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GD INFOTECH • Beginner to Professional
AI Professional Course
A 20-week, teacher-ready programme that moves from clear foundations to real AI projects. Every module includes explanation, examples, classroom activity, individual practice and a quick knowledge check.
20 weeks14 modules6 portfolio projects
Course design
One complete learning path—without unnecessary difficulty.
Two 90-minute teaching sessions plus one 60-minute lab each week, with guided practice and reflection.
Browser-based AI tools, Google Colab/Jupyter, Python, pandas, scikit-learn and Keras. Equivalent current tools may be used.
Quizzes 15%, practice 25%, mini-projects 30%, capstone 20%, responsible-AI viva and portfolio 10%.
By the end, learners can:
✓ Explain core AI, ML, deep-learning and generative-AI ideas✓ Write, test and improve professional prompts✓ Use Python and prepare data for analysis✓ Train and evaluate simple ML and neural-network models✓ Build NLP, vision, RAG and automation prototypes✓ Apply privacy, fairness, safety and human-oversight checks✓ Present a documented portfolio and capstone
Complete curriculum
Open any module for the full teaching plan.
01Week 1AI Foundations & Problem Framing
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AI Foundations & Problem Framing
Simple explanation
Artificial intelligence means making computers perform tasks that normally need human intelligence. Narrow AI solves one defined task; machine learning learns patterns from examples; generative AI creates new content. A good AI project begins by defining the problem, user, input, output and success measure.
Practical examples
- A spam filter labels email as spam or safe.
- A school assistant answers questions from an approved timetable.
Classroom activity
Sort 12 familiar applications—calculator, face unlock, maps, chatbot and others—into rule-based software, predictive AI or generative AI. Discuss borderline cases.
Student practice
Choose one school or small-business problem. Write: user, problem, available input, desired output, success measure and one non-AI alternative.
Quick quiz
- Which type of AI creates new text or images?
- Why should a problem be framed before selecting a tool?
Check answers
Generative AI; because the user, evidence, desired output and success criteria determine whether AI is suitable.
02Week 2Generative AI, LLMs & Multimodal Models
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Generative AI, LLMs & Multimodal Models
Simple explanation
Generative models learn patterns in large datasets and produce likely new outputs. A large language model works with tokens, uses context and predicts useful continuations. Multimodal models can work across text, images, audio or video. Their output can sound confident without being correct, so important facts need verification.
Practical examples
- Turn rough class notes into a clear revision sheet.
- Upload a product photo and ask for an accessible description, then check it manually.
Classroom activity
Ask the same factual question with little context and with clear source material. Compare accuracy, relevance and unsupported claims.
Student practice
Create a one-page comparison of predictive AI and generative AI with purpose, input, output, strength and risk.
Quick quiz
- What is a token?
- What is a hallucination in generative AI?
Check answers
A word, subword or character unit processed by a language model; an unsupported or incorrect output presented as if true.
03Weeks 3–4Prompt Engineering
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Prompt Engineering
Simple explanation
A strong prompt gives the model a role, task, context, constraints, examples and output format. Start clear, inspect the result, then refine. Zero-shot prompting gives instructions only; few-shot prompting includes examples. Complex work improves when it is divided into verifiable steps.
Practical examples
- Weak: ‘Explain photosynthesis.’ Strong: ‘Explain photosynthesis to Class 7 in 120 words, use one analogy and finish with three recall questions.’
- Give two sample enquiry rows and ask the model to classify a third into Hot, Warm or Cold using stated rules.
Classroom activity
Teams improve one vague prompt in three rounds. Score each output for accuracy, completeness, clarity and format compliance.
Student practice
Write prompts for summarising notes, creating a lesson plan and extracting a table. Add a checklist for checking each output.
Quick quiz
- What does few-shot prompting add?
- Name four useful parts of a professional prompt.
Check answers
Examples; any four of role, task, context, constraints, examples, audience and output format.
04Weeks 5–6Python Essentials for AI
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Python Essentials for AI
Simple explanation
Python is widely used in AI because it is readable and has strong libraries. Students learn variables, data types, conditions, loops, functions, lists, dictionaries, files, errors and notebooks before using NumPy and pandas.
Practical examples
- Use a loop to calculate the average of five quiz marks.
