KAHF × HASANAH FOUNDATION
A joint strategic framework

National Digital Citizenship & AI Readiness Initiative

Protecting a generation online, and preparing it for the age of AI.

Hasanah Foundation&Kahf Bangladesh Ltd

Read the framework
The problem

We actively protect our children physically, but abandon them digitally.

Physical realm · guided

Protected by people

  • Supervised by teachers, parents, and scholars.
  • Educational institutions.
  • Scholar’s book and authority.
VS
Digital realm · unguided
4.6
hours online
every day
81%
face severe unguided
harm early
  • Zero curricula, trained guides, or protective policies.
Why now

We cannot afford to miss the AI revolution as we did past technological shifts.

15th century

The printing press

A 200-year delay cost leadership.

200 years late
20th century

Internet & computing

Addressed too late.

Too late
21st century

Cognitive automation

Proactive leadership is required for AI.

Lead from day one
The framework

Global technical standards anchored perfectly by Islamic ethics.

Islamic EthicsAmanah & Truth
Built on the 8 domains of the IEEE 3527.1™ DQ Framework
Anchored in Amanah and Islamic truthfulness
Cultivates digital safety and tech mastery
The learning journey

A comprehensive, age-appropriate journey from primary safety to workforce readiness.

1

Primary Phase

CLASS 1–5

Digital Amanah, privacy, and screen limits.

2

Lower Secondary

CLASS 6–8

Fact-checking, AI hallucinations, and ML mechanics.

3

Secondary Phase

CLASS 9–10

AI prompting, Python logic, and deepfake ethics.

4

Higher Secondary

CLASS 11–12

Data stewardship, sector solutions, and portfolios.

5

Post-Secondary

BRIDGE COURSE

12-week professional bridge for ethical careers.

The curriculum

AI-Ready Bangladesh: a class-by-class curriculum for Classes 1–12, plus a bridge to work.

Every class has a year-end outcome and a full lesson list: 184 lessons in all.

12lessons a year
Class 1–5
16lessons a year
Class 6–8
18lessons a year
Class 9–10
20lessons a year
Class 11–12
Class 1–5

Primary Phase

12 lessons a year · tap to open

Class 6–8

Lower Secondary

16 lessons a year · tap to open

Class 9–10

Secondary

18 lessons a year · tap to open

Class 11–12

Higher Secondary

20 lessons a year · tap to open

The curriculum · Primary Phase

Primary Phase Class 1–5

Belong Safely and Kindly in a Digital World

Age 6–7 Years · 12 Lessons × 25 Minutes

Year-end outcome: Recognize common devices and simple AI features, follow classroom/home rules, protect private information, use kind language, and ask a trusted adult for help.

  1. My Digital WorldPicture-card tour of phones, tablets, computers; sorting tools by purpose.
  2. Device Care and Family RulesCo-creating a 3-rule device agreement for balanced home use.
  3. People Behind the ScreenPuppet scenarios distinguishing classmates from strangers and identifying trusted adults.
  4. AI in Everyday LifeTeacher-led examples of voice recognition and photo sorting (no student accounts).
  5. Instructions and PatternsUnplugged robot game following step-by-step logical directions.
  6. Private or Okay to Share?Sorting cards to keep name, address, and passwords private.
  7. Pause Before ClickingPicture-based "safe / ask first / avoid" click scenarios.
  8. Kind Words OnlineRole-playing helpful vs. hurtful messages; rewriting unkind phrases.
  9. Ads, Games and SurprisesSpotting ad/reward cues in app screenshots and asking before tapping.
  10. AI Can Be WrongStory-based lesson where an AI gives an incorrect answer; checking with an adult.
  11. Healthy Digital BalanceDesigning a day wheel balancing sleep, play, study, and limited screen time.
  12. My Digital Citizen BadgeTeam quiz, class pledge, and mini-poster demonstration.

Notice Patterns, Protect Privacy and Use Technology with Care

Age 7–8 Years · 12 Lessons × 25 Minutes

Year-end outcome: Explain simple inputs and outputs, recognize patterns, distinguish private data from public information, and check questionable digital content with an adult.

