Aap kya seekhenge
What this course delivers
From using AI tools to governing institutional AI adoption
This leadership programme prepares principals, coordinators, HoDs and trustees across vision, readiness and governance, teacher capacity, policy, privacy and child safety, procurement, integrity, monitoring, communication and a phased roadmap — governing AI at institutional level, not just using it.
Set an institutional AI vision, establish governance, ownership and decision rights (RACI), and require a leadership decision in every module — not passive reading.
Protect student data and child safety, classify high-risk uses out, keep humans accountable for decisions about children, and never upload confidential data to public AI tools.
Adopt problem-first, evaluate vendors on data ownership and exit, budget for the true total cost, and monitor value without surveillance — no hype, no 'AI theatre'.
Every module builds an institutional artifact — vision, policy, registers, plans and roadmaps — that assembles into a governing-body-ready AI leadership portfolio.
What you'll build
Every module builds an institutional artifact, and you graduate with a governing-body-ready Institutional AI Leadership Portfolio — these sixteen sections — scored on a ten-criterion, hundred-point analytic rubric.
Course Syllabus
Start here: understand how the course, assessments, institutional portfolio and certificate work; then accept the responsible-use commitments — human accountability, data privacy, no confidential uploads, evidence over hype, and not treating the course as legal advice. Take the entry diagnostic to find where to focus.
Learning Outcomes
- Describe the course structure, assessments, institutional portfolio and certificate requirements.
- Accept the responsible-use commitments: accountability, privacy, no confidential uploads, evidence over hype.
- Use the entry diagnostic to identify where to focus, without punitive evaluation.
Lessons
Welcome and Course Navigation
Objective: Describe how the orientation, ten modules, assessments and capstone build an institutional AI leadership portfolio, and how to use the course's tools and access options.
Responsible Use and the Entry Diagnostic
Objective: Accept the responsible-use commitments and complete the entry diagnostic to generate a private focus profile, without punitive evaluation.
Module Assessment
Entry diagnostic (ungraded, 20 questions) + accepted responsible-use declaration · 0 Questions
Visual Concepts
Timeline Visual
Course roadmap: orientation to capstone
Flowchart
Institutional portfolio architecture
Comparison Chart
Learner access matrix
Understand AI accurately before selecting tools, adopt from educational value rather than tool hype, and define a clear institutional AI vision aligned to the school's mission and endorsed by leadership.
Learning Outcomes
- Explain AI's capabilities and limitations, distinguishing assistance from autonomous decision-making.
- Adopt AI from educational value and problem-first thinking rather than tool hype, avoiding 'AI theatre'.
- Define an institutional AI vision aligned to mission, equity, safety, transparency and accountability.
Lessons
AI in Education — Capabilities, Limitations and Misconceptions
Objective: Distinguish predictive from generative AI, identify limitations (hallucination, bias, automation bias), and separate assistance from autonomous decision-making.
From Tool Adoption to Educational Value
Objective: Adopt AI from a problem-first, value-driven stance, identify low-risk quick wins, and avoid tool-first adoption and 'AI theatre'.
Building an Institutional AI Vision
Objective: Draft an institutional AI vision aligned to the school's mission and grounded in educational purpose, human agency, equity, safety, transparency and accountability.
Module Assessment
One-page Institutional AI Vision Canvas (mission-aligned, leadership-endorsed) · 8 Questions
Visual Concepts
Comparison Chart
AI capability-versus-risk matrix
Flowchart
Tool-first versus purpose-first decision flow
Flowchart
Institutional AI vision pyramid
Timeline Visual
AI adoption maturity ladder
Resources
Responsible-use commitments for AI leadership
Accountability, privacy, no confidential uploads, evidence over hype, and not legal advice.
AI capability-versus-risk matrix
Judge proposed AI uses by educational value against risk before adopting.
Institutional AI Vision Canvas
A one-page template grounded in purpose, agency, equity, safety and measurable value.
Purpose-first adoption decision guide
Start from a real problem and avoid tool-first 'AI theatre'.