- Store student name, course and score in a dictionary, then print a formatted report.
Classroom activity
Human robot: one student follows only exact instructions while the class writes an algorithm for making tea. Connect sequence, condition and repetition to code.
Student practice
Build a marks analyser that accepts scores, calculates average, returns grade and identifies the highest score.
Quick quiz
- When would you use a list instead of one variable?
- What is the purpose of a function?
Check answers
To store an ordered collection; to package reusable instructions that can accept inputs and return an output.
05Week 7Data Literacy & Preparation
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Data Literacy & Preparation
Simple explanation
Data may be numerical, categorical, text, image or time-series. Before modelling, inspect its source, meaning and quality; handle missing values and duplicates; encode categories; scale when appropriate; and split data into training, validation and test sets without leakage.
Practical examples
- Standardise ‘Punjab’, ‘PB’ and ‘punjab’ before counting enquiries by state.
- Keep the final test set unseen while developing a pass-risk model.
Classroom activity
Give groups a deliberately messy attendance table. Ask them to mark missing, duplicate, inconsistent and impossible values and propose corrections.
Student practice
Load a CSV with pandas, show summary statistics, clean two quality issues and create one labelled chart.
Quick quiz
- Why is test data kept separate?
- What is data leakage?
Check answers
To estimate performance on unseen data; when information unavailable at prediction time improperly enters training.
06Weeks 8–9Machine Learning Fundamentals
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Machine Learning Fundamentals
Simple explanation
Supervised learning learns from labelled examples for classification or regression. Unsupervised learning finds patterns such as clusters. A feature is an input; a label is the target. Students train simple linear regression, logistic regression, decision-tree and clustering models and compare them with a baseline.
Practical examples
- Regression estimates house price from area and location.
- Classification predicts whether an enquiry is likely to convert.
Classroom activity
Students become a decision tree: ask yes/no questions to classify classroom objects, then discuss which questions split the objects best.
Student practice
Train one classifier with scikit-learn. Report the features, target, train/test split, baseline and result.
Quick quiz
- Classification or regression: predicting monthly sales amount?
- What is a feature?
Check answers
Regression; an input variable used by the model to make a prediction.
07Week 10Evaluation, Generalisation & Improvement
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Evaluation, Generalisation & Improvement
Simple explanation
A useful model must generalise beyond training examples. Overfitting means memorising training details; underfitting means the model is too simple. Classification is evaluated with a confusion matrix, precision, recall and F1; regression commonly uses MAE or RMSE. The right metric depends on the real cost of each error.
Practical examples
- For disease screening, missing a positive case may make recall especially important.
- Compare a sales model’s MAE with the simple baseline of always predicting the average.
Classroom activity
Use 20 paper predictions to build a confusion matrix physically, then calculate precision and recall together.
Student practice
Evaluate two models with at least two metrics and explain which should be selected for the stated use case.
Quick quiz
- What is overfitting?
- Why can accuracy be misleading with imbalanced classes?
Check answers
Strong training performance but weak unseen-data performance; a model can predict only the majority class and still appear accurate.
08Weeks 11–12Deep Learning & Neural Networks
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Deep Learning & Neural Networks
Simple explanation
A neural network connects layers of small mathematical units. Weights control signal strength, activation functions add non-linearity, loss measures error and backpropagation adjusts weights. CNNs are useful for images; transformers use attention and power many modern language and multimodal systems.
Practical examples
- A small network learns handwritten digit classes from pixels.
- Attention helps a language model connect a pronoun with the relevant earlier word.
Classroom activity
Create a paper network: students pass numbered signals through input, hidden and output layers using simple weights and an activation rule.
Student practice
Use a guided Keras notebook to train a small network, plot training and validation loss and identify possible overfitting.
Quick quiz
- What does a loss function measure?
- Why are activation functions used?
Check answers
Prediction error; they let networks learn non-linear relationships.
09Week 13Natural Language Processing
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Natural Language Processing
Simple explanation
NLP helps computers work with human language. Core tasks include text classification, sentiment, entity extraction, translation, summarisation and question answering. Text is tokenised and represented numerically; embeddings place related meanings closer in vector space.
Practical examples
- Classify student feedback as positive, neutral or negative.
- Extract names, dates and course titles from admission enquiries.
