Lesson 6 of the six-lesson Year 9 AI Digital Literacy unit: Understanding How AI Shapes Digital Content. Can pupils apply what they have learned to decide whether an AI-assisted campaign is ready to publish? This final lesson combines guided practice with a new, individual evidence investigation and a 20-mark unit assessment. Pupils will: Judge claims using supplied evidence. Explain generation, recommendation and copied-source dependence. Evaluate consent and synthetic endorsements. Analyse persuasion and the limits of click data. Make a justified publication decision and identify useful next checks. Activities include: Retrieval and a common investigation framework. Paired Riverside Repair Fair case. Independent Makers Afternoon investigation. Reflection and optional extension. Included: 24-page PDF teacher and pupil pack, complete evidence files, worksheets, worked answers, five-domain mark scheme, follow-up guidance, SEND/EAL support and matching HTML version. Year 9 | Computing | 60 minutes Includes a 25-minute individual assessment, with a longer teaching route available. Fully offline: no accounts or real synthetic-media creation required. This is the culminating lesson in the unit. Lesson 1 is available separately for free.

Lesson 2 of the six-lesson Year 9 AI Digital Literacy unit: Understanding How AI Shapes Digital Content. Why can an AI answer sound convincing while containing false or unsupported information? This complete lesson explores how patterns and context shape generated output, and why fluent language is not proof of accuracy. Pupils will: Explain plausible generation versus factual verification. Judge claims as supported, contradicted or unsupported. Recognise copied sources and the limits of prompt improvement. Activities include: Token-selection simulation. Fictional museum evidence investigation. Claim audit and prompt repair. Independent club scenario and optional extension. Included: 20-page PDF teacher and pupil pack, source records, worksheets, timed plan, worked answers, assessment guidance, SEND/EAL support and matching HTML version. Year 9 | Computing | 60 minutes + optional extension Fully offline: no AI accounts, logins or paid tools required. Builds on the free Lesson 1 on misinformation, deepfakes and verification. Later lessons cover recommendation, consent, persuasion and a final investigation.

Lesson 3 of the six-lesson Year 9 AI Digital Literacy unit: Understanding How AI Shapes Digital Content. Why do similar posts keep appearing in a feed—and does watching something mean you agree with it? This complete lesson uses a fictional recommendation system to explore how signals, ranking and feedback shape what people see. Pupils will: Calculate and compare recommendation scores. Explain how viewing and saving can influence later recommendations. Distinguish inferred interest from agreement or belief. Evaluate how different ranking goals change a feed. Activities include: StreamSquare profile and ranking investigation. Feedback-loop simulation. Analysis of ambiguous viewing motives. Alternative ranking goals and limits. Independent BeatBox case and optional extension. Included: 21-page PDF teacher and pupil pack, source records, worksheets, timed plan, worked answers, assessment guidance, SEND/EAL support and matching HTML version. Year 9 | Computing | 60 minutes + optional extension Fully offline: no accounts, logins or personal browsing data required. The ranking system is an invented classroom model. Part of a unit covering verification, generation, recommendation, consent, persuasion and a final evidence investigation. Lesson 1 is available separately for free.

Lesson 4 of the six-lesson Year 9 AI Digital Literacy unit: Understanding How AI Shapes Digital Content. Does permission to use someone’s photograph also allow a synthetic voice or public endorsement? This complete lesson explores consent, identity and responsible decisions about synthetic media using fictional evidence records. Pupils will: Evaluate permission by purpose, audience and conditions. Distinguish a synthetic-media label from consent. Explain how sharing can remove important context. Propose supportive responses and permitted alternatives. Activities include: Northbank Arts Evening evidence investigation. Consent and disclosure analysis. Decisions under deadline pressure. A supportive response plan. Independent library-kiosk case and optional extension. Included: 21-page PDF teacher and pupil pack, source records, worksheets, timed plan, worked answers, assessment guidance, SEND/EAL support and matching HTML version. Year 9 | Computing | 60 minutes + optional extension Fully offline: no accounts, uploads or real synthetic-media creation required. All people and scenarios are fictional. Part of a unit covering verification, generation, recommendation, consent, persuasion and a final evidence investigation. Lesson 1 is available separately for free.