  1. Devices Solve Different ProblemsMatching devices and assistive tech to daily tasks.
  2. Input, Process, OutputPretend smart helper using question cards (input) and response cards (output).
  3. Rules and PassphrasesClassroom treasure-lock activity using long memorable passphrases.
  4. How a Machine Spots PatternsSorting animal, shape, and sound cards into labelled groups.
  5. Data is Made of ExamplesBuilding a class tally to show that examples help systems find patterns.
  6. Private InformationSorting profile cards to keep name, location, school, and password private.
  7. Stop, Think, TellComic strips demonstrating actions for pop-ups, prizes, and unknown links.
  8. Online KindnessBuddy role-play providing constructive feedback without teasing.
  9. Real, Pretend or Advertisement?Comparing stories, advertisements, and factual notices.
  10. Ask Clear QuestionsTurning vague questions into detailed, specific prompts.
  11. Check Before BelievingVerifying claims against classroom books or teacher-approved sources.
  12. Balanced Tech ChoicesPlanning a healthy afternoon routine with screen breaks.

Search, Verify and Begin to Understand AI Outputs

Age 8–9 Years · 12 Lessons × 30 Minutes

Year-end outcome: Use teacher-approved search examples, explain digital footprints and data patterns, draft simple prompts, and recognize that AI-generated answers need checking.

  1. My Digital FootprintMapping online actions and understanding that digital actions persist.
  2. Usernames and Secret CredentialsCreating strong privacy profiles and keeping passwords secret.
  3. How Search Results AppearComparing curated result snippets for relevance and credibility.
  4. AI Looks for PatternsRunning a human "prediction machine" with labelled image cards.
  5. Data Quality MattersDemonstrating how missing or mistagged cards lead to machine errors.
  6. Fact, Opinion or Fiction3-corner classroom activity classifying online claims.
  7. Write a Clear PromptBuilding prompts with topic, task, and format without personal details.
  8. AI May Invent DetailsComparing AI sample answers with reference books to spot hallucinations.
  9. Empathy Across MessagesRewriting chat messages to reduce harm and misunderstanding.
  10. Spotting Suspicious MessagesIdentifying urgency, unknown senders, and scam cues.
  11. Create a Digital StorySmall group storyboarding on a healthy digital habit.
  12. Show Your Source and ToolPresenting mini-projects while crediting tools and sources used.

Understand Simple AI Systems and Act Responsibly

Age 9–10 Years · 12 Lessons × 30 Minutes

Year-end outcome: Explain inputs, data, and outputs; use a supervised prompt routine; verify information; and recognize bias, manipulated media, and intellectual-property basics.

  1. Digital Identity and PermissionsAuditing profile permissions and recommending privacy fixes.
  2. Algorithms and Decision TreesPaper decision trees tracing how rules lead to specific outcomes.
  3. Labels and DatasetsLabelling small image sets and discussing ambiguous examples.
  4. What Generative AI DoesTeacher demonstration generating descriptions from safe prompts.
  5. Prompt Building BlocksConstructing prompts using task, context, audience, and output format.
  6. Find Trustworthy SourcesCross-checking claims against age-appropriate credible sources.
  7. Edited Images and DeepfakesComparing edited images with originals to understand manipulation.
  8. Fairness and RepresentationDiscussing datasets that exclude certain groups or situations.
  9. Cyberbullying and ReportingResponse sequences: save evidence, block, report, tell an adult.
  10. Copyright and CreditApplying "use, adapt, credit" rules to classroom media and posters.
  11. Healthy Use and AttentionPlanning focus blocks and breaks to manage persuasive screen design.
  12. Mini Project (Helpful Guide)Creating a poster or storyboard with source checks and safeguards.

Become a Critical, Safe and Creative Digital Learner

Age 10–11 Years · 12 Lessons × 35 Minutes

Year-end outcome: Use a repeatable safety and verification routine, explain basic AI limitations, prompt responsibly, and create a small evidence-based project.

