AI - FSTC Curriculum
Andhra Pradesh
AP MT Training
Round 1 �August 2026
DAY 1
Check - In:
🔵 Stand in a circle!
✏️ Pick up a sketch pen & blank name tag
😊 Write your name + draw an emoji showing your current feeling
🏫 Share your school & district
🌟 Share one memorable moment from your 2-year Amazon Future Engineer Program journey!
Expectations setting:
Cooking Pot:
Norm Setting
“What behaviours and norms do we need to create a space where everyone learns from everyone?”
Unfolding Quest Alliance
QUEST ALLIANCE
Quest Alliance is a not-for-profit organization based in Bangalore, India with a presence across the country. Established in 2008, Quest Alliance equips children and young people with future skills for thriving careers enabling equitable school to work transitions
Problem Statement: By 2030, India is expected to add 31 Crore School Graduates, yet a significant challenge remains: Only 47% are likely to possess the Future-ready Skills needed for employability. Low prospects of employment are likely to trap youth into vicious cycles of unemployment, low wages and inter-generational poverty.
Who?
What?
Vision
A world where all young people take charge of their future and drive equitable social and economic progress.
Mission
We innovate for learning ecosystems to be future ready by creating and embedding tech enabled learning experiences for young people to thrive in their careers.
How?
Quest Alliance
Journey in AP
MasterCoach Model: Scaling computational thinking through dedicated MasterCoaches
Educator Empowerment: 557 teachers trained on 21st-century pedagogy and STEM mindset via the I & H process
Student Reach: 72,107 students actively engaged through the program
MoU Signed: SSA, LFE - Funded by Amazon to build a state-level AI curriculum grounded in 21st-century teacher pedagogy��100 schools across Srikakulam, Vizianagaram, and Visakhapatnam
Teachers Capacity: 200 teachers trained to deliver the curriculum�
Impact: 23,000 students (Grades 7–9) empowered with AI, Computational Thinking (CT), and Future Careers skills
2024
Journey of Andhra Pradesh
District Saturation Model
State engagement: 640 schools in 3 districts and 102 schools across the state.
Teachers Capacity: 1537 teachers trained to deliver the curriculum
Impact: 1 lakh students on core concepts of AI, ethics, Equity , Justices and Problem solving lens.
2025
MoU Signed: Partnered with �Department of School Education, �Govt. of Andhra Pradesh.
Skill Building: Fostering a STEM mindset and 21st-century skills in students.
Impact: 44,000+ students across 91 schools in 2 districts (Srikakulam & Guntur)
Implementation Partners: AIF and NIIT Foundation
2019
Impact Of AI Education
KEY FINDINGS
The programme made AI normal in the classroom. Now use style is what matters.
76%
of students now use AI�
Three in four, mostly for learning. About seven in ten use it weekly.
+0.75
the top technology gain�
AI-tool use rose more than any other learning technology (2.18 to 2.93 on a 1 to 6 scale).
+3.7
points on reasoning�
Raven's score rose every round, 62.6% to 66.3% correct. RAVENs is an objective, curriculum-free test of non-verbal cognitive ability. .
How,
not whether
is what counts�
Active, verifying users gain; passive over-reliance* goes with about 1.2 fewer Raven's items.
!
The bottom line. AI-based teaching can work in these schools. The next step is teaching students to use AI actively, not passively, and equipping teachers to guide that.
*Passive over reliance: Students accept AI answers without checking, depend on AI for difficult homework, or find it hard to start work without AI
2
WHAT WE MEASURED
Six instruments, the same students, three rounds
1
Raven's Progressive Matrices
Fluid reasoning that does not depend on the curriculum. A 12-item block common to all three rounds.
2
21st-century skills (SICKS)
Five domains, collaboration, communication, creativity, self-direction, critical thinking. 15 items, self-rated 1 to 5.
3
Alternative-Uses Task
Behavioural creativity: how many different uses a student can list for everyday objects. An objective count.
4
AI-use & offloading module
New in 2026. Adoption, how often, and use style: active (check, question, rewrite) versus passive (copy, accept).
5
Technology-use battery
Eight everyday learning technologies, from finding information to AI tools. Frequency 1 to 6.
6
Three linked rounds
2024 baseline, 2025 midline, 2026 endline. The same children followed across two years.