Assess institutional readiness across leadership, policy, people, data and infrastructure; map stakeholders and decision rights with a RACI model; and establish an AI governance structure with clear ownership, approval authority and escalation.
Learning Outcomes
- Assess institutional AI readiness across leadership, policy, people, curriculum, infrastructure, data governance and monitoring.
- Map stakeholders and decision rights using a RACI model.
- Establish an AI governance structure with terms of reference, approval authority and escalation.
Lessons
Conducting an Institutional AI Readiness Assessment
Objective: Assess institutional readiness across the twelve readiness domains and identify the two or three that most constrain safe AI adoption.
Stakeholders, Roles and Decision Rights
Objective: Map the stakeholders in institutional AI decisions and assign responsible, accountable, consulted and informed (RACI) roles to remove ambiguity.
Establishing an AI Governance Structure
Objective: Design an AI steering committee with terms of reference, meeting cadence, approval authority, risk and policy ownership, and escalation paths.
Module Assessment
AI Readiness Assessment + Governance Committee Charter + Stakeholder RACI · 8 Questions
Visual Concepts
Flowchart
Institutional AI governance structure
Comparison Chart
RACI matrix for AI decisions
Radar Chart
AI readiness radar chart
Timeline Visual
Governance escalation ladder
Identify role-specific teacher AI competency needs, design effective professional development that transfers to the classroom, and lead change while managing resistance without mandatory, unprepared adoption.
Learning Outcomes
- Identify role-specific AI competency needs across teaching and support staff.
- Design professional development that transfers to classroom practice and produces evidence of use.
- Lead change and manage resistance without mandatory, unprepared adoption.
Lessons
Understanding Teacher AI Competency Needs
Objective: Identify the AI competencies teachers need and design role-specific pathways for new teachers, experienced teachers, HoDs, coordinators and support staff.
Designing Effective Professional Development
Objective: Design a professional-development cycle that moves from demonstration through guided practice and coaching to classroom transfer with evidence of use.
Leading Change and Managing Resistance
Objective: Diagnose the sources of resistance to AI adoption and design a balanced change response using early adopters, pilots, psychological safety and feedback loops.
Module Assessment
Twelve-month Teacher AI Capacity Development Plan + role-based pathways + change-management response · 8 Questions
Visual Concepts
Timeline Visual
Teacher AI competency progression
Timeline Visual
Change-adoption curve
Cycle Diagram
Capacity-building cycle
Flowchart
Pilot-to-scale funnel
Ground institutional AI in responsible principles, draft an acceptable-use policy covering approved and prohibited uses, and classify use cases by risk with clear approval workflows.
Learning Outcomes
- Apply responsible AI principles (human agency, fairness, transparency, accountability, proportionality) to school decisions.
- Draft an acceptable-use policy covering approved uses, prohibited uses, data, integrity and review cycle.
- Classify AI use cases into low, moderate, high and prohibited risk with approval workflows.
Lessons
Responsible AI Principles for Schools
Objective: Apply responsible AI principles — human agency, fairness, transparency, accountability, privacy, safety and proportionality — to concrete school decisions.
Developing an Acceptable-Use Policy
Objective: Draft an institutional acceptable-use policy that specifies approved and prohibited uses, data rules, integrity, disclosure, review and sanctions.
Risk Classification and Approval Workflows
Objective: Classify AI use cases into low, moderate, high and prohibited risk, and route each through a proportionate approval workflow.
Module Assessment
Acceptable-Use Policy + AI Use-Case Register + AI Risk Register · 8 Questions
Visual Concepts
Cycle Diagram
Responsible AI principles wheel
Comparison Chart
AI use-case risk matrix
Flowchart
Acceptable-use decision tree
Flowchart
Approval workflow
Map data across AI-enabled workflows, apply consent, transparency and children's rights with data minimisation, and prevent and contain AI cybersecurity incidents — separating legal requirements from institutional choices.
Learning Outcomes
- Map personal, sensitive and derived data across AI-enabled school workflows and their processors.
