🎓 Teacher Guide — Multi-Lesson Activity (Lesson 1: 45 min + Homework + Lesson 2: 45 min)
1
Force-Fitting (10 min): Creative warm-up — students combine unrelated objects and brainstorm the 3 pillars of responsible consumption. Sets the frame for e-waste investigation.
2
E-Waste Lab (15 min): Groups pick an electronic item. Use AI prompt builder to research: recycle/repurpose, prolong life, declutter. Read AI responses critically.
3
AI Literacy (20 min): Groups report findings, identify values/assumptions/gaps in AI responses. Critical discourse analysis of AI-generated environmental content at C2 level.
4
Presentation Builder (Homework): Groups write a mini-presentation script for B1-B2 junior students. Use AI tools to produce a supporting video (max 3 min).
5
Peer Teaching Arena (Lesson 2, 30 min): Groups deliver AI-assisted presentations (max 7 min each). Q&A session. Audience proposes community solutions. Peer evaluation scoreboard.
6
Reflection (15 min): Metacognitive discussion: AI use, teamwork, behaviour change. Gamified quiz on e-waste facts. Homework: personal e-waste action plan.
0 of 6 stages visited
⏱ Stage 1 — Force-Fitting & Warm-Up
10:00
🔌 Force-Fitting Challenge
📋 Task: Click each pair of unrelated objects below. Your challenge: explain how they could be combined, repurposed, or used together to solve an e-waste or sustainability problem. This creative problem-solving technique forces lateral thinking at C2 level.
👩🏫 Teacher Note: Force-Fitting is a creativity technique where students must connect two unrelated concepts. Accept all responses — the goal is fluency and inventiveness, not a "right" answer. Use this to warm up the class for the e-waste investigation that follows.
Desk Lamp + Cane
How could these be combined for a sustainable purpose?
Bicycle + Telephone
How could recycling one help repurpose the other?
Door Knob + Light Bulb
What sustainable product could combine both?
Mitten + Dog Collar
How could e-waste materials be repurposed into either?
Thermometer + Whistle
What environmental monitoring device could combine both concepts?
Umbrella + Dictionary
How could old electronics be transformed into either?
💡 Brainstorm: Responsible Consumption
Discussion Questions: What are the 3 pillars of responsible consumption? How can individuals practise responsible consumption? What electronic waste causes the most problems in your community?
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Environmental
Reduce ecological impact of production and disposal
👥
Social
Fair labour, ethical sourcing, community welfare
💰
Economic
Refuse, reduce, reuse, repair, recycle
Creative Problem SolvingC2 FluencySDG 12
⏱ Stage 2 — E-Waste Investigation
15:00
🔍 Select Your E-Waste Item
📋 Task: In groups, pick one electronic item that can be — but currently is not — dealt with responsibly in your community. Click an item to select it. Then use the AI Prompt Builder below to investigate three research angles.
Disposable Vapes
~1.3 billion units discarded globally/yr
Smartphones
~5.3 billion phones become e-waste/yr
Batteries & Chargers
~15 billion batteries sold globally/yr
Cables & Cords
Average household has 6+ unused cables
TVs & Monitors
Lead, mercury, cadmium in CRT screens
Home Appliances
Fridges, washing machines — 21.7 Mt/yr
🤖 AI Prompt Builder
Task: Write precise prompts for an AI tool (ChatGPT, Gemini, Copilot) to research your selected item from three angles. Use the starter prompts below as scaffolding, then refine them. Critical prompt engineering is a C2 digital literacy skill.
Angle 1: How to Recycle / Repurpose
Angle 2: How to Prolong the Life
Angle 3: How Not to Clutter Homes
👩🏫 Teacher Note: Encourage students to use phones/tablets/PCs to actually send these prompts to an AI tool. The digital part of this stage happens in parallel with the portal — students work with real AI tools and return to the portal for the analysis stage.
⏱ Stage 3 — AI Literacy Lab
20:00
🧠 Critical Analysis of AI-Generated Content
📋 Task: Read the sample AI-generated text about smartphone e-waste below. Identify: (1) values implicit in the AI response, (2) information that might be missing, (3) assumptions the AI makes. Click the annotation chips to categorise highlighted phrases. Then answer the comprehension questions.