3
THE SAMPLE
738 students across three north-coastal districts (76% AI users)
738
students followed across all three rounds
51
government secondary schools
562
had used AI by 2026 (76%)
Rural, first-generation learners in government schools.�
Adoption is remarkably even across districts, about 76% in each. This is not an elite or urban sample; it is exactly where low-cost AI could matter most.
4
OUTCOME · TECHNOLOGY USE
AI-tool use grew the most of any learning technology
AI is the standout gain.�
Of eight learning technologies, AI-tool use rose the most, up 0.75 point since 2024. Other everyday uses stayed broadly flat.
Why the 2025 spike, then dip?�
AI-tool use jumped in 2025 as the module arrived, then settled in 2026 as novelty gave way to habit. Adoption is now steady at three in four students.
5
OUTCOME · HOW AI IS USED
Students reach for AI to learn, but habits are mixed
Mostly for understanding.�
The top uses are learning uses: understand a topic, get step-by-step help, find information.
But over-reliance rivals good habits.�
About 4 in 10 copy answers exactly; only about 3 in 10 check, question or rewrite. That gap is the opening for the programme.
6
THE KEY IDEA
There are two ways to use AI, and only one builds skill
ACTIVE · self-regulated use
builds skill ↑
PASSIVE · cognitive offloading
substitutes for thinking ↓
Same tool, opposite effects.�
The two styles pull apart cleanly in the data. Everything that follows is about which style a student adopts, and how to shift them towards the active one.
9
SAME START, DIFFERENT PATHS
Where students end up tracks how they use AI
They began together in 2024.�
Non-users, light users and heavy users all started within a few points of one another on reasoning. Starting ability does not explain what happened next.
They diverged by 2026.�
Light and non-users climbed to about 70%. Heavy, passive-leaning users fell back to where they began. The fork lines up with use style, not starting point.
13
THE MESSAGE
AI-based teaching can work here, with the right safeguards
1
Adoption already succeeded
AI is now a normal, mostly-for-learning tool in these classrooms. Access is no longer the constraint.
2
Use style is the real lever
Active, verifying use goes with higher reasoning and creativity; passive copying goes with lower. It is how, not whether.
3
The opening is the middle
Top learners already use AI well. Over-reliance concentrates among capable-but-not-yet-expert students in the middle.
4
Equip teachers to guide it
Safeguards mean teaching verification, check, ask, rewrite, and giving teachers the capacity to show students how to use AI well.
14
SSA Report - AFE
SLIDE 1 OF 3
A joint review by Samagra Shiksha AP, Quest Alliance, and PwC evaluating AI & Computational Thinking in government secondary schools across Srikakulam & Vizianagaram.
Samagra Shiksha Andhra Pradesh • Quest Alliance Diagnostic Study
Guide for Teachers & School Leaders
• SAMAGRA SHIKSHA AP
Amazon Future Engineer (AFE) Program Insights
3 / 3
Teacher Training
Universal attendance across training rounds
2.8 / 3
Ethical AI Score
Students strongly identify & reject AI bias
67%
Fixed Period Coverage
vs 58% in ad-hoc/free period schools
600+
Schools Reached
Across ZPHS, APMS & KGBVs in AP
Program Purpose
Embed Artificial Intelligence, ethics, and career awareness in 600+ ZPHS, APMS, and KGBV schools, empowering rural and marginalized students with future skills.
Key Success Anchor
Teachers showed 100% attendance (3.0/3.0) across training rounds. The current priority is translating training into regular classroom practice.
Primary Bottleneck
Timetable allocation is the single biggest factor governing student outcomes. Fixed periods boost coverage from 58% to 67%+.
SLIDE 2 OF 3
EVALUATION RESULTS • SCORE BREAKDOWN (SCALE 1.0 - 3.0)
Student Learning & Classroom Delivery Scorecard
Key Takeaway: High student enthusiasm and strong ethical comprehension must be backed by predictable scheduling and career guidance to build genuine career agency.
AI Concepts & Definition
Students clearly articulate basic AI concepts, daily-life examples (chatbots, recommendations), and distinguish human vs. machine intelligence.
Ethics, Fairness & Bias
Highest performing area! Students consistently reject gender and data bias in AI outputs, recognizing fairness as a core technical requirement.