- Apply consent, transparency, purpose limitation and data minimisation with special care for children's data.
- Prevent, contain and escalate AI cybersecurity incidents such as confidential uploads and shadow AI.
Lessons
Mapping Data in AI-Enabled School Workflows
Objective: Identify the personal, sensitive, prompt, metadata and derived data flowing through AI-enabled workflows, and the processors that handle it.
Consent, Transparency and Rights of Children
Objective: Apply purpose limitation, data minimisation, age-appropriate transparency and consent, distinguishing legal requirements from institutional policy choices.
Cybersecurity and AI Incident Prevention
Objective: Identify AI-specific cybersecurity risks (confidential uploads, shadow AI, API-key exposure, deepfakes) and design containment, escalation and prevention.
Module Assessment
School AI Data Inventory + Privacy Impact Screening + Incident Reporting Form · 8 Questions
Visual Concepts
Cycle Diagram
School AI data lifecycle
Flowchart
Data-minimisation funnel
Flowchart
Incident-response flowchart
Comparison Chart
Data classification model
Design sustainable, inclusive AI infrastructure; evaluate vendors on educational, privacy, security and exit criteria; and budget for the true total cost of ownership including hidden and exit costs.
Learning Outcomes
- Design AI infrastructure that is secure, inclusive and resilient across devices, bandwidth and shared environments.
- Evaluate AI vendors on educational, privacy, security, accessibility, lock-in and exit criteria.
- Budget for the total cost of ownership, including hidden, usage-based and exit costs.
Lessons
Designing Sustainable AI Infrastructure
Objective: Design AI infrastructure covering identity, access, devices, bandwidth, logging, continuity and inclusive low-bandwidth and shared-device access.
Evaluating AI Vendors and Products
Objective: Evaluate an AI vendor against educational, privacy, security, accessibility, data-ownership, lock-in and exit criteria using a weighted scorecard.
Budgeting, Total Cost and Procurement Governance
Objective: Estimate the total cost of ownership of an AI tool — including hidden, usage-based, training and exit costs — and apply procurement stage-gates.
Module Assessment
Vendor Due-Diligence Questionnaire + Weighted Vendor Scorecard + Total-Cost-of-Ownership estimate · 8 Questions
Visual Concepts
Flowchart
School AI reference architecture
Comparison Chart
Vendor-evaluation heat map
Comparison Chart
Total-cost-of-ownership iceberg
Timeline Visual
Procurement stage-gate
Guide responsible AI-supported teaching with human review, uphold academic integrity fairly without over-relying on detection scores, and redesign assessment for authentic learning in an AI-enabled environment.
Learning Outcomes
- Guide responsible AI-supported teaching and learning with human review and evidence checking.
- Uphold academic integrity fairly, using due process and not relying on AI-detection scores as sole evidence.
- Redesign assessment for authenticity using performance tasks, process evidence and oral defence.
Lessons
Responsible AI-Supported Teaching and Learning
Objective: Distinguish appropriate AI support for teaching and learning from inappropriate use, with human review and evidence checking as the constant.
Academic Integrity in the Age of Generative AI
Objective: Design an academic-integrity approach that distinguishes appropriate from unauthorised assistance and uses due process, not AI-detection scores, as evidence.
Redesigning Assessment for Authentic Learning
Objective: Redesign assessment for authenticity using performance tasks, local context, process evidence, oral explanation and in-class checkpoints.
Module Assessment
School AI Academic-Integrity Protocol + Authentic Assessment Redesign + Fair Review Checklist · 8 Questions
Visual Concepts
Timeline Visual
Assessment authenticity continuum
Flowchart
AI-use disclosure model
Comparison Chart
Evidence-triangulation diagram
Flowchart
Academic-integrity response flow
Monitor AI adoption proportionately without surveillance, measure educational and operational value using activity, output and outcome metrics, and run a structured incident-response and continuous-improvement cycle.
