropriate detail levels and format requests. Through comparing weak prompts like "Tell me about space" with stronger alternatives requesting specific information at appropriate comprehension levels, students practice crafting effective queries.
The prompt workshop assigns groups different goals, explaining concepts, brainstorming ideas, providing instructions, or summarising information, with real-time generative AI testing demonstrating prompt quality impacts.
Ethics scenario discussions address homework integrity, personal advice appropriateness, image manipulation concerns, and information verification.
The session culminates in collaboratively creating class guidelines covering privacy protection, fact-checking, appropriate applications, and proper attribution, equipping students as informed, responsible AI users.
Session 6: AI and Social Media
Students uncover the pervasive but invisible AI systems shaping their online experiences through recommendation algorithms and content moderation.
The digital footprint activity reveals how both intentional actions and passive behaviours generate data that AI systems analyse to personalise content.
Students sort online activities into information they consciously share versus data collected without explicit awareness, discovering how these inputs fuel recommendation systems.
The session introduces filter bubbles, explaining how AI might limit perspective diversity by showing primarily familiar content. Through simplified Terms and Conditions detective work, students decode privacy permissions they typically accept unknowingly.
The Privacy Detective worksheet helps students identify concrete privacy-enhancing strategies, culminating in a collaboratively created Social Media Safety Toolkit that empowers informed consent and conscious digital footprint management.
Session 7: Creating AI Guidelines and Exploring Deepfakes
The program's culminating session synthesises learning as students develop personal AI usage frameworks addressing when AI proves helpful, when human judgment remains superior, privacy protection strategies, information verification approaches, and creative responsibility.
The deepfakes introduction reveals AI's capacity for creating convincing but fabricated content showing people saying or doing things they never actually did.
Through age-appropriate "Spot the Deepfake" challenges, students learn to identify suspicious visual and audio indicators like unnatural facial movements, lighting inconsistencies, and voice-lip synchronisation problems.
Discussions explore deepfakes' democratic implications, particularly regarding election integrity and public trust.
Students develop Media Detective Checklists emphasising multi-source verification, creator motivation consideration, and trusted adult consultation.
The session concludes with Future Ready certificates, celebrating students' transformation into knowledgeable digital citizens prepared for increasingly AI-integrated futures.