  1. Digital Citizenship CharterDrafting rights, responsibilities, and classroom online norms.
  2. Accounts, Passwords and 2-Step ChecksAccount security walkthrough and multi-step protection.
  3. Search Literacy and Source TrailsTracing facts from online claims back to original primary sources.
  4. AIPattern, Prediction and Generation: Distinguishing classification models from generative text models.
  5. Why AI Can Be WrongCategorizing AI answers into verified, uncertain, and unsupported claims.
  6. Prompt, Refine, CompareRefining prompts across two rounds to observe output improvement.
  7. Private Data and ConsentScrubbing personal data (names, photos, school) from prompt inputs.
  8. Misinformation and Emotional HooksEvaluating headlines and urgent claims using source checks.
  9. Fairness, Stereotypes and Missing VoicesAnalyzing dataset imbalance and identifying missing perspectives.
  10. AI and Academic HonestyEstablishing class rules for brainstorming vs. submitting copied AI work.
  11. Design a Community-Helpful ArtefactPlanning an explainer or poster addressing a local need.
  12. Project Gallery and ReflectionPeer review using a rubric covering safety, accuracy, and creativity.
The curriculum · Lower Secondary

Lower Secondary Class 6–8

Understand Data, Algorithms and Dependable Online Practice

Age 11–12 Years · 16 Lessons × 40 Minutes

Year-end outcome: Explain algorithms and machine learning conceptually, protect accounts, evaluate sources and AI outputs, use structured prompts, and complete a small digital project.

  1. Digital Life AuditMapping digital recreation, learning, and setting personal usage goals.
  2. Accounts, Authentication and RecoveryThreat modelling login practices using long passphrases.
  3. Phishing and Social EngineeringIdentifying warning signs in simulated suspicious emails/links.
  4. Algorithmic ThinkingBuilding step-by-step algorithmic instructions for real-world tasks.
  5. AI, Machine Learning and RulesComparing rule-based programming with pattern-learning systems.
  6. Data Types and LabelsCreating a non-sensitive classroom dataset with rows, features, and labels.
  7. Training and Testing ConceptSplitting data into training and test sets to understand model validation.
  8. Prompt StructureConstructing prompts with role, task, context, constraints, and output format.
  9. Verify AI AnswersApplying verification checklists for claims, calculations, and dates.
  10. Bias and FairnessAnalyzing recommendation scenarios to see how data causes unfairness.
  11. Images, Deepfakes and ContextInspecting source provenance, crop, and reverse-image clues.
  12. Digital Communication and ConflictPracticing calm, respectful conflict resolution online.
  13. Copyright, Attribution and ReuseApplying public domain, creative commons, and credit rules.
  14. Data Tables and VisualisationsBuilding charts from synthetic data and interpreting trends.
  15. Block-Code a Simple SystemUnplugged or block-based conditional logic and pattern exercises.
  16. Project (Useful Digital Explainer)Creating a documented explainer with source logs and AI disclosure.

Critically Assess AI, Information and Digital Impacts

Age 12–13 Years · 16 Lessons × 40 Minutes

Year-end outcome: Evaluate AI-generated content, understand dataset bias and model limits, practice secure digital habits, and build a source-checked digital solution.

  1. Digital Identity and ReputationReviewing post histories and future audience impacts.
  2. Threats, Scams and Account SecurityThreat-modelling student accounts and recovery routes.
  3. Networks and Internet BasicsSimulating data transmission across devices, routers, and servers.
  4. How Models Learn PatternsSimulating training iterations, prediction outputs, and error correction.
  5. Overfitting and Weak ExamplesComparing memorized rules with generalized machine models.
  6. Prompt Iteration LabMaintaining prompt-change logs and evaluating output improvements.
  7. Hallucinations and CitationsVerifying generated citations against primary databases.
  8. Media Manipulation and DeepfakesAnalyzing audio/video inconsistencies and corroborating evidence.
  9. Bias, Fairness and AccessibilityTesting systems across diverse user personas to fix accessibility gaps.
  10. Personal Data, Consent and AIScrubbing unnecessary sensitive inputs from automated workflows.
  11. Online Harm and Bystander ActionRehearsing block, report, evidence preservation, and escalation.
  12. Copyright, Remixing and AttributionCreating a source ledger for text, image, and audio assets.
  13. Spreadsheet and Chart SkillsCleaning synthetic data and presenting accurate trends.
  14. Introductory Coding or No-Code LogicBuilding and debugging decision-tree or no-code workflows.
  15. AI Across Local SectorsMapping safe AI use cases in agriculture, health, education, and small business in Bangladesh.
  16. Project Studio and Peer ReviewDeveloping a community problem solution with peer rubric evaluation.

Move from AI Awareness to Responsible Creation

Age 13–14 Years · 16 Lessons × 45 Minutes

Year-end outcome: Compare AI tools and outputs, identify misinformation and bias, use prompts systematically, interpret basic data, and deliver a responsible project.

