What you will learn
What this course delivers
Teach the Classes 9–10 AI curriculum accurately and responsibly
This Practitioner course prepares teachers to teach, assess and supervise the Classes 9–10 AI curriculum — foundations, the AI Project Cycle, data, modelling, evaluation, generative AI, computer vision, NLP, Python and ethics — and to graduate with a complete AI capstone portfolio.
Mapped to the current CBSE AI (Code 417) curriculum for Classes IX–X and adaptable to other boards — with a curriculum-alignment matrix, not vague claims of approval.
The full pipeline done accurately: the AI Project Cycle, data and statistics, modelling, the confusion matrix and precision/recall/F1, computer vision, NLP and Python — with validated, runnable examples.
Responsible AI is a continuous layer: verification and hallucination, no real student data in public tools, no profiling or surveillance of children, and a human accountable for every AI use.
Every module produces a classroom-ready artefact, and the course ends in a complete, rubric-scored AI capstone portfolio you can supervise your own students through.
What you'll build
You graduate with a reviewed Classes 9–10 AI Capstone Portfolio — sixteen components documenting a complete, responsible AI Project Cycle — scored on a sixteen-criterion analytic rubric.
Course Syllabus
Orient to the course, take the diagnostic, and build an accurate foundation: what AI is and is not, the three major AI domains (data, computer vision, NLP), AI's real capabilities and limitations, common misconceptions, and how to teach these to Classes 9–10 — CBSE-oriented and without presenting AI as human or conscious.
Learning Outcomes
- Define AI accurately, distinguish it from conventional automation, and state its capabilities and limitations without anthropomorphising it (Class IX AI basics).
- Describe the three major AI domains — data, computer vision and natural language processing — with real applications (Class IX/X domains).
- Correct common misconceptions about AI and plan an age-appropriate way to teach AI foundations to Classes 9–10 (teacher competency).
Lessons
Course Orientation and Diagnostic
Objective: Navigate the course, accept the responsible-use rules, and use the ungraded diagnostic to plan where to focus.
What AI Is — and What It Is Not
Objective: Define AI accurately, distinguish it from conventional automation, and state its core limitations without anthropomorphising it.
The Three Major AI Domains
Objective: Describe the three major AI domains — data (statistical) AI, computer vision and natural language processing — with real applications.
AI Around Us: Capabilities, Limitations and Misconceptions
Objective: Identify AI in everyday life, state realistic capabilities and limitations, and correct common misconceptions with accurate explanations.
Teaching AI Concepts through Age-Appropriate Activities
Objective: Plan how to teach an AI foundations concept to Classes 9–10 using active, inquiry-based, differentiated methods rather than lecture alone.
Module Assessment
"AI or Not AI?" activity + AI-domain map + active lesson plan · 8 Questions
Visual Concepts
Comparison Chart
AI versus automation comparison
Flowchart
Interactive AI-domain map (data / vision / NLP)
Checklist
Real-life AI application gallery
Flowchart
Human–AI decision flow
Comparison Chart
Myth-versus-fact cards
Resources
AI vs automation reference (Teacher-facing)
Accurate, non-anthropomorphic definitions.
"AI or Not AI?" classification cards + answer key
10 systems, with teacher notes on borderline cases.
Three-domain AI map poster (data / vision / NLP)
Classroom wall poster with examples.
Myth-vs-fact cards
Five common AI misconceptions and accurate corrections.
Course handbook + responsible-use acknowledgement
How the course works; the responsible-use agreement to accept.
Teach the AI Project Cycle and problem scoping the CBSE way — problem identification, the 4Ws canvas, stakeholder and system mapping, writing a project goal, and reviewing feasibility and ethics before any data or modelling.
Learning Outcomes
- Explain the stages of the AI Project Cycle and how it differs from a conventional project (CBSE Project Cycle).
- Facilitate problem scoping using the 4Ws canvas, stakeholder mapping and a clear goal statement.
- Guide an early feasibility and ethics review before committing to data collection or modelling.