Pedagogy & Collaboration
Teachers use interactive tools (QuickDraw, Turing Test) and group activities effectively to check student understanding during class.
Safe & Responsible Use
Students understand baseline safety but struggle when asked to apply safety principles to complex real-world digital scenarios.
Timetable Regularity
Major systemic challenge. Ad-hoc scheduling in free periods leads to fragmented learning and lower topic coverage across schools.
Career Aspirations
Lowest student score. While students can name tech job titles, they lack deeper clarity on what these careers involve and how AI links to them.
Tea Break
11:00 AM - 11:10 AM
Curriculum Framework + Exploration
Mentimeter: Palasa
6264 0139
5701 2043
Let’s Discuss:
What did Grade 7 help us understand?
1.
AI is not the same as all digital technology.
2.
AI is human-created and human-led.
3.
A correct or “smart” response does not automatically mean human-like intelligence.
Theme (Construct) | Grade 7 Outcome (Recognition) | Grade 8 Outcome (Analysis) | Grade 9 Outcome (Judgment & Agency) |
AI Spark & Futures Stories | Identify examples of AI in daily life and describe how technology has changed work in their community. | Analyze how AI is reshaping at least one industry and compare optimistic and cautious AI futures. | Evaluate multiple AI futures and articulate local and global consequences. |
AI Systems Discovery Labs | Distinguish between rule-based and AI systems; explain AI as “data + decision.” | Map an AI system (data - model - output - impact) and explain how changing data affects outcomes. | Critique limitations of AI systems and assess how data quality influences reliability. |
Bias, Ethics & Responsibility Studios | Recognize unfair AI outcomes and identify at least one responsible human role. | Analyze bias in a real AI tool and explain accountability across roles. | Apply ethical reasoning to real AI dilemmas and propose modifications to reduce harm. |
AI Design Quests | Identify simple situations where AI may not be appropriate and ask basic verification questions. | Justify whether AI should or should not be used in a given scenario, citing risks and benefits. | Decide whether AI should be built, modified, or rejected using ethical, technical, and contextual criteria. |
Responsible AI Hackathons | Modify a simple AI-generated output and participate in a guided mini design challenge. | Design a basic AI-supported solution to a local problem and explain reasoning behind design decisions. | Develop and present a structured AI solution prototype integrating ethical considerations and iterate after feedback. |
AI Portfolio & Reflection Loop | Maintain a basic AI portfolio and reflect on new insights about AI. | Curate portfolio artefacts showing progression and reflect on preferred roles in collaborative work. | Demonstrate longitudinal growth through portfolio synthesis and articulate personal stance on responsible AI use. |
“How is AI changing the world around us?”
What might that mean for the future?
THEN → NOW
Farming
HealthCare
Transport
Communication
Education
THEN → NOW
What changed for people?
Question
04
What role does AI/technology play?
Question
03
What is happening now?
Question
02
What was this like earlier?
Question
01
If today’s change continues, what might 2035 look like?�
Include: � 1 opportunity + 1 concern
Live from 2035:
Ethical way of usage
Let’s Reflect:
Grade 7 - Recognize AI:� �Where AI is used ?
Grade 8- �Analyse AI: � � What changed?
How are people affected?
Grade 9 - �Judge & Shape AI: ��What future might follow? �Who benefits? �What could go wrong? �What should humans decide?
Understanding �FSTC
Activity:
Scenario 1:
Revathy – Choosing My Next Career Step
Revathy has worked for five years in a company changing rapidly due to Generative AI and automation. Some routine technical tasks are becoming less relevant. Her family wants her to stay because the job is secure, while her friends suggest an expensive technology course.
How can Revathy understand her current strengths, explore changing opportunities, and make a confident decision rather than reacting to pressure?
Activity:
Scenario 2:
Krishna – The Digital Jump
Krishna is part of a third-generation family retail business. Store footfall is declining, and the family wants to explore digital marketplaces, payments, social media, customer databases, low-code/no-code and AI tools. They are also concerned about privacy, fraud and cybersecurity.
How can Krishna and the family decide what technology they actually need, understand the risks, and transition to digital without losing their existing strengths?
Activity:
Scenario 3:
Hemalatha – The School Transport Puzzle
Hema latha's school team is trying to understand why students arrive late. Data about arrival times, routes, traffic, weather, breakdowns, stops and departure times is scattered across registers, WhatsApp messages and spreadsheets.