Lesson 5 of the six-lesson Year 9 AI Digital Literacy unit: Understanding How AI Shapes Digital Content. How can AI-assisted messages target an audience—and do more clicks mean a message is effective or true? This complete lesson explores persuasive techniques, commercial incentives, claim accuracy and the limits of engagement data. Pupils will: Connect AI-assisted drafting, targeting and measurement. Identify persuasive techniques and who benefits. Check claims against supplied evidence. Distinguish clicks and sign-ups from learning or trust. Rewrite a message to support an informed choice. Activities include: Comparison of fictional PlanPilot campaign variants. Claim and persuasion audit. Click-data investigation. Honest message rewrite. Independent FixForward case and optional extension. Included: 21-page PDF teacher and pupil pack, source records, worksheets, timed plan, worked answers, assessment guidance, SEND/EAL support and matching HTML version. Year 9 | Computing | 60 minutes + optional extension Fully offline: no accounts, purchases or personal profiling required. All campaigns and data are fictional. Part of a unit covering verification, generation, recommendation, consent, persuasion and a final evidence investigation. Lesson 1 is available separately for free.

A complete 17-page Year 7 AI lesson explaining how chatbots generate answers, why confident responses can be wrong and how to verify them. This fully resourced Key Stage 3 Computing and Digital Literacy lesson introduces generative AI and next-token prediction through practical, paper-based activities. Pupils begin with a prediction laboratory, selecting likely continuations for different prompts. They learn that a chatbot repeatedly predicts probable next tokens and that probability is not a measure of factual truth. The resource includes: A complete 60–75 minute lesson plan Learning objectives and differentiated success criteria Teacher subject knowledge and safeguarding guidance Diagnostic starter and hinge question Practical next-token prediction laboratory Probability and context activities Accessible model of chatbot generation Fictional chatbot answer and evidence pack Claim-by-claim answer autopsy Supported, contradicted and unverified classifications Hallucination diagnosis activity Risk-based verification triage Source-quality comparison Newsroom publication simulation Final publication decision Retrieval review and exit ticket Teacher answers and assessment rubric Support, challenge and misconception guidance The fictional Bellminster Observatory case gives pupils a polished chatbot response containing incorrect dates, invented people, false precision and unsupported claims. Pupils compare each claim with the supplied evidence and write a corrected sentence. The hallucination clinic introduces four useful failure patterns: invented entities, false precision, blended facts and gap filling. Pupils diagnose errors and prescribe suitable verification checks. A verification-triage task asks pupils to match the level of checking to the possible consequences. Examples range from generating fictional character names to medical advice, historical quotations, accusations of cheating and computer code. The final newsroom simulation requires teams to create a publication protocol, prioritise claims, select suitable sources and decide whether an AI-generated article should be published, corrected, held or rejected. Differentiation includes colour coding, structured classifications, sentence frames and oral rehearsal. Higher-attaining pupils evaluate source limitations, uncertainty, risk and whether consulting another chatbot counts as independent corroboration. No AI account, pupil login, internet access or specialist software is required. All chatbot outputs, people, places and evidence are fictional and included in the printable resource. Suitable for Year 7 Computing, Key Stage 3 Digital Literacy, generative AI, chatbot literacy, misinformation, online safety and verification lessons. This is Lesson 4 of the six-part Year 7 AI Literacy series.

A complete 17-page Year 7 AI lesson teaching pupils how to verify AI images, misleading photographs and fake online content using evidence. This fully resourced Key Stage 3 Computing and Digital Literacy lesson explores the question: Can you spot what is real? Instead of relying on unreliable visual guessing, pupils learn a structured verification process using provenance, context, corroboration and trace evidence. The resource includes: A complete 60–75 minute lesson plan Learning objectives and differentiated success criteria Teacher subject knowledge and safeguarding guidance Purpose-made visual-confidence starter Diagnostic hinge question Fictional Riverside Academy flood investigation Image claim and social-media post Provenance timeline Context-audit evidence Independent corroboration records Manipulation-method classification Six verification stations Reverse-image-search reasoning Evidence-chain reconstruction Correction-notice writing task Final verification-bureau verdict Retrieval review and exit ticket Teacher answers and assessment rubric Support, challenge and misconception guidance Pupils initially rank four image cards by apparent trustworthiness. The activity exposes how easily confidence can be based on visual plausibility rather than reliable evidence. The main investigation concerns a dramatic online post claiming that Riverside Academy has flooded. Pupils examine the account, caption, cropped image, timestamps, archive records, school notice, emergency-service statement and weather evidence. They discover that the image originally appeared in a fictional 2023 newspaper report about a staged emergency drill at a film set. The central image may be genuine, but it has been cropped and reused to support a false current claim. Pupils learn to distinguish: Synthetic images Edited images Miscontextualised photographs Selective cropping Impersonation Satire and parody The lesson explains what reverse-image searching, metadata and content credentials can and cannot establish. It challenges common misconceptions such as “no strange pixels means it is real”, “lots of shares prove it” and “a genuine photograph must support its caption”. The final task requires pupils to choose a calibrated verdict: reliable, misleading, unverified or false. They must use provenance, context and independent corroboration before recommending whether the post should be shared, corrected or reported. Differentiation includes colour coding, evidence categories, verdict definitions and sentence frames. Higher-attaining pupils evaluate source independence, missing metadata, uncertainty and the distinction between misinformation and deliberate disinformation. Suitable for Year 7 Computing, Key Stage 3 Digital Literacy, AI literacy, misinformation, fake news, online safety, media literacy and image-verification lessons. This is Lesson 5 of the six-part Year 7 AI Literacy series.