Lessons
Understanding the AI Project Cycle
Objective: Describe the stages of the AI Project Cycle and explain why it is iterative rather than linear.
Identifying Meaningful Problems
Objective: Help students identify meaningful, appropriately scoped problems that AI can genuinely help address.
The 4Ws Problem Canvas
Objective: Use the 4Ws canvas (Who, What, Where, Why) to define a problem precisely before building anything.
Stakeholder and System Mapping
Objective: Map the stakeholders affected by a problem and the system around it to understand influence, needs and constraints.
Writing a Project Goal and Reviewing Feasibility and Ethics
Objective: Write a clear, measurable AI project goal and complete an early feasibility and ethics review before data collection.
Module Assessment
Problem-scoping pack (4Ws canvas + stakeholder map + system map + goal + ethics review) · 8 Questions
Visual Concepts
Cycle Diagram
Interactive AI Project Cycle
Comparison Chart
AI project cycle vs a conventional project
Flowchart
4Ws problem canvas
Flowchart
Stakeholder influence map
Cycle Diagram
Iterative feedback loop
Resources
AI Project Cycle poster
PDF classroom resource
4Ws problem canvas
PDF classroom resource
Stakeholder map template
PDF classroom resource
System map template
PDF classroom resource
Feasibility & ethics review checklist
PDF classroom resource
Teach data as the foundation of AI — data features, types, sources, reliability, sampling — while building strong privacy practice: consent, anonymisation, data minimisation, and never entering real student data into public AI tools.
Learning Outcomes
- Explain data features and types (structured/unstructured, qualitative/quantitative, primary/secondary) and evaluate data sources for reliability.
- Teach sampling, representation and how bias enters at data collection.
- Apply privacy, consent, anonymisation and data-minimisation rules to safe classroom data collection.
Lessons
Data and Data Features
Objective: Define data literacy, and distinguish data types — structured/unstructured, qualitative/quantitative, primary/secondary — and their features.
Data Sources and Acquisition
Objective: Identify appropriate data sources and safe acquisition methods, preferring approved open datasets and synthetic data.
Reliable versus Unreliable Data, Sampling and Representation
Objective: Judge data reliability and explain how sampling and representation determine whether conclusions and models are fair.
Privacy, Consent and Anonymisation
Objective: Apply privacy, consent, anonymisation and data-minimisation rules, and never enter real student data into public AI tools.
Detecting Bias and Poor-Quality Data
Objective: Detect bias, missing values and poor quality in a dataset, and explain how they propagate into unfair models.
Module Assessment
Privacy-safe classroom dataset + data dictionary · 8 Questions
Visual Concepts
Cycle Diagram
Data lifecycle
Flowchart
Safe-data decision tree
Checklist
Data-quality dashboard
Comparison Chart
Sampling-bias illustration
Comparison Chart
Structured versus unstructured data map
Comparison Chart
Anonymisation example
Resources
Data-source checklist
PDF classroom resource
Data dictionary template
PDF classroom resource
Data privacy checklist
PDF classroom resource
Safe-data decision tree
PDF classroom resource
Sampling & bias discussion guide
PDF classroom resource
Teach the exploration and math that AI rests on — descriptive statistics (mean, median, mode, range), basic probability, choosing the correct chart, spotting misleading graphs, and preparing data for modelling — through a spreadsheet data laboratory.
Learning Outcomes
- Explore data and compute descriptive statistics (mean, median, mode, range) and interpret distributions.
- Apply basic probability, choose the correct visualisation, and identify misleading graphs.
- Prepare and clean data for modelling using a spreadsheet, distinguishing correlation from causation.
Lessons
Exploring Data
Objective: Explore a dataset systematically — its shape, features, ranges and obvious patterns — before any analysis or modelling.
Statistics for AI
Objective: Compute and interpret mean, median, mode and range, and choose the right measure for a given dataset.
Probability in Real Life
Objective: Explain basic probability and connect it to how AI expresses uncertainty and confidence.
Choosing the Right Visualisation and Spotting Misleading Graphs
Objective: Choose the correct chart for a data type and question, and identify how graphs mislead.