Classroom activity
Students label 15 short messages, compare disagreements and write a clear labelling guide—showing why good training data needs consistent definitions.
Student practice
Build a basic text classifier or use a pretrained pipeline; test it on slang, spelling mistakes and mixed-language examples.
Quick quiz
- What is an embedding?
- Name two NLP tasks.
Check answers
A numeric representation that captures useful relationships; any two such as classification, extraction, translation, summarisation or question answering.
10Week 14Computer Vision & Multimodal AI
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Computer Vision & Multimodal AI
Simple explanation
Computer vision extracts meaning from images or video. Classification assigns a label, object detection locates items and segmentation labels pixels. Image quality, lighting, camera angle and representative datasets strongly affect performance.
Practical examples
- Classify a leaf image by disease category.
- Detect helmets in a workplace image and draw a box around each person.
Classroom activity
Show images under different crops and lighting. Students predict which changes will confuse a model and explain why.
Student practice
Test a pretrained vision model on 20 ethically sourced images; record correct, incorrect and uncertain outputs and analyse patterns.
Quick quiz
- How does detection differ from classification?
- Name one source of vision-model error.
Check answers
Detection also locates objects; examples include poor lighting, unusual angles, low resolution or unrepresentative training data.
11Week 15AI Tools for Study, Content & Productivity
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AI Tools for Study, Content & Productivity
Simple explanation
Professional AI use is a workflow: define the task, choose a suitable text/image/audio/data tool, protect sensitive information, produce a draft, verify it and improve it. Tool names change, so students focus on transferable evaluation skills rather than memorising buttons.
Practical examples
- Create a lesson outline, verify facts from the textbook and revise for the class level.
- Generate three poster concepts, check spelling and licensing, then refine one in a design editor.
Classroom activity
Tool comparison lab: run the same approved task in two available tools and score usability, evidence, control, privacy and output quality.
Student practice
Complete one productivity workflow and submit the original input, prompt history, final output, verification notes and disclosure statement.
Quick quiz
- Why should students save prompt history?
- What information should never be pasted into an unapproved public AI tool?
Check answers
For reproducibility and improvement; private, confidential, identifying or restricted information.
12Weeks 16–17Automation, APIs, RAG & Agents
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Automation, APIs, RAG & Agents
Simple explanation
Automation connects triggers, data and actions. An API lets software request a service. Retrieval-augmented generation (RAG) finds relevant approved documents before an LLM answers. An agent can plan steps and use tools, but needs limits, logging, testing and human approval for important actions.
Practical examples
- When a form arrives, classify the enquiry, draft a reply and require staff approval before sending.
- A notes assistant retrieves the relevant policy paragraph and answers with a citation.
Classroom activity
Design an automation on cards: trigger → validation → AI step → decision → human approval → action → log. Teams identify failure points.
Student practice
Build a safe no-code workflow or a simple Python/API prototype using sample data. Include an error path and approval checkpoint.
Quick quiz
- What extra step distinguishes RAG from an ordinary chatbot prompt?
- Why should a high-impact action require human approval?
Check answers
Retrieving relevant trusted information; to catch errors and preserve accountability before consequences occur.
13Week 18Responsible AI, Safety & Governance
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Responsible AI, Safety & Governance
Simple explanation
Responsible AI addresses fairness, reliability and safety, privacy and security, inclusiveness, transparency and accountability. Teams identify possible harms, test across relevant groups, minimise data, document limitations, provide human oversight and monitor the system after release.
Practical examples
- Audit whether an admissions model rejects one group more often because historical data contains bias.
- Label AI-generated content and give users a way to challenge an incorrect decision.
Classroom activity
Red-team a fictional school chatbot: one group tries confusing or unsafe prompts; another records risks, safeguards and remaining limitations.
Student practice
Write a model card containing purpose, intended users, data, metrics, limitations, prohibited uses, human oversight and monitoring plan.
Quick quiz
- Name three responsible-AI principles.
- What should happen after an AI system is deployed?
Check answers
Any three of fairness, safety, privacy, inclusiveness, transparency and accountability; monitor performance, harms and feedback and update controls.