How should Hemalatha break the problem into smaller parts, identify patterns, determine what information matters, and test possible explanations before choosing a solution?
Let’s Discuss:
Debrief:
“Identify about three to five important skills, but be prepared to explain why those skills are needed.”
What is FSTC?
🔤 F = Future
📚 S = Skills for
🌟 T = Thriving
💼 C = Careers
Let’s Reflect:
“At this stage, learners are developing awareness: � of themselves, of technology, of changing opportunities and of the skills involved.”�
“Learners now move beyond recognising the skill. They begin using skills to understand problems, evaluate situations and respond to changing contexts.”�
“Here, the learner is expected to exercise greater agency: making reasoned choices, responding to uncertainty, adapting and thinking about how they can shape outcomes.”
Lunch
12:30 PM - 01:30 PM
Energizer
MT Identity –
Community Formation
Activity: Instructions
Posters:
Let’s Reflect:
“If your entire two-year journey as a nominated teacher were an OBJECT or a MOVIE, what would it be? and why?”
Activity: Story-telling
Instructions:
Individual Reflection : 10 mins
Think and write.
Day Closure:
What is one learning or one takeaway you are carrying with you from today?
DAY 2
‹#›
MT Training round 1
August
MT Training + Cascade training round 2
Hack to the Future , Recognition and reward to students and teachers and Top announcement
State level showcase event & Teacher recognition
HM Orientation�Cascade Training +�Curriculum delivery in classroom
September
QA Field team support to MT teachers , by school visits, 1 on 1 phone call mentoring, biweekly virtual calls
MT Journey - 2026 - 27
Join the journey�July�
AI Curriculum Vetted by SCERT
Intermediate Ideathon Process with selected schools
84 740 500 1569 100000
MT| Schools | Head Teachers | Teacher Trained | Students
MASTER TRAINER JOURNEY ROADMAP
July–October 2026
From selected teacher to facilitator, implementer and mentor
Selected MT →Prepared Learner→Cascade Facilitator→Classroom Implementer→Teacher Mentor
Purpose
The MT journey builds a teacher-led support system in which Master Trainers remain rooted in their own classrooms while helping cascade teachers implement the Critical AI and Data Literacy curriculum. Across these four months, MTs move from onboarding and preparation to facilitation, classroom implementation and sustained teacher support.
Journey at a glance
July - Join the journey
August - Train to facilitate
September - Train teachers & begin implementation
October - Mentor & monitor
Vizianagaram | Visakhapatnam | Srikakulam | Palasa |
27 | 10 | 34 | 13 |
👥 Confirmed Cohort Size 84 Master Trainers
📑 Official Communication Final MT list � communicated to the � State Education � Department
💬 Community Building WhatsApp onboarding & � continuous engagement � launched
JULY 2026 |
AUGUST 2026 |
01
PHASE 1: PRE-TRAINING READINESS
📋 Confirm Attendance
📝 Complete Pre-Form
📖 WhatsApp Pre-Reads
02
PHASE 2: MT PHYSICAL TRAINING ROUND 1
🎯 Curriculum Progression (G7–G9)�👩🏫 Learner Experience & Hands-on Practice
💡 Active Facilitation & Peer Feedback Loops
👥 MT Group Alignment for Cascade Trainings
03
PHASE 3: INITIAL CASCADE-TRAINING ACTION PLAN
📍 Blocks Selection & Training Dates
👥 Co-Facilitator & Teacher Groups Allocation
📑 Outline Thematics & Session Flow�🔄 Divide Facilitation vs. Coordination Roles
SEPTEMBER 2026 |
👨🏫 Cascade ��Teacher Training
Guide teachers on curriculum & implementation
🏫 Classroom ��Implementation
Roll out curriculum with your own students during September
📱 Ongoing ��WhatsApp Support
Encourage peer learning & �share key resources
OCTOBER 2026 | I Implement, Mentor and Support Teachers
Role shift: Cascade facilitator → classroom implementer and teacher mentor
With my own students
· Complete the first curriculum construct- First 2 chapters.
· Begin the next construct.
· Track lesson completion- Call Cascade Teachers, fill KOBO Form.
