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Class 6 students can learn what AI does without writing code. Start with familiar examples, trace how information becomes a prediction or recommendation, then discuss who may be affected and how a decision could be made fairer. UNESCO’s middle-school guidance includes online and offline activities, so a teacher can adapt the lesson to available devices, time, and students’ prior knowledge.
What Class 6 students should understand about AI
For an introductory lesson, explain AI as technology that uses data to make predictions or decisions. Students do not need to build a model to explore the basic relationship: information goes in, a system processes it, and a prediction or recommendation comes out. UNESCO’s middle-school curriculum describes AI in terms of a dataset, learning, and prediction, and introduces algorithms through inputs, changes to inputs, and outputs. UNESCO’s middle-school curriculum guidance provides the curriculum context.
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AI literacy is broader than technical vocabulary. UNESCO’s student competency framework connects a human-centred mindset and AI ethics with AI techniques and applications, as well as AI system design. It groups 12 competencies across those four dimensions and describes progression levels of understand, apply, and create. These are framework categories, not evidence of learning gains or a fixed Class 6 syllabus. The student framework and curriculum materials are intended as references that educators can adapt.
A no-code lesson sequence
1. Begin with examples students recognize
Ask students where they have seen a system that seems to make a suggestion or prediction. Possible examples include route suggestions, autocorrect, chatbots, and voice assistants. For each, ask: “What does this system appear to decide?” Use examples from students’ daily lives and local context rather than assuming every learner uses the same apps or devices. UNESCO IITE recommends starting from familiar applications and offers unplugged ideas for teaching ICT and AI fundamentals.
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2. Trace information to a prediction
Choose one example and ask the class to map three parts: the information that goes in, the data the system may have learned from, and the output it gives. For a route suggestion, students might consider a starting point and destination as inputs, past or current route information as relevant data, and a recommended route as the output. Present this as a simplified classroom model; the exact information used depends on the system.
Keep the distinction clear: the system’s output is a prediction or recommendation, not automatically a fact or a decision that must be followed. Ask students what information might be missing and whether another person would make a different choice.
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3. Run AI Bingo or a board-based version
UNESCO describes AI Bingo, where pairs identify familiar AI applications and work out the dataset and prediction involved. Prepare a few examples on paper or write them on the board; students can discuss what each system might take in and produce without using a device. The learning task is the explanation, not proving that every example uses AI in exactly the same way. If students disagree, ask what evidence would help them decide.
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4. Discuss fairness and consequences
Use UNESCO IITE’s example of an algorithm recommending scholarship recipients. Ask students what information might shape the recommendation, who could be disadvantaged by biased calculations, and what a human reviewer should check. Invite them to consider who benefits, who may be left out, and whether the data or decision process treats groups differently.
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Keep the discussion grounded in the decision rather than asking students to label a system simply “good” or “bad.” A useful follow-up is: “What would you want to know before trusting this recommendation?” The unplugged guidance explicitly uses scholarship decisions to introduce algorithmic bias.
5. Propose an improvement
Have students suggest one change: include another relevant perspective, review the information used, add a safeguard, or keep a person involved before a consequential decision is final. They can draw or describe a revised process on paper. UNESCO’s middle-school curriculum example moves from investigating perspectives, benefits, and risks toward proposing a solution prototype; a paper sketch is a practical offline adaptation, not a separately tested intervention.
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6. Check understanding with an exit prompt
Ask each student to answer: “What information does this system use? What does it predict or decide? Who could be affected? What would you change or check?” Assess whether the student can connect inputs and outputs, identify an affected group, and offer a reasoned check or safeguard. This is a suggested classroom prompt based on the learning goals, not a published or validated rubric.
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UNESCO’s student framework is a global reference, not a prescribed Class 6 syllabus. UNESCO advises adapting curriculum focus and expected mastery to local readiness, instructional time, teacher skills, infrastructure, and students’ prior competencies. An introductory discussion can remain conceptual; a class with more time or experience can investigate more perspectives or develop a fuller proposed solution.
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Use these checks when choosing an activity:
- Device dependence: Decide whether the lesson will be fully unplugged, use devices optionally, or require them. The input–output discussion and fairness case can be handled without devices.
- Conceptual focus: Choose whether students will concentrate on data and prediction, everyday applications, ethics and bias, or designing a possible improvement. A short lesson may focus on one connection rather than introduce every topic.
- Readiness and time: Adjust the complexity of the example and the depth of discussion to students’ prior knowledge and the time available.
- Evidence of learning: Look for explanations of inputs and outputs, recognition that other people may be affected, and a plausible check or safeguard—not coding proficiency.
- Local relevance and inclusion: Select examples students can understand, and ask whether the system or decision could affect some people differently or leave them out.
Plan teacher support without making coding a prerequisite
UNESCO’s teacher AI competency framework groups 15 competencies across five dimensions: a human-centred mindset, ethics of AI, AI foundations and applications, AI pedagogy, and AI for professional learning. It describes progression levels of acquire, deepen, and create. These categories can help a school plan teacher support; they do not mean a teacher must master every technical topic before leading an introductory discussion. UNESCO’s AI competency framework for teachers explains the framework.
For further planning, TeachAI’s toolkit identifies practical implementation resources for primary and secondary AI literacy, while UNESCO’s student framework and curriculum materials provide competency and curriculum context. Treat these as optional planning resources and adapt them to local requirements.
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