Spreadsheet-Based Data Laboratory
Objective: Clean a dataset, compute descriptive statistics, build appropriate charts, and prepare the data for modelling using a spreadsheet.
Module Assessment
Spreadsheet data lab (clean data + descriptive stats + appropriate charts) · 8 Questions
Visual Concepts
Flowchart
Interactive chart chooser
Comparison Chart
Mean–median–mode visual
Cycle Diagram
Probability simulator
Comparison Chart
Distribution explorer
Comparison Chart
Misleading-chart comparison
Comparison Chart
Correlation-versus-causation illustration
Resources
Chart selection guide
PDF classroom resource
Descriptive-statistics worksheet
PDF classroom resource
Probability activity sheet
PDF classroom resource
Misleading-chart examples
PDF classroom resource
Spreadsheet data-lab workbook
PDF classroom resource
Teach modelling foundations accurately — models and representations, rule-based vs learning-based systems, the AI–ML–deep-learning relationship, the three learning types, classification/regression/clustering, decision trees, and neural networks through analogy — up to choosing an appropriate model.
Learning Outcomes
- Explain what a model is, and distinguish rule-based from learning-based systems and AI from ML and deep learning.
- Describe supervised, unsupervised and reinforcement learning, and classification, regression and clustering with examples.
- Explain decision trees and neural networks through analogy, and select an appropriate model for a task.
Lessons
What Is a Model?
Objective: Define a model as a simplified representation used to make predictions or decisions, and distinguish training from inference.
Rule-Based versus Learning-Based Systems
Objective: Distinguish rule-based systems (humans write the rules) from learning-based systems (rules are learned from data) and know when each fits.
AI, Machine Learning and Deep Learning
Objective: Explain the nested relationship between AI, machine learning and deep learning accurately.
Supervised, Unsupervised and Reinforcement Learning
Objective: Distinguish the three types of machine learning by what data and feedback each uses.
Classification, Regression, Clustering and Decision Trees
Objective: Distinguish classification, regression and clustering, and explain how a decision tree makes a prediction.
Neural Networks through Analogy, and Selecting a Model
Objective: Explain a neural network through analogy without overstating it, introduce overfitting, and select an appropriate model for a task.
Module Assessment
Paper decision tree + a simple no-code classification model · 8 Questions
Visual Concepts
Comparison Chart
AI–ML–DL relationship diagram
Flowchart
Model-selection map
Flowchart
Interactive decision tree
Flowchart
Neural-network flow
Comparison Chart
Training-versus-inference illustration
Comparison Chart
Overfitting visualisation
Resources
Model-selection guide
PDF classroom resource
Decision-tree template
PDF classroom resource
AI/ML/DL reference card
PDF classroom resource
Learning-types comparison
PDF classroom resource
No-code model lab guide
PDF classroom resource
Teach model evaluation accurately — the train-test split, the confusion matrix, and accuracy, precision, recall and F1 — plus how to choose the right metric, analyse errors and fairness, and communicate a model's limitations honestly.
Learning Outcomes
- Explain why models must be evaluated and how the train-test split works.
- Read a confusion matrix and calculate accuracy, precision, recall and F1 correctly.
- Select the right metric for a context, analyse errors and fairness, and communicate model limitations.
Lessons
Why Models Must Be Evaluated, and the Train-Test Split
Objective: Explain why evaluation is essential and how the train-test split gives an honest estimate of real-world performance.
Understanding the Confusion Matrix
Objective: Read a confusion matrix and identify true positives, false positives, true negatives and false negatives.
Accuracy, Precision, Recall and F1 Score
Objective: Calculate accuracy, precision, recall and F1 from a confusion matrix and state what each measures.
Selecting the Right Metric
Objective: Choose the appropriate evaluation metric for a context based on the cost of false positives versus false negatives and class balance.
Fairness, Error Analysis and Communicating Limitations
Objective: Analyse errors for fairness across groups and communicate a model's limitations honestly.