· ** Support student participation, project work and emerging Ideathon-Hackathon milestones.
· Use student work and classroom experience to strengthen my own facilitation.
With my pre-assigned cascade teachers
· Make at least one meaningful contact with every assigned teacher during the month.
· Use phone or WhatsApp; where possible, use both meaningfully.
· Confirm whether classroom implementation has started.
· Ask how many curriculum sessions have been completed.
· Understand challenges and support required.
· Escalate unresolved issues to the assigned PO immediately when urgent, or through the next scheduled support space when non-urgent.
WhatsApp support
· Share reminders.
· Respond to routine teacher questions.
· Encourage peer exchange.
Milestones
✓ Complete the first curriculum construct in my classroom and begin the next.
✓ Contact every assigned cascade teacher, confirm whether implementation has begun and escalate every unresolved issue.
Continuous Support Across July–October
WhatsApp engagement
WhatsApp engagement continues from July 2026 to June 2027.
· Onboarding and training preparation.
· Curriculum resources and reminders.
· Questions, troubleshooting and peer exchange.
· Celebration of progress and informal recognition.
Virtual support
Four fortnightly/bi-weekly calls across the two-month support period:
· Three curriculum or content-focused virtual support calls.
· One virtual support call for cascade-teacher progress review.
The final format and ownership of the review meeting will be communicated separately.
Recognition
· Informal recognition during July–October through WhatsApp appreciation, training spaces and programme interactions.
· Formal recognition after October, based on completion of MT training, facilitation of Cascade Teacher Training, and sustained support to cascade teachers and implementation progress.
Curriculum Exploration - 1
Https://www.menti.com/?code=86512380
PPPPPPPPPPPPA
MENTIMETER DAY 2 -PALASA
Turing Test:
WHAT DID YOU NOTICE?
Transition
So far, the machine depended on what humans had already provided.
“What will change when we give a machine data-examples instead of every answer?”
Learning Patterns:
2 → A 4 → A 6 → A
3 → B 5 → B 7 → B
8 → ?
REVEAL
A = Even numbers
B = Odd numbers
EXAMPLES → NOTICE A PATTERN → PREDICT A NEW CASE
So the machine says that
New Case 8 = Gorup A
Group �1
Group �2
Activity:
“ Did anyone tell you the rule?”
· How did you decide?
· What did the examples help you notice?
· How is this different from the Turing machine?
· Did more examples help?
· Could poor or limited examples lead to a poor prediction?
· Can a machine make a confident prediction and still be wrong?
Group �3
MACHINE VERSION 1- �Turing Machine
HUMAN
↓
RULE / PREPARED ANSWER
↓
MACHINE
↓
RESPONSE
MACHINE VERSION 2 - �Machine learning from Examples HUMAN ↓ EXAMPLES / DATA ↓ MACHINE FINDS A PATTERN ↓ PREDICTION ↓ UNFAMILIAR EXAMPLE ↓ MACHINE STRUGGLES/ MIS-CLASSIFIES ↓ MORE EXAMPLES GIVEN ↓ MACHINE PREDICTS CORRECTLY |
WHAT STAYED THE SAME? WHAT CHANGED?
VERSION 1: Turing Machine learns Rules → Gives Response
VERSION 2: Takes in Examples/Data → Recognizes Pattern → Makes Prediction
What stayed the same in both?
Where were humans involved?
FINAL KEY MESSAGE
VERSION 1: Turing Test Machine
Human instructions → Machine response
RULE BASED MACHINE
VERSION 2: MACHINE LEARNING From DATA (AI Systems)
Examples/Data → Pattern → Prediction
DATA BASED MACHINE
In both, humans design, provide information and check the system.
Curriculum Exploration - 2
Activity:
Scenario:
YOUR SCHOOL IS OFFERED AN AI LEARNING ASSISTANT
Benefits: Personalised questions; language translation; identifies learning gaps.
Concerns: Unequal device access; may give wrong explanations; collects performance data; recommendations are not fully transparent.
Activity:
Scenario:
YOUR SCHOOL STUDENTS WANTS TO USE AN AI ART & WRITING GENERATOR FOR COMPETITION
Benefits: Helps students who struggle with drawing or writing create professional-looking artwork and polished stories quickly; saves production costs and time.