AI-GENERATED
Smartphones contain over 60 different elements, including rare earth elements such as neodymium, dysprosium, and terbium. When properly recycled, up to 80% of a smartphone's materials can be recovered and reused. However, planned obsolescence — the deliberate design of products with a limited useful life — means that the average smartphone is replaced every 2.5 years, well before its functional lifespan expires.
The most environmentally responsible approach is to extend the device's life through software updates, battery replacement, and protective cases. When replacement is unavoidable, certified recycling programmes such as the EU's WEEE Directive ensure that hazardous materials like lead, mercury, and cadmium are safely extracted rather than leaching into groundwater from landfill sites.
Consumers should also consider purchasing refurbished devices, which reduce demand for new mineral extraction and lower the carbon footprint of smartphone ownership by approximately 70% compared to buying new. Electronic waste is the fastest-growing waste stream globally, projected to reach 82 million metric tonnes by 2030.
The most environmentally responsible approach is to extend the device's life through software updates, battery replacement, and protective cases. When replacement is unavoidable, certified recycling programmes such as the EU's WEEE Directive ensure that hazardous materials like lead, mercury, and cadmium are safely extracted rather than leaching into groundwater from landfill sites.
Consumers should also consider purchasing refurbished devices, which reduce demand for new mineral extraction and lower the carbon footprint of smartphone ownership by approximately 70% compared to buying new. Electronic waste is the fastest-growing waste stream globally, projected to reach 82 million metric tonnes by 2030.
ANNOTATE — Click chips, then click highlighted text:
Your annotations will appear here. Example: "The text assumes consumers have access to certified recycling programmes — this is not true in many Eastern European communities."
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📝 Comprehension Check
Q1. What implicit value hierarchy does the AI text establish in its recommendation structure?
Q2. What significant perspective is absent from the AI-generated text?
Q3. Why is the 80% material recovery rate potentially misleading?
⏱ Stage 4 — Presentation Builder (Homework)
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🎬 Build Your Peer-Teaching Presentation
📋 Homework Task: Your group must prepare a mini-presentation (max 7 minutes) about your chosen e-waste item for a B1-B2 level audience (students one or two years younger). Condense your AI research into clear, accessible language. Produce a supporting mini-video (up to 3 min) using an AI video tool.
👩🏫 Teacher Note: This is the mediation component (CEFR Companion Volume, p. 104). C2 students must adapt complex information for a less proficient audience — a demanding cognitive and linguistic task. Provide AI video tool suggestions: Sora, FlexClip, Vidnoz AI, Pictory AI, Veed.
👤 Audience Profile
Your audience is B1-B2 level students (ages 13-16). They can understand the main points of clear standard input on familiar matters. You must simplify your C2-level research findings without losing accuracy or nuance. Consider: vocabulary level, sentence complexity, use of visuals, concrete examples vs. abstract data.
✅ DO
Use concrete examples and real numbers · Include visually engaging graphics · Explain technical terms · Use short, clear sentences · Relate to their daily experiences · Ask engaging questions
❌ DON'T
Use unexplained jargon · Read from notes without engaging · Present raw data without interpretation · Assume prior knowledge of EU directives · Use abstract academic register throughout
📝 Presentation Script Workspace
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✅ Presentation Quality Checklist
- Hook: Opens with a startling fact or question that engages the B1-B2 audience
- Vocabulary is adapted — technical terms are explained in accessible language
- Includes at least 3 real-world examples or data points from the AI research
- Visuals or video supplement the spoken content (not just decoration)
- Solutions are concrete and actionable for the audience's age group
- AI sources are responsibly cited — students can explain where information came from
- Presentation stays within 7-minute time limit
- Q&A responses are prepared — anticipated questions have been discussed in group
⏱ Stage 5 — Peer Teaching Arena (Lesson 2)
30:00
🎙 Presentation Timer — 7 Minutes Per Group
7:00
GROUP PRESENTATION TIME
Task: Each group presents for a maximum of 7 minutes, followed by Q&A. The B1-B2 audience is invited to ask questions and propose community-level solutions.
💡 Community Solutions Board
Audience Task: After each presentation, propose at least one solution that could be implemented in your school or community. Type your solution below.