A complete 16-page Year 7 AI lesson exploring training-data quality, labels, representation and testing through a practical investigation. This fully resourced Key Stage 3 Computing and Digital Literacy lesson answers the question: What makes training data fit for its intended purpose? Pupils become data detectives investigating RecycleRight AI, a fictional system designed to sort school lunch packaging. Although the system performed well during demonstrations, it makes serious mistakes in a real canteen. Pupils examine three competing datasets, identify weaknesses, resolve ambiguous labels and redesign the data plan. The resource includes: A complete 60–75 minute lesson plan Clear learning objectives and differentiated success criteria Teacher subject knowledge and data-pipeline guidance “Which dataset would you choose?” diagnostic starter Whole-class representation hinge question RecycleRight AI case-file investigation Three competing dataset dossiers Sample labelled training records Dataset-quality audit worksheet Evidence, weakness, consequence and action framework Ambiguous-labelling laboratory Representative sampling challenge Independent test report Category accuracy comparisons Error-pattern analysis Prioritised redesign board Deployment recommendation task Retrieval review and exit ticket Complete indicative answers Assessment rubric Support, challenge and misconception guidance Key vocabulary includes dataset, relevant, accurate, representative, duplicate, label, mislabel, imbalance, training set and test set. The lesson teaches pupils to evaluate data using four essential quality lenses: Relevance to the intended task and real operating conditions Accuracy and consistency of labels Representation of important objects, conditions and variations Independent testing using realistic examples not seen during training The opening activity challenges the common assumption that the largest dataset must be the best. Pupils compare professional catalogue photographs, real school-canteen images and a much larger collection of automatically labelled internet images. During the main investigation, pupils identify near-duplicates, incorrect labels, missing categories, narrow collection conditions and unrealistic training examples. Every conclusion must be supported by a specific record or statistic and connected to a likely consequence for the trained model. A labelling laboratory explores difficult mixed-material and contaminated packaging. Pupils develop clear labelling rules and consider when “unknown” is more responsible than forcing a confident category. This is Lesson 2 of the six-part Year 7 AI Literacy series and follows Lesson 1: How Do Machines Learn?

A complete 17-page Year 7 AI lesson exploring bias, fairness, unequal outcomes, proxy data and human oversight through a practical investigation. Pupils investigate OpportunityMatch, a fictional AI system used to recommend pupils for a school Digital Creators programme. Although every pupil is scored using the same formula, the results create unequal outcomes. The resource includes: A complete 60–75 minute lesson plan Learning objectives and differentiated success criteria Teacher subject knowledge and safeguarding guidance Fairness diagnostic starter Whole-class hinge question Fictional AI selection-system case file Training-history evidence Group outcome statistics False-positive and false-negative analysis Matched pupil-profile comparisons Fact, inference and question classification Four-lens fairness carousel Proxy-data investigation Safeguard-design activity AI review-panel verdict Retrieval review and exit ticket Teacher answers and assessment rubric Support, challenge and misconception guidance Pupils consider four different ways of judging fairness: applying the same rule, providing equal opportunity, examining accuracy and considering purpose and need. They discover that these approaches can produce different judgements. The lesson introduces proxy data through apparently neutral inputs such as travel distance, homework submission time and previous club attendance. Pupils examine how these factors might indirectly reflect access or opportunity without making unsupported assumptions about individuals. Evidence includes selection rates, error patterns and closely matched pupil profiles. Pupils must distinguish facts from plausible inferences and unanswered questions before reaching a conclusion. The final review-panel task requires pupils to recommend stopping the system, redesigning it, conducting a limited trial or approving it. Their verdict must use statistical evidence, a matched-profile comparison, a fairness principle and practical safeguards. Differentiation includes structured reasoning routines, sentence frames, colour coding and carefully staged evidence. Higher-attaining pupils evaluate competing definitions of fairness, uncertainty, proxy variables and the limits of human oversight. All pupil profiles and circumstances are fictional. No AI account, pupil login, internet access or specialist software is required. Suitable for Year 7 Computing, Key Stage 3 Digital Literacy, AI ethics, algorithmic bias, data ethics and responsible technology lessons. This is Lesson 3 of the six-part Year 7 AI Literacy series.