Concerns: AI models are trained on real artists’ and writers’ work without permission; student artists might feel demotivated; generated content might lack authentic voice and emotional depth.
Activity:
Scenario:
A LOCAL YOUTH CENTER PLANS AN AI RESUME & INTERVIEW COACH FOR JOB SEEKERS
Benefits: Gives tailored feedback on job applications; helps young adults practice interviews anytime; improves grammar and professional formatting
Concerns: Might suggest buzzwords that don't reflect the applicant's real skills; candidates without regular internet access lose out; employers' AI filters might reject AI-generated resumes.
Scenario: YOUR SCHOOL IS OFFERED AN AI LEARNING ASSISTANT (Time 5 mins)
Benefits: personalised questions; language translation; identifies learning gaps.
Concerns: unequal device access; may give wrong explanations; collects performance data; recommendations are not fully transparent.
Place 4 charts in the 4 corners of the room:
Corner 1 Chart: YES — USE IT
Corner 2 Chart: YES USE IT, BUT WITH CONDITIONS
Corner 3 Chart: NO — DO NOT USE IT AT ALL
Corner 4 Chart: WE CANNOT DECIDE, WE NEED MORE INFORMATION
Ask participants to move to the position that best reflects their judgement.
Let’s Reflect:
Prepare your reasons:
Each corner has 3 minutes.
They have to prepare:
1 strong reason for choosing that specific corner +
1 concern they have for not choosing any other corner
Shareout from each corner - Only one person will consolidate and share. Everyone else will listen to each other
Listen to others.
Use your judgement.
Shift corners if anything changed your mind
**If you move, be ready to say what changed your mind.
Now ask all participants
Which benefit of using new AI Technology (in this case the AI Assistant) was worth accepting?
What problem does using AI solve?
Who benefits from using AI ?
What could go wrong?
Is anyone overlooked, excluded or harmed unintentionally/intentionally?
What safeguards are needed?
Then introduce trade-offs and human oversight:
gaining a benefit may involve a limitation, cost or risk that must be reduced, managed or sometimes rejected. Trade-off does not mean every risk is acceptable → Connect to Tamarind Truth Story
Big Picture Question:
So What responsibility do we have as humans in an AI-applied environment ?
access, language, infrastructure, learner needs and teacher capacity may change the decision.
Grade 7 Session 3 | AI Systems Discovery Labs | Can Machines Think? Exploring the Turing Test |
Grade 7 Session 4 | AI Systems Discovery Labs | How AI Learns: Rule-Based and Data-Based Systems |
Grade 8 Session 3 | AI Systems Discovery Labs | How Machines Learn Patterns |
Grade 8 Session 4 | AI Systems Discovery Labs | Computer Vision: Teaching AI to See |
Grade 8 Session 5 | AI Systems Discovery Labs | Natural Language AI: Beyond Chatbots |
Grade 9 Session 3 | AI Systems Discovery Labs | Inside an AI System: Data, Model, Output & Impact |
Grade 9 Session 4 | AI Systems Discovery Labs | Can We Trust AI? Reliability, Accuracy & Limitations |
Grade 9 Session 5 | AI Systems Discovery Labs | AI Failure Lab: When Good AI Goes Wrong |
Let’s Reflect:
Facilitation Sessions
Facilitation Habits:
“What facilitation habits build a classroom culture focused on deep conceptual understanding rather than rote memorization?”
FACILITATE OR TELL? - Quiz Mode
AGREE (Stand Up) if it is GOOD Facilitation
DISAGREE (SIT down) if it is NOT GOOD Facilitation.
1. If a student is confused , give the correct AI definition even before giving thinking prompt to students — DISAGREE
2. If students have different opinions during activity/discussion, resolve the disagreement immediately — DISAGREE
3. Ask students to for evidence before calling an AI output correct/verified — AGREE
4. In a discussion on ethics-related topic, every student in the class must reach one correct answer. — DISAGREE
Key insight
A facilitation strategy is not tied to one activity.
The same pedagogy can come across lessons, but its depth & details becomes deeper as learners progress from Grade 7 to Grade 8 to Grade 9.