  1. Digital Rights and ResponsibilitiesDebating dilemmas on privacy, consent, and speech.
  2. Cybersecurity FundamentalsMulti-factor authentication, software updates, and permission audits.
  3. AI Systems (Input to Output)Diagramming data, model, user prompt, output, and feedback loops.
  4. Data Quality and RepresentativenessAuditing datasets for gaps, duplicates, and representation imbalance.
  5. Model Accuracy and UncertaintyUnderstanding why machine confidence is not correctness.
  6. Advanced Prompt PatternsMulti-step prompts using constraints, persona, and output schemas.
  7. Research with AI SafelyGenerating research questions via AI, followed by primary source verification.
  8. Deepfake and Synthetic Media LiteracyExamining metadata, provenance, and news corroboration.
  9. Bias, Fairness and Inclusion TestBuilding test matrices to identify edge-case model failures.
  10. AI and Learning IntegrityWriting AI disclosure statements and separating original analysis.
  11. Digital Wellbeing and Persuasive DesignAuditing autoplay, notifications, and recommendation algorithms.
  12. Privacy and Security by DesignMapping data flows and stripping personal data from app designs.
  13. Visualisation and InterpretationCritiquing misleading chart axes and cherry-picked data.
  14. Build with Code or No-CodePrototyping an interactive workflow using approved tools.
  15. Use Case and Social ValueDefining problem statements and success metrics for local needs.
  16. Project Demo and ReflectionPresenting working prototypes with source logs and responsible-use disclosures.
The curriculum · Secondary

Secondary Class 9–10

Apply AI Critically in Study, Research and Problem Solving

Age 14–15 Years · 18 Lessons × 45 Minutes

Year-end outcome: Use structured prompts and source verification, understand model/data limitations, explore coding or no-code prototyping, and publish a documented project.

  1. Digital Citizenship and Current RisksAnalyzing identity theft, scams, and digital harassment.
  2. Privacy and Security Threat ModellingMapping assets, threats, and mitigations for student projects.
  3. AI Types and Generative SystemsCategorizing models into classification, prediction, recommendation, and generation.
  4. Data, Features and LabelsInspecting synthetic datasets to fix feature leakage and missing values.
  5. Training, Test and EvaluationUsing classifier simulations and error matrices to evaluate model accuracy.
  6. Prompt Design and IterationMaintaining reproducible prompt logs with task, context, constraints, and schemas.
  7. AI for Research and StudyPlanning study outlines via AI and verifying every statement independently.
  8. Source Evaluation and CitationsTracing claims to primary literature and documenting formal citations.
  9. Hallucination and ConfidenceEstablishing verification checklists prior to accepting generated content.
  10. Bias, Fairness and Cultural ContextTesting AI outputs across different languages and cultural nuances.
  11. Synthetic Media and Digital EvidenceApplying provenance checks to evaluate audio/visual evidence.
  12. AI-Assisted Coding FundamentalsUsing sandboxed AI suggestions to explain, test, and adapt code.
  13. Cyber Hygiene for ProjectsManaging API keys, permissions, secrets, and secure backups.
  14. Copyright and Intellectual PropertyCreating asset licensing plans and documenting AI assistance.
  15. Data Exploration and VisualisationAnalyzing public datasets and visualising patterns with source notes.
  16. Project Design and ProposalSubmitting 1-page proposals for local problem-solving projects.
  17. Build, Test and RevisePrototyping workflows using code/no-code tools and gathering user feedback.
  18. Showcase and PortfolioPresenting project portfolios with source logs and responsible-use statements.

Build and Evaluate a Responsible AI-Supported Solution

Age 15–16 Years · 18 Lessons × 45 Minutes

Year-end outcome: Design a solution from a defined problem, apply structured AI workflows, evaluate sources and outputs, and communicate limitations and ethical implications.