Module Assessment
Confusion-matrix practical (build + calculate metrics + interpret) · 8 Questions
Visual Concepts
Comparison Chart
Interactive confusion matrix
Flowchart
Metric calculator
Comparison Chart
False-positive versus false-negative scenario
Comparison Chart
Model comparison dashboard
Flowchart
Fairness-check workflow
Resources
Confusion matrix worksheet
PDF classroom resource
Metric formulas reference (accuracy/precision/recall/F1)
PDF classroom resource
Metric-selection guide
PDF classroom resource
Fairness-check workflow
PDF classroom resource
Error-analysis template
PDF classroom resource
Teach generative AI responsibly — how it differs from predictive AI, how it produces outputs, effective prompting, and the critical skills of verification, source-checking, spotting synthetic media and deepfakes, copyright and AI-use disclosure — culminating in a teacher-reviewed classroom resource.
Learning Outcomes
- Explain generative AI, how it differs from predictive AI, and how it produces outputs by predicting likely content.
- Write effective prompts and verify outputs for accuracy, sources, bias and privacy.
- Identify synthetic or manipulated media, apply copyright and AI-use disclosure, and design a safe classroom workflow.
Lessons
Understanding Generative AI
Objective: Distinguish generative AI from predictive/conventional AI and describe what it can generate.
How Generative AI Produces Outputs
Objective: Explain, at an appropriate level, that generative AI predicts likely next content from patterns, and why that causes hallucinations.
Writing Effective Prompts
Objective: Write clear, structured prompts using role, task, context, constraints, examples and desired output format.
Checking Accuracy, Sources and Evidence
Objective: Verify generative output against reliable sources using a verification checklist and a source-quality ladder.
Synthetic Media, Deepfakes, Copyright and Disclosure
Objective: Identify synthetic or manipulated media, and apply copyright, attribution and AI-use disclosure rules.
Safe Classroom Workflows and a Teacher-Reviewed Resource
Objective: Design a safe classroom workflow for generative AI and produce a fully teacher-reviewed AI-assisted resource.
Module Assessment
Teacher-reviewed AI-assisted resource (prompt + draft + verification + corrections + disclosure) · 8 Questions
Visual Concepts
Flowchart
Prompt construction framework
Flowchart
Generation-versus-verification flow
Comparison Chart
Hallucination warning example
Checklist
Synthetic-media detection checklist
Timeline Visual
Source-quality ladder
Flowchart
Responsible-use decision tree
Resources
Prompt-design template
PDF classroom resource
AI output verification checklist
PDF classroom resource
Generative AI disclosure form
PDF classroom resource
Synthetic-media detection checklist
PDF classroom resource
Source-quality ladder
PDF classroom resource
Teach computer vision from the ground up — images as pixels, resolution, RGB and greyscale, features, the classification/detection/segmentation tasks, convolution and CNN concepts — and build and bias-test a privacy-safe no-code image classifier.
Learning Outcomes
- Explain how images are represented as pixels with resolution and colour (RGB/greyscale).
- Distinguish image classification, object detection and segmentation, and explain convolution and CNNs at an introductory level.
- Build a privacy-safe no-code image classifier and test it for bias and failure cases.
Lessons
Introduction to Computer Vision
Objective: Explain what computer vision is, its real applications, and its limitations, without overstating it.
Pixels, Resolution and Colour
Objective: Explain that images are grids of pixels, and how resolution, RGB and greyscale represent visual information as numbers.
Classification, Detection and Segmentation
Objective: Distinguish the three core computer-vision tasks — image classification, object detection and segmentation.
Image Features, Convolution and CNNs
Objective: Explain image features, what convolution does with a kernel, and how CNNs build up features at an introductory level.
Building a No-Code Image Classifier and Testing for Bias
Objective: Build a privacy-safe no-code image classifier, evaluate it, and test it for bias and failure cases.