Match the Following - This is an individual Activity
MATCH THE FACILITATION MOVE TO THE LESSON
A. G7 L1 — What is AI? AI Around Us
B. G7 L7 — Understanding Problems Around Us
C. G8 L2 — What Makes Good Data?
D. G8 L3 — How Machines Learn Patterns
E. G9 L2 — AI and Society: Opportunities, Risks & Trade-offs
F. G9 L6 — Who Is Responsible? Accountability in AI Systems
1. Examine the issue from developer, user, company, school and government perspectives
2. “Who benefits? What could go wrong? What conditions would change your decision?”
3. “Useful for what? What information is missing?”
4. Let learners predict, give more examples, then revise.
5. “What made you decide about AI?”
6..Keep asking “Why?” to move towards a root cause.
Suggested matching: A→5, B→6, C→3, D→4, E→2, F→1.
6 Helpful Facilitation Habits for 21st Century Pedagogy
1. Ask students for their answer before you explain — “What do you think?”
2. Test the reasoning given by students— “What made you say that?”
3. Ask the student for evidence when they make any claim— “What are you noticing?”
4. Dont give direct answers, ask more helpful questions. Keep uncertainty alive — “What more do we need to know?”
5. Disagreement is good, use it productively to guide Empathy and Equity — “Who sees it differently? Why?”
6. Dont just do an activity. Consolidate it and reflect to make learning deeper — “So what have we discovered?”
The facilitator’s job is not to remove thinking by giving answers too quickly.
It is to create the conditions in which learners can:
NOTICE → QUESTION → INVESTIGATE → REASON → JUDGE → ACT RESPONSIBLY.- 21st Century PEDAGOGY
MOCK SESSION
Min ClassRoom : 15 mins
Checklist for observers during facilitation. They can share out after activity is done:
Now Individual Reflection Wall for all:
Put up 3 charts with the above prompts give them 2 min. To write their responses on sticky notes and put it on the charts.
Mini Training Scene preparation (15 min.):
Say “So far we thought of facilitating a classroom, but you are also a Master Trainer who will be guiding the teachers, so now think if you have to facilitate the same activity in a teacher training rather than a classroom, what would you do differently?”
Take responses on the shift they will bring in a teacher training and note it down on a chart/board.
Consolidate
DAY 3
Check - In:
🔵 MY WALL OF STRENGTH
✏️ After attending the MT training sessions:
What are my new learnings that makes me confident as a Master Trainer to support my Cascade Teachers?
Curriculum Theme/Construct 3 :
Bias, Ethics & Responsibility Studios
Teacher training Outline | ||
Time | Day 1 – Curriculum Foundation + Experiencing the Curriculum | Day 2 – Facilitation + Preparing to Cascade |
9:00–9:30 | Registration, pre-training form + Cooking Pot Questions | Recap / Cooking Pot Questions |
9:30–10:00 | Introduction, Check-in, Norms & Agenda setting | Curriculum Exploration - Construct 3: Experience the curriculum as a learner |
10:00–10:30 | - About QA + state journey - Impact of AI Education / SSA context | |
10:30–11:00 | FSTC Connection to the curriculum – What skills are we building and why? | Curriculum Session Walkthroughs – Understand session flow, resources, key facilitator moves and likely learner challenges |
11:00–11:10 | ☕ Tea Break | ☕ Tea Break |
11:15–12:15 | Curriculum Exploration – Construct 1: Experience the curriculum as a learner + unpack the learning intent | Facilitation Practice – Mini Classroom: Practise selected curriculum activity as facilitator |
12:15 to 12:30 | Reporting forms: introduce the session completion forms and the support they will get from MTs | |
12:30–1:15 | Curriculum Exploration – Construct 2: Experience + unpack key concepts, activities and learner experience | - Commitment Circle - Post-training form - Group Photo |
1:15–2:00 | 🍱 Lunch | 🍱 Lunch |
2:00 to 2:15 | Energiser - Unplugged | Group Photo |
2:15–3:00 | Facilitation Strategies – From Telling to Enabling Thinking | |
3:00 to 3:30 | Day 1 Reflection + Cooking Pot – What do I understand? What do I still need to clarify? | |
Themes for Cascade Training:
Individual Reflection : 10 mins
Think and write.
Observation questions:
- what tools did the group use?
- was there space for participants to think?
- what is one thing you liked about the presentation.
Day Closure: MAKE A PLEDGE
What is one thing that I will do better as an MT from today?
GROUP PHOTO