🏆 Peer Evaluation Scoreboard — Teacher + Audience Assessment
| Group | Teamwork | Creativity | Clarity | Sources | Engagement | Total / 25 |
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⏱ Stage 6 — Reflection & Metacognition
15:00
💬 Metacognitive Reflection Prompts
Task: Click each question to reveal a model C2 response. Discuss these with your group before sharing with the class.
1. How did the use of AI tools change the quality and direction of your research compared to traditional search methods?›
AI tools provided a structured, synthesised overview of the e-waste problem that would have required considerably more time to assemble through conventional research methods. However, the synthesis came at a cost: the AI responses tended to present a consensus view that smoothed over contested areas — for instance, the tension between economic growth models and genuine waste reduction, or the geopolitical dimensions of rare earth mineral supply chains. Traditional research methods, whilst slower, would have exposed us to a wider range of perspectives, including dissenting voices and minority viewpoints that AI training data may underrepresent. The critical takeaway is that AI is a powerful starting point for research, but it should never be the endpoint — it provides a map, but the map is not the territory.
2. What was the most challenging aspect of adapting C2-level content for a B1-B2 audience, and what does this reveal about language proficiency?›
The most demanding aspect was not simplification per se — it was the task of maintaining conceptual precision whilst reducing linguistic complexity. When one simplifies "planned obsolescence constitutes a systemic barrier to sustainable consumption" into language accessible to a B1-B2 learner, there is a genuine risk of losing the structural critique embedded in the original formulation. This experience revealed something important about the relationship between language and thought: certain ideas require a particular level of linguistic sophistication to express without distortion. The CEFR Companion Volume describes C2 mediation competence as the ability to "explain the relevance of specific information in clear, fluent, well-structured language" — but this underestimates the cognitive complexity of deciding what can and cannot be simplified without loss of meaning.
3. What biases or assumptions did you identify in AI-generated environmental content, and how should users guard against them?›
The most pervasive bias was a consumer-centric framing that placed primary responsibility on individual behaviour change rather than on systemic, corporate, or legislative transformation. The AI consistently recommended actions individuals could take — buy refurbished, recycle properly, extend device life — whilst underemphasising the role of manufacturer design choices, lobbying against right-to-repair legislation, and the fundamental contradiction between tech industry growth models and environmental sustainability. This is not accidental: AI training data reflects the distribution of existing published content, which is disproportionately shaped by corporate communication strategies. Users should guard against this by deliberately seeking out counter-narratives — investigative journalism, NGO reports, academic critiques — and by asking the AI itself to present opposing viewpoints or to identify what its own response might be omitting.
4. How has this activity changed your own behaviour or intentions regarding electronic waste?›
The most significant shift was from abstract awareness to concrete accountability. Before this activity, most of us would have described ourselves as environmentally aware, but awareness without action is, in effect, a form of cognitive dissonance. The research process made the gap between knowing and doing uncomfortably visible — we discovered that our own households contained precisely the kinds of accumulated e-waste we had been investigating, and that our consumption patterns were shaped by the same planned obsolescence dynamics we were critiquing. The peer-teaching component amplified this: explaining the problem to younger students created a social commitment that is psychologically harder to retract than a private intention. Whether this translates into sustained behavioural change remains an open question — but the motivational conditions for change are now considerably stronger than they were before the activity.
🎮 Quick E-Waste Knowledge Quiz
Test your e-waste knowledge! Click the correct answer.
📌 Homework — Personal E-Waste Action Plan
Task: Write a personal e-waste action plan (300 words) that addresses: (1) an audit of electronic devices in your household — what is used, what is stored, what is broken, (2) a concrete plan for each category (repair, donate, recycle, or responsibly dispose), (3) a commitment to one behavioural change in your purchasing habits, with a specific timeline.
Register: Reflective personal essay with evidence-based reasoning. Reference at least one finding from your AI research and one external source. Maintain C2 accuracy and lexical range throughout.
Extension (optional): Investigate the e-waste legislation in your country (Lithuania, Turkey, Italy, or Spain). Compare it with the EU WEEE Directive. What enforcement gaps exist, and what would you recommend to address them?