  1. Digital Citizenship (Rights and Remedies)Action plans for online risk scenarios and legal reporting channels.
  2. Security, Scams and Privacy EngineeringApplying threat models to student portfolio app ideas.
  3. AI Lifecycle and System MapMapping problems, data, models, outputs, evaluation, and human oversight.
  4. Prompt Chaining and Task DecompositionBreaking complex multi-step tasks into staged prompts with human reviews.
  5. Source-Grounded ResearchBuilding reference-grounded workflows that produce exact citations.
  6. Data Collection and QualityDesigning surveys and handling public data with privacy minimisation.
  7. Evaluation Metrics in Plain LanguageCalculating accuracy, precision, and recall using simple examples.
  8. Bias and Impact AssessmentMapping affected stakeholder groups and completing impact worksheets.
  9. Deepfakes and Information IntegrityEstablishing origin verification workflows before sharing media.
  10. AI-Assisted Python or No-CodeWriting and testing functional scripts with logical explanations.
  11. Debugging and Human ReviewUsing test cases to find and fix bugs in AI-suggested code.
  12. Intellectual Property and DisclosurePreparing transparent contribution statements for project portfolios.
  13. AI in Work and Public ServicesEvaluating AI integration risks/benefits in Bangladeshi health and agriculture.
  14. User-Centred Accessibility DesignImproving prototype accessibility using user persona checklists.
  15. Project Plan and Risk RegisterSubmitting scope, risk registers, and milestones for capstones.
  16. Build SprintWorking in teams to build working demonstrator prototypes.
  17. Test, Review and IterateRunning peer tests for correctness, safety, usability, and fairness.
  18. Pitch, Portfolio and ReflectionPitching capstone projects with complete auditable portfolios.
The curriculum · Higher Secondary

Higher Secondary Class 11–12

Develop Transferable AI, Data and Career Skills

Age 16–17 Years · 20 Lessons × 50 Minutes

Year-end outcome: Work with structured data and AI-assisted workflows, evaluate outputs and risks, prototype a domain-relevant solution, and create a career-oriented portfolio.

  1. AI-Era Digital ProfessionalismDrafting professional online profiles and communication charters.
  2. Cybersecurity and Data StewardshipDesigning access controls and least-privilege security models.
  3. AI Concepts and Model FamiliesComparing rule-based, ML, and generative model families for specific use cases.
  4. Data Literacy and Descriptive StatisticsSummarizing synthetic/public datasets using charts and caveats.
  5. Data Provenance and DocumentationWriting data sheets detailing source, collection consent, and limits.
  6. Machine-Learning LifecycleMapping end-to-end problem framing, training, validation, and deployment.
  7. Evaluation and Error AnalysisAnalyzing confusion matrices to evaluate false positive/negative trade-offs.
  8. Prompt Engineering for Work TasksBuilding reusable, structured prompts with verification gates.
  9. Retrieval and Source-Grounded AI ConceptsUnderstanding how Retrieval-Augmented Generation (RAG) reduces hallucination.
  10. Workflow AutomationMapping repetitive tasks into triggers, checks, and approval gates.
  11. Introductory Python/Data WorkReading, filtering, and summarizing datasets using sandboxed Python scripts.
  12. Responsible Code AssistanceReviewing AI code for vulnerabilities, security flaws, and licensing.
  13. Bias, Fairness and AccessibilityConducting subgroup testing to eliminate model discrimination.
  14. Privacy, Security and Model AbuseIdentifying data leakage, prompt injection, and model safety risks.
  15. Copyright, Academic Honesty and DisclosureProviding transparent contribution ledgers and citations.
  16. AI in Sector PathwaysExploring AI applications in education, business, agriculture, software, and health.
  17. Entrepreneurship and Social InnovationBuilding a problem-solution canvas for community service ideas.
  18. Project Sprint and MilestonesExecuting staged prototype reviews with version logs.
  19. Testing and Impact ReviewTesting prototypes against quality, fairness, security, and accessibility matrices.
  20. Portfolio PresentationPresenting career portfolios, skills gap analyses, and next steps.

Demonstrate AI Readiness for Higher Education and Employment

Age 17–18 Years · 20 Lessons × 50 Minutes

Year-end outcome: Plan and evaluate an AI-supported solution, combine data and domain reasoning, communicate transparently, and graduate with an evidence-based portfolio and career plan.