A complete 17-page Year 7 AI lesson exploring responsible AI, privacy, copyright, accuracy, fairness, safeguards and human accountability. This fully resourced Key Stage 3 Computing and Digital Literacy lesson concludes the six-part Year 7 AI Literacy series. Pupils become Responsible AI Advisers evaluating StudyBuddy, a fictional AI learning assistant proposed for use throughout a school. They examine its potential benefits, evidence, data use, risks and safeguards before making a justified recommendation. The resource includes: A complete 60–75 minute lesson plan Learning objectives and differentiated success criteria Teacher subject knowledge and safeguarding guidance Initial decision-line activity Benefit, risk and safeguard diagnostic task Whole-class human-oversight hinge question Fictional StudyBuddy AI proposal Stakeholder hearing Privacy and data-journey analysis Accuracy, fairness and verification evidence Copyright and attribution questions Learning and dependence considerations Environmental-impact discussion Access and inclusion analysis Prioritised risk register Enforceable safeguard contract Red-team stress-testing scenarios Adviser evidence ledger Responsible AI Adviser Report Teacher answers and assessment rubric Whole-unit exit ticket StudyBuddy is presented as an AI system that can generate homework explanations, create practice questions, translate instructions, flag possible wellbeing concerns and rank pupils by predicted support needs. Pupils must look beyond the product’s promotional claims and ask whether each function is necessary, proportionate and supported by evidence. The lesson includes fictional pilot results covering incorrect practice questions, translation errors, false wellbeing alerts, missed concerns, incomplete pupil histories and teacher overreliance. Pupils calculate an error rate and consider why averages can conceal serious or unequal outcomes. Pupils also examine copyright, attribution, long-term learning, environmental evidence and unequal access to suitable devices or internet connections. The safeguard-contract task replaces vague promises such as “teachers will check it” with enforceable conditions containing an owner, trigger, action and review method. For the final outcome, pupils produce a Responsible AI Adviser Report recommending one of four options: Reject the proposal Pause until further evidence is supplied Conduct a limited trial with strict conditions Approve the system The report must include benefits, evidence, risks, safeguards, accountability, uncertainty and a clear final recommendation. Suitable for Year 7 Computing, Key Stage 3 Digital Literacy, AI ethics, online safety, data privacy, responsible technology and end-of-unit assessment. This is Lesson 6 and the concluding lesson of the Year 7 AI Literacy series.

Free scheme of work for Year 9 AI Digital Literacy: Understanding How AI Shapes Digital Content. This 10-page planning guide maps a six-lesson unit exploring how digital content is generated, recommended, shared and used to influence decisions. The sequence covers: Misinformation, deepfakes and verification. Why AI produces false or unsupported information. Recommendation algorithms and feedback. Synthetic media and consent. AI-assisted persuasion and engagement data. A final evidence investigation and unit assessment. Included: Unit purpose and learning progression. Objectives and activity summaries for each lesson. Assessment opportunities and follow-up teaching. Overview of the final 20-mark individual assessment. SEND/EAL access and safeguarding guidance. Resource directory and pupil print maps. A 60-minute adaptation of the original Lesson 1 route. Year 9 | Computing | Six lessons of approximately 60 minutes Includes a printable PDF and matching HTML version. This is a planning guide; the complete lesson packs are separate resources. Lesson 1 is also available for free. Lessons 2-6 are available separately.

A complete 15-page Year 7 AI lesson introducing machine learning, training data, patterns, classification and prediction through practical activities. This fully resourced Key Stage 3 Computing and Digital Literacy lesson answers the question: How do machines learn? Pupils become a “Human AI” and use labelled examples of imaginary creatures to identify patterns, construct a simple classification model and test it against unseen cases. They calculate its accuracy, investigate uncertainty and explain why a correct prediction does not prove that a machine understands. The resource includes: A complete 60–75 minute lesson plan Clear learning objectives and differentiated success criteria Teacher subject knowledge and modelling guidance “AI or not?” diagnostic starter Whole-class hinge question Practical Human AI classification investigation Pupil roles and structured investigation method Labelled training dataset Unseen test dataset Model-building worksheet Candidate-rule and counterexample activity Feature-audit task Accuracy calculation Missing-information challenge Evaluation and improvement questions School AI transfer challenge Retrieval review and exit ticket Complete indicative answers Assessment rubric Support, challenge and misconception guidance Key vocabulary includes artificial intelligence, algorithm, training data, label, feature, pattern, model, classification and prediction. The lesson develops pupils’ understanding that an AI system can learn useful patterns from examples and apply those patterns to new inputs. It also challenges common misconceptions, including the ideas that all automation is AI, that a correct prediction proves understanding, or that an AI-generated answer must be correct. Differentiation is built into the lesson. Support includes structured roles, vocabulary guidance, sentence stems and column-by-column dataset analysis. Higher-attaining pupils are challenged to use counterexamples, assess missing features, question limited testing and recommend proportionate human oversight. A final transfer task asks pupils to evaluate a fictional