14Weeks 19–20Deployment, Portfolio & Career Readiness
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Deployment, Portfolio & Career Readiness
Simple explanation
A professional project is more than a model. It includes a clear user problem, reliable data flow, usable interface, testing, privacy and safety controls, documentation, monitoring and a demonstration that explains both value and limitations.
Practical examples
- Deploy a Streamlit predictor with input validation and a limitations panel.
- Present a RAG assistant that cites its source and refuses when evidence is missing.
Classroom activity
Project clinic: peers test each prototype using normal, edge-case and misuse scenarios, then give evidence-based feedback.
Student practice
Publish a portfolio case study: problem, users, data, method, evaluation, responsible-AI review, screenshots, demo and next improvements.
Quick quiz
- Why is monitoring needed after launch?
- What evidence makes a portfolio project credible?
Check answers
Real data and user behaviour can change; clear methods, evaluation results, testing, limitations and a working demonstration.
Full weekly teaching content
Exactly what to teach, demonstrate and practise each week.
Each week contains two guided teaching sessions and one practical lab. Open a week and follow the complete classroom sequence.
WEEK 01Understanding AI Around Us
Open lesson +
Class 1 — Understand
- ✓ AI, algorithms, rules and learning
- ✓ Narrow, predictive and generative AI
Class 2 — Apply
- ✓ Problem framing: user, input, output and success
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Compare a calculator, spam filter and chatbot. Trace input → processing → output.
Student assignment
Write an AI opportunity brief for a school or local business.
Weekly assessment
Define AI and give one suitable and one unsuitable use.
WEEK 02How Generative AI and LLMs Work
Open lesson +
Class 1 — Understand
- ✓ Training data, patterns, tokens and parameters
- ✓ Next-token prediction, context and multimodal models
Class 2 — Apply
- ✓ Hallucinations, limitations and fact verification
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Predict the next word in a sentence, then change the context and compare.
Student assignment
List five GenAI strengths and five risks with examples.
Weekly assessment
Quiz: tokens, context, multimodal input, hallucination and verification.
WEEK 03Prompt Engineering Foundations
Open lesson +
Class 1 — Understand
- ✓ Role, task, context, audience and constraints
- ✓ Output formats, delimiters and reference material
Class 2 — Apply
- ✓ Zero-shot and few-shot prompting
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Turn ‘make notes’ into a precise Class 10 revision prompt.
Student assignment
Create and test five reusable study or office prompts.
Weekly assessment
Find missing parts in three weak prompts and rewrite one.
WEEK 04Advanced Prompting and Evaluation
Open lesson +
Class 1 — Understand
- ✓ Prompt chains and task decomposition
- ✓ Critique, revise and rubric-based evaluation
Class 2 — Apply
- ✓ Factual, citation and format checking
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Draft a lesson plan, score it with a rubric and revise weak parts.
Student assignment
Submit three prompt versions, scored outputs and reflection.
Weekly assessment
Choose the safest prompt and verification method in four scenarios.
WEEK 05Python Basics
Open lesson +
Class 1 — Understand
- ✓ Colab/Jupyter, variables, strings and numbers
- ✓ Input, operators and formatted output
Class 2 — Apply
- ✓ Conditions, loops, indentation and debugging
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Build a valid-range marks and grade calculator.
Student assignment
Create a bill calculator, number classifier and multiplication table.
Weekly assessment
Predict five code outputs and correct two errors.
WEEK 06Python Functions and Data Structures
Open lesson +
Class 1 — Understand
- ✓ Lists, tuples, sets and dictionaries
- ✓ Functions, parameters and return values
Class 2 — Apply
- ✓ Exceptions and text-file handling
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Store student records, calculate averages and save a report.
Student assignment
Build a contact book with add, search and display.
Weekly assessment
Coding check on collections, functions and files.
WEEK 07Data Literacy and Preparation
Open lesson +
Class 1 — Understand
- ✓ Rows, columns, features, labels and data types
- ✓ Missing values, duplicates, outliers and encoding
Class 2 — Apply
- ✓ Scaling, train/validation/test split and leakage
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Clean inconsistent city names and record each decision.
Student assignment
Submit cleaned data, before/after summary and one chart.
Weekly assessment
Diagnose five data problems and explain why test data stays unseen.