  1. Digital Portfolio and Professional ConductAuditing portfolios for privacy, evidence, and public tone.
  2. Threat Modelling and Privacy by DesignDiagramming data flows, access controls, and minimisation.
  3. AI Capability BoundariesDistinguishing tasks suitable for AI from those requiring human judgment.
  4. Problem Framing and Measurable GoalsDefining testable problem statements and user metrics.
  5. Data Acquisition and GovernanceDrafting data management plans using licensed public data.
  6. Data Cleaning and Exploratory AnalysisHandling missing data and distribution charts in guided exercises.
  7. Model/Workflow SelectionComparing manual, rule-based, AI-assisted, and automated solutions.
  8. Prompt Systems and Quality ControlsBuilding multi-stage prompt chains with stop conditions.
  9. Evaluation DesignDefining baselines, user test sets, and quantitative success criteria.
  10. Bias, Fairness and Accessibility AuditConducting stakeholder audits and prioritizing mitigations.
  11. Source-Grounded Knowledge WorkflowsBuilding reference-backed workflows using authorized documents.
  12. Automation and Integration FundamentalsConnecting workflow steps using approval gates and recovery paths.
  13. Code, Debugging and ReproducibilityReviewing code logic, edge cases, and version control.
  14. Security and Incident ResponseCreating incident response checklists for accidental data leaks.
  15. Ethics, Law and Institutional PolicyReviewing Bangladeshi cyber laws and institutional AI policies.
  16. IP, Attribution and Transparent UseCreating complete provenance records and licensing notes.
  17. Career Pathways and Labour-Market LearningMapping TVET, university, freelancing, and job pathways.
  18. Capstone DevelopmentPrototyping a real-world solution for a validated community need.
  19. Independent Testing and ReviewGathering structured feedback to revise failure points.
  20. Final Showcase and Next-Step PlanPitching capstone portfolios to a panel and finalizing learning roadmaps.
8 core domains · 1 of 2

IEEE 3527.1™ technical skills, each paired with an Islamic ethical principle.

Digital Identity

Technical competency

Managing profiles, privacy settings, and online footprints.

Islamic ethical integration

Online truthfulness, avoiding false identities, refraining from backbiting (Gheebah).

Digital Use

Technical competency

Healthy screen-time allocation and balanced device usage.

Islamic ethical integration

Custodianship of time (Amanah), avoiding wastage (Israf) and digital addiction.

Digital Safety

Technical competency

Identifying cyberbullying, phishing, scams, and dangerous contacts.

Islamic ethical integration

Guarding sight and hearing from harmful content; protecting oneself and others.

Digital Security

Technical competency

Password hygiene, passphrases, multi-factor authentication, data backup.

Islamic ethical integration

Maintaining trustworthiness (Amanah) and protecting personal and organizational data.

8 core domains · 2 of 2

IEEE 3527.1™ technical skills, each paired with an Islamic ethical principle.

Digital Emotional Intelligence

Technical competency

Online empathy, conflict resolution, respectful communication.

Islamic ethical integration

Islamic etiquette (Adab) in communication, avoiding insult, exercising restraint.

Digital Communication

Technical competency

Evaluating digital messages, respectful online discourse, reporting harms.

Islamic ethical integration

Verifying information before sharing (Surah Al-Hujurat: 6); eradicating fake news.

Digital & AI Literacy

Technical competency

Search literacy, prompt engineering, understanding ML models, verifying AI.

Islamic ethical integration

Seeking knowledge as a religious obligation; using AI for beneficial research.

Digital Rights

Technical competency

Intellectual property, licensing, copyright, and ethical disclosure.

Islamic ethical integration

Respecting intellectual property, rejecting plagiarism (theft of credit), upholding honesty.

Beyond Class 12

A 12-week AI and Digital Skills Bridge Course.

For college, university, TVET and madrasa graduates and job-seeking youth. 60–72 guided hours plus a portfolio project, ending in an actionable digital portfolio and career readiness.

Week 1

Digital Professionalism, Cyber Hygiene, Account Security & Privacy Checklist

Week 2

AI Foundations, Model Boundaries, Ethics & Tool-Selection Frameworks

Week 3

Advanced Prompting, Task Decomposition & Verification Logging

Week 4

Data Literacy, Spreadsheet Analysis, Visualisation & Caveat Reporting

Week 5

Research Methods, Source Quality, Citation Ledger & Reference Analysis

Week 6

No-Code Automation, Workflow Mapping & Human Approval Gates

Week 7

Specialized Track: Beginner Coding or Digital Media Production Prototyping

Week 8

AI-Assisted Development, Sandboxed Execution, Testing & Debugging

Week 9

Track Specialization: Education, Business, Media, Agriculture, Software or Health

Week 10

Responsible System Design: Privacy, Bias, Accessibility, Copyright & Risk Registers

Week 11

Capstone Construction, Peer Testing, Red-Teaming & Failure Documentation

Week 12

Showcase Portfolio, CV Narrative, Project Presentation & Career Roadmap

Pathway badges

Standardized micro-credentials earned on demonstrated performance.