Module Assessment
No-code image classifier (build + evaluate + bias/failure test) · 8 Questions
Visual Concepts
Comparison Chart
Pixel zoom explorer
Comparison Chart
RGB mixer
Flowchart
Convolution-kernel animation
Comparison Chart
Classification / detection / segmentation comparison
Flowchart
CNN layer diagram
Checklist
Model-confidence display
Resources
Computer vision laboratory guide
PDF classroom resource
Pixels & RGB reference
PDF classroom resource
Classification/detection/segmentation cards
PDF classroom resource
CNN concept diagram
PDF classroom resource
Vision bias & failure-test checklist
PDF classroom resource
Teach NLP from the ground up — how machines process text, tokenisation and normalisation, bag-of-words and keyword extraction, sentiment analysis, chatbots (scripted vs intelligent), and multilingual limitations and language bias — with a privacy-safe no-code text practical.
Learning Outcomes
- Explain how machines process text through tokenisation, normalisation and bag-of-words.
- Explain sentiment analysis, keyword extraction, and how chatbots differ from scripted bots.
- Recognise multilingual limitations and cultural/linguistic bias, and run a privacy-safe no-code text practical.
Lessons
Introduction to NLP and How Machines Process Text
Objective: Explain what NLP is, its applications, and why human language is hard for machines to process.
Tokenisation and Text Normalisation
Objective: Explain tokenisation and normalisation as the first steps of turning text into data a machine can use.
Bag of Words and Keyword Extraction
Objective: Explain the bag-of-words representation and keyword extraction, and their strengths and limits.
Sentiment Analysis, Chatbots and Conversational Systems
Objective: Explain sentiment analysis and distinguish scripted (rule-based) bots from AI-based conversational systems.
Multilingual NLP, Language Bias and a No-Code Practical
Objective: Recognise multilingual limitations and cultural/linguistic bias in NLP, and run a privacy-safe no-code text practical.
Module Assessment
No-code NLP practical (sentiment or text classification on approved data) · 8 Questions
Visual Concepts
Flowchart
NLP pipeline
Flowchart
Tokenisation animation
Comparison Chart
Bag-of-words matrix
Comparison Chart
Sentiment-score explanation
Comparison Chart
Scripted-bot versus AI-bot flow
Comparison Chart
Multilingual ambiguity examples
Resources
NLP laboratory guide
PDF classroom resource
Tokenisation & normalisation reference
PDF classroom resource
Bag-of-words worksheet
PDF classroom resource
Sentiment-analysis activity
PDF classroom resource
Multilingual bias discussion guide
PDF classroom resource
Teach the Python and no-code practice the CBSE curriculum expects — the notebook environment, variables, data types, operators, input/output, conditions, loops, lists, functions, reading CSV data, simple statistics and charts, reading images, debugging — with runnable, validated examples and clear guidance on when no-code is the better choice.
Learning Outcomes
- Set up and use the notebook environment and Python basics — variables, data types, operators, input/output, conditions, loops, lists and functions.
- Read CSV data, compute simple statistics and charts, and read/inspect images with validated, runnable examples.
- Debug and explain code, and decide when a no-code or low-code approach is the better choice.
Lessons
Python Environment and Notebooks
Objective: Set up and navigate the Jupyter Notebook environment and run a first validated cell of Python.
Variables, Data Types and Operators
Objective: Use variables, core data types (int, float, string, boolean) and arithmetic and comparison operators correctly.
Input, Output, Conditions, Loops and Lists
Objective: Use input/output, if–else conditions, for/while loops and lists to write simple, correct programs.
Functions and Reading CSV Data
Objective: Write reusable functions and read tabular data from a CSV file for analysis.
Statistics, Charts and Reading Images with Python
Objective: Compute simple statistics and build a basic chart in code, and read and inspect an image as pixel data.
Debugging, Explaining Code, and Code versus No-Code
Objective: Debug and explain a piece of code, and decide when a no-code or low-code approach is the better choice.