Learning Outcomes
- Monitor AI adoption proportionately using aggregated, anonymised data without disproportionate surveillance.
- Measure educational and operational value using activity, output, outcome and risk indicators.
- Run a structured incident-response and continuous-improvement cycle with root-cause analysis.
Lessons
Monitoring AI Adoption Without Surveillance
Objective: Design proportionate monitoring using aggregated, anonymised data and purpose limitation, avoiding hidden monitoring and behavioural overreach.
Measuring Educational and Operational Value
Objective: Select balanced indicators across activity, output, outcome and risk, avoiding vanity metrics and distinguishing usage from genuine value.
Incident Response and Continuous Improvement
Objective: Run a structured incident-response cycle — intake, triage, containment, investigation, communication, corrective action and root-cause-driven improvement.
Module Assessment
AI Adoption Balanced Scorecard + Incident Severity Matrix + Corrective Action Report · 8 Questions
Visual Concepts
Radar Chart
Balanced AI adoption scorecard
Checklist
Monitoring-without-surveillance principles
Comparison Chart
Incident severity matrix
Cycle Diagram
Continuous-improvement cycle
Communicate AI adoption clearly (including where AI is not used), build trust through student voice and parent participation, and ensure equity, inclusion and linguistic diversity so AI never excludes.
Learning Outcomes
- Communicate AI adoption clearly to parents and students, including where AI is and is not used.
- Build trust through consultation, student voice, parent participation and complaint handling.
- Ensure equity, inclusion and linguistic diversity so AI adoption does not exclude any group.
Lessons
Communicating AI Adoption Clearly
Objective: Communicate why, where and where not AI is used, the human oversight in place, and how data is handled, addressing common misconceptions.
Student Voice, Parent Participation and Trust
Objective: Build trust through consultation, student voice, parent participation, accessible feedback channels, objection pathways and fair complaint handling.
Equity, Inclusion and Linguistic Diversity
Objective: Assess and mitigate equity impacts of AI adoption across the digital divide, disability, language, socioeconomic and rural contexts, avoiding exclusion through mandatory technology.
Module Assessment
Parent Information Notice + Student-Friendly AI Explanation + Equity Impact Checklist · 8 Questions
Visual Concepts
Comparison Chart
Stakeholder communication map
Cycle Diagram
Trust-building cycle
Flowchart
Communication escalation pathway
Checklist
Equity-impact canvas
Prioritise use cases into a ninety-day plan, build a phased twelve-month institutional AI roadmap with owners and decision gates, and prepare a governing-body-ready capstone presentation of the institutional AI plan.
Learning Outcomes
- Prioritise AI use cases by impact, risk, readiness, effort and cost into a ninety-day plan with stop criteria.
- Build a phased twelve-month institutional AI roadmap with milestones, owners, budget and decision gates.
- Prepare a governing-body-ready capstone presentation with evidence, controls and decisions requested.
Lessons
Prioritising Use Cases and Building a Ninety-Day Plan
Objective: Prioritise AI use cases by impact, risk, readiness, effort and cost, and build a ninety-day pilot plan with success and stop criteria.
Building a Twelve-Month Institutional AI Roadmap
Objective: Build a phased twelve-month roadmap (discover, govern, prepare, pilot, evaluate, improve, scale, review) with milestones, owners, budget, risks and decision gates.
Capstone — Presenting the Institutional AI Plan
Objective: Prepare a governing-body-ready capstone presentation of the institutional AI plan with current state, strategy, controls, budget, roadmaps and the specific decisions requested.
Module Assessment
Complete Institutional AI Leadership Portfolio + Ninety-Day Plan + Twelve-Month Roadmap + Capstone Presentation · 8 Questions
Visual Concepts
Comparison Chart
Impact-effort-risk prioritisation matrix
Timeline Visual
Ninety-day roadmap
Timeline Visual
Twelve-month phased roadmap
Flowchart
Governance decision gates
AI can draft, but it does not understand or verify. You remain responsible for the accuracy, fairness, privacy and classroom-appropriateness of anything you use.