1

Digital Citizen

Class 1–5

Mastery in online safety, privacy, communication, and basic source checking.

2

AI Literate Learner

Class 6–8

Understanding of AI concepts, limitations, verification, and honest academic use.

3

AI Creator

Class 9–10

Prompt design, dataset handling, workflow prototyping, and responsible disclosure.

4

AI-Ready Professional

Class 11–12 + Bridge

Portfolio creation, threat modeling, code debugging, and sector-specific AI integration.

Sample lesson plans

What a lesson looks like in the classroom.

Sample lesson 1 · Class 1 · 25 min

AI in Everyday Life & Device Care

Recognize common devices and simple AI features, name two device safe-use rules, and understand that eyes and time are Amanah.

Start5 minDiscuss sight and hearing as gifts (Amanah).
Model8 minTeacher demonstrates voice recognition.
Team activity7 minSort cards: AI-like feature vs non-AI tool.
Apply & exit5 minCo-create a 3-rule home device agreement.
Sample lesson 2 · Class 7–8 · 45 min

“Can We Trust This AI Answer?”

Distinguish an AI claim from verified evidence, check two independent sources, explain uncertainty, and disclose AI assistance.

Start5 minWhat makes an answer trustworthy? Surah Al-Hujurat: 6.
Model8 minHighlight claims, find the source, check date and context.
Team task15 minLabel claims Supported / Contradicted / Unclear.
Apply10 minRewrite, cite primary sources, state one limitation.
Exit check5 min1 verified claim, 1 unsupported claim, 1 next step.
Sample lesson 3 · Class 9–10 · 45 min

Prompt Engineering & Academic Integrity

Build structured prompts (Task + Context + Constraints + Format), evaluate outputs, and apply honesty disclosures.

Start5 minAI as a learning assistant vs copying work.
Model10 minTurn “Explain gravity” into a structured prompt.
Lab practice20 minDraft, run under teacher mediation, verify against textbook.
Exit check10 minSubmit prompt log, verified summary, AI disclosure.
The delivery engine

A scalable, mathematically sound engine to train educators nationwide.

50Master Trainers · elite regional master core
▼
500Pilot Teachers · equipped school and madrasa educators
▼
10,000+Students · protected and AI-ready generation
24–30hDigital citizenship & media literacy
24–30hAI literacy & pedagogy
MonthlyCommunities of practice refreshers
5 partsTeacher guides, slides, activity cards, offline worksheets & quizzes, parent guides
Ready-to-Teach Kits power every tier: slides, lesson plans, activity cards, parent guides, and policy templates.
The partnership

This partnership cements Hasanah Foundation as the premier guardian of the Ummah’s future.

Pioneering Leadership

Safeguarding faith and youth mental wellbeing.

Ethical Workforce

Creating 100% halal, AI-ready career pathways.

Policy Authority

Collaborating directly with national education ministries.

What Kahf brings

Kahf provides the turn-key, secure technological backbone to execute the vision.

Content Engine

Full bilingual workbooks and animation modules.

⇅

Kahf Kids LMS

Gamified learning and automated student tracking.

⇅

Kahf Guard

DNS-level automated inappropriate content blocking.

The roadmap

A realistic, four-phase, three-year path from pilot to nationwide integration.

Month 1612182436
Phase One · Months 1–3

Foundations

English and Bangla curriculum assets, localized workbooks, LMS modules, and master trainer assessments.

M1–3
Phase Two · Months 4–6

Pilot Implementation

50 schools and madrasas, 500 pilot teachers trained, 10,000 students evaluated.

M4–6
Phase Three · Months 7–9

Impact Evaluation & Refinement

Pre/post evaluation, safety indices, and content refined from classroom observations.

M7–9
Phase Four · Months 10–36

Scaling & National Rollout

National school and madrasa networks, Hasanah Digital & AI Clubs, and annual child online safety reports.

Months 10–36

It is time to unite vision and technology to protect the generation.

Unite Vision

Merge Hasanah’s leadership with Kahf’s technology.

Ultimate Goal

Shape an ethical, AI-ready generation.

Hasanah Foundation Leadership
Kahf Bangladesh Ltd Leadership
Launch 50-School Pilot
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