Module Assessment
Runnable, validated Python notebook (data → stats → chart) + debugging log · 8 Questions
Visual Concepts
Flowchart
Jupyter notebook anatomy
Comparison Chart
Variable as a labelled box
Flowchart
Loop execution trace
Flowchart
CSV-to-table flow
Cycle Diagram
Find → test → fix debugging loop
Comparison Chart
Code-versus-no-code decision guide
Resources
Python practical workbook
PDF classroom resource
Starter Jupyter notebooks
PDF classroom resource
Common errors & debugging guide
PDF classroom resource
Python cheat sheet (accessible formatting)
PDF classroom resource
Code-vs-no-code decision guide
PDF classroom resource
Teach AI ethics as a practical governance skill — human agency, fairness, accountability, transparency, privacy, safety, inclusion and accessibility — and connect AI to the Sustainable Development Goals through an age-appropriate AI impact assessment.
Learning Outcomes
- Explain why AI ethics matters and apply ethical principles — human agency, fairness, accountability, transparency, privacy, safety, inclusion.
- Analyse bias, exclusion, transparency and accountability in realistic AI scenarios and propose responsible actions.
- Connect AI projects to the SDGs and conduct an age-appropriate AI impact assessment.
Lessons
Why AI Ethics Matters, and Ethical Frameworks
Objective: Explain why AI ethics matters and apply core principles — human agency, fairness, accountability, transparency, privacy, safety, inclusion.
Bias, Fairness and Exclusion
Objective: Analyse how bias arises and causes exclusion, and evaluate fairness across affected groups.
Privacy and Child Safety
Objective: Apply privacy and child-safety rules to AI use in schools, including the firm limits on student data and profiling.
Transparency, Accountability, Accessibility and Inclusion
Objective: Apply transparency and accountability to AI decisions, and design AI use that is accessible and inclusive.
AI for the SDGs and Conducting an Impact Assessment
Objective: Connect AI projects to the Sustainable Development Goals and conduct an age-appropriate AI impact assessment.
Module Assessment
AI impact assessment for a proposed school AI project · 8 Questions
Visual Concepts
Flowchart
Ethics-principle wheel
Comparison Chart
Stakeholder-impact matrix
Comparison Chart
Benefits-versus-harms map
Checklist
Responsible-deployment checklist
Comparison Chart
Fairness scenario comparison
Flowchart
AI impact assessment flow
Resources
Ethics scenario cards
PDF classroom resource
Ethics-principle wheel
PDF classroom resource
AI impact assessment template
PDF classroom resource
Responsible-deployment checklist
PDF classroom resource
School AI-use checklist
PDF classroom resource
Bring the whole course together: select and scope a capstone problem, write a proposal with a data, privacy and ethics plan, build and evaluate a prototype, assemble the portfolio, and prepare for presentation and viva voce — plus a classroom implementation plan.
Learning Outcomes
- Select, scope and propose a capstone AI project with a data, privacy and ethics plan.
- Build and evaluate a prototype or classroom demonstration and gather evaluation evidence.
- Assemble the capstone portfolio and prepare for presentation, viva voce and classroom implementation.
Lessons
Selecting a Capstone Problem and Preparing the Proposal
Objective: Select and scope a capstone problem and write a proposal linking problem, stakeholders, SDG and approach.
Data, Privacy and Ethics Plan
Objective: Write a data plan with a data dictionary and a privacy-and-ethics review for the capstone project.
Prototype, Implementation and Evaluation
Objective: Build a prototype or classroom demonstration and evaluate it with appropriate evidence and honest limitations.
Preparing the Portfolio
Objective: Assemble the required capstone components into a coherent, well-documented portfolio.
Presentation, Viva Voce and Classroom Implementation
Objective: Prepare a capstone presentation and viva, write a reflective statement, and produce a classroom implementation plan.
Module Assessment
Capstone AI portfolio (proposal → prototype → evaluation → presentation → reflection) · 8 Questions
Visual Concepts
Timeline Visual
Capstone roadmap
Flowchart
Project proposal structure
Checklist
Portfolio completion checklist
Flowchart
Evaluation-evidence map
Flowchart
Presentation & viva structure
Checklist
Classroom implementation-plan template
Resources
Project proposal template
PDF classroom resource
Portfolio template
PDF classroom resource
Capstone rubric
PDF classroom resource
Viva voce question bank
PDF classroom resource
Classroom implementation plan
PDF classroom resource
Model card template
PDF classroom resource
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.