A complete six-lesson Year 7 AI Literacy scheme of work covering machine learning, training data, bias, generative AI and responsible use. Designed for Key Stage 3 Computing and Digital Literacy, this structured scheme introduces artificial intelligence through six connected questions: What is AI? What does AI learn from? Why can AI produce unfair results? How does generative AI produce answers? How can AI-generated content mislead us? When should people use AI? The scheme includes lesson titles, key learning, suggested classroom activities, assessment opportunities, curriculum rationale, vocabulary progression and intended final outcomes. Topics covered include: Artificial intelligence, algorithms and predictions Machine learning and pattern recognition Training data, labels and representative datasets AI bias, fairness and human oversight Generative AI, chatbots and hallucinations AI images, synthetic media and misinformation Verification, provenance, context and corroboration Privacy, copyright, transparency and accountability Responsible and appropriate uses of AI The sequence progresses from understanding how AI learns from data to evaluating whether an AI system should be used. It concludes with pupils producing a Responsible AI Adviser Report containing benefits, risks, safeguards and a justified final recommendation. Activities are tool-independent, so pupils do not need access to paid AI software or personal accounts. The content is designed to remain useful as individual AI products and curriculum wording change. Suitable for Year 7, Key Stage 3 Computing, Digital Literacy, introductory AI lessons, curriculum planning and

A complete 60–75 minute AI literacy lesson teaching pupils to verify online claims using lateral reading, source tracing and the SIFT method. Includes a lesson plan, source-tracing activity, printable pupil worksheets, independent challenge and teacher answers. No student accounts or external websites required.

Free Lesson 1 of the six-lesson Year 9 AI Digital Literacy unit: Understanding How AI Shapes Digital Content. This lesson can be taught independently or as the introduction to the complete unit. Subsequent lessons explore AI-generated information, recommendation algorithms, synthetic media and consent, persuasion, and a final evidence investigation. This complete, classroom-ready lesson explores AI hallucinations, misinformation, deepfakes and verification, with case studies, worksheets and answers. Pupils learn how to evaluate AI-generated claims and other digital information, recognising that confident or professionally presented output is not automatically reliable evidence. Concepts covered Generative AI hallucinations Provenance and original sources Relevant expertise Supporting evidence Independent corroboration Currency of information Deepfakes and manipulated media Proportionate verification decisions What’s included Teacher lesson plan Diagnostic starter Verification reference sheet Four evidence case files Deepfake response scenario Six-step verification protocol activity Review questions and marking guidance Year group: Year 9 Duration: 60–75 minutes Format: Printable teacher pack and pupil activities No AI accounts, pupil logins or internet access are required.

Complete GCSE Computer Science lesson on bitmap images, colour depth, file-size calculations and compression, with worksheets and answers. Pupils begin by decoding an 8 × 8 binary image and calculating its memory requirement in bits and bytes. They compare bitmap and vector graphics before exploring resolution, metadata and how colour depth determines the number of available colours. The lesson then introduces the formula: Uncompressed image size = width × height × colour depth Pupils complete supported calculations using 1-bit, 2-bit, 4-bit, 8-bit and 24-bit colour. The compression activities introduce lossless compression and run-length encoding. Pupils encode and decode pixel rows, investigate when RLE reduces file size and construct an example where RLE becomes inefficient. Pupils also compare lossy and lossless compression and consider appropriate uses for PNG, JPEG, GIF and SVG formats. This resource contains: Approximately 70-minute lesson plan Learning objectives and success criteria Teaching sequence and suggested timings Key vocabulary and common misconceptions Binary mystery-image starter Bitmap vocabulary matching cards Visual colour-depth reference sheet Printable 8 × 8 pixel-design activity Binary pixel-encoding exercise Run-length encoding investigation Lossy and lossless compression scenarios Scaffolded GCSE examination questions Answers and marking guidance The complete lesson can be delivered from the printable pack. It requires no external websites, videos or student accounts. Suitable for GCSE Computer Science and higher-attaining Key Stage 3 classes.