WEEK 08Supervised Machine Learning
Open lesson +
Class 1 — Understand
- ✓ ML workflow and baseline models
- ✓ Linear regression and regression error
Class 2 — Apply
- ✓ Classification, logistic regression and decision trees
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Predict pass risk using sample attendance and study hours.
Student assignment
Report problem, features, label, split, model and result.
Weekly assessment
Choose regression or classification for six situations.
WEEK 09Unsupervised Learning and Recommendations
Open lesson +
Class 1 — Understand
- ✓ Similarity, distance and k-means clustering
- ✓ Interpreting clusters without inventing labels
Class 2 — Apply
- ✓ Content-based and collaborative recommendations
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Cluster paper data points manually and compare with a plot.
Student assignment
Design a recommender including signals and cold-start risk.
Weekly assessment
Explain why a cluster is not automatically a meaningful label.
WEEK 10Model Evaluation and Improvement
Open lesson +
Class 1 — Understand
- ✓ Confusion matrix, accuracy, precision, recall and F1
- ✓ MAE and RMSE for regression
Class 2 — Apply
- ✓ Overfitting, underfitting, validation and tuning
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Calculate precision and recall from 20 paper predictions.
Student assignment
Select a model using two metrics and one stated limitation.
Weekly assessment
Interpret a confusion matrix and choose metrics for three cases.
WEEK 11Neural Network Foundations
Open lesson +
Class 1 — Understand
- ✓ Neurons, weights, bias and forward pass
- ✓ Activation functions and loss
Class 2 — Apply
- ✓ Backpropagation, epochs and batches
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Pass numbered signals through a paper neural network.
Student assignment
Label a network and explain training in six simple steps.
Weekly assessment
Match eight neural-network terms with meanings.
WEEK 12Deep Learning Architectures
Open lesson +
Class 1 — Understand
- ✓ CNN filters, feature maps and image hierarchy
- ✓ Attention and transformer concepts
Class 2 — Apply
- ✓ Pretrained models and transfer learning
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Use transparent grids to show how filters detect edges and shapes.
Student assignment
Compare CNN and transformer uses and limitations.
Weekly assessment
Select a suitable architecture idea for four tasks.
WEEK 13Natural Language Processing
Open lesson +
Class 1 — Understand
- ✓ Text cleaning, tokens, vectors and embeddings
- ✓ Classification, sentiment and entity extraction
Class 2 — Apply
- ✓ Translation, summarisation and question answering
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Arrange related word cards to model embedding space.
Student assignment
Test 20 varied messages and write an error analysis.
Weekly assessment
Identify the NLP task used for six example outputs.
WEEK 14Computer Vision and Multimodal AI
Open lesson +
Class 1 — Understand
- ✓ Pixels and labels
- ✓ Classification, detection and segmentation
Class 2 — Apply
- ✓ OCR, captioning and multimodal prompting
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Show whole-image labels, object boxes and pixel regions on one photo.
Student assignment
Test 20 images and record conditions, correctness and uncertainty.
Weekly assessment
Compare the three main vision tasks and sources of error.
WEEK 15Professional AI Tools
Open lesson +
Class 1 — Understand
- ✓ Text, image, audio, coding, research and data tools
- ✓ Tool selection by task, privacy and copyright
Class 2 — Apply
- ✓ Verification and AI-use disclosure
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Create a teaching resource and verify every factual claim.
Student assignment
Submit prompt history, draft, verification log and final version.
Weekly assessment
Complete tool-choice, privacy and copyright scenarios.
WEEK 16Automation and APIs
Open lesson +
Class 1 — Understand
- ✓ Triggers, actions, conditions, branches and logs
- ✓ API requests, responses, JSON and key safety
Class 2 — Apply
- ✓ Validation, error paths and human approval
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Map form → validate → classify → draft → approve → send → log.
Student assignment
Build a no-code or Python workflow with a failure path.
Weekly assessment
Put seven automation steps in the safest order.
WEEK 17RAG and AI Agents
Open lesson +
Class 1 — Understand
- ✓ Documents, chunks, embeddings and retrieval
- ✓ Cited generation and evidence-based refusal
Class 2 — Apply
- ✓ Agent goals, tools, permissions and stopping rules
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Retrieve an approved paragraph, cite it and refuse if evidence is absent.
Student assignment
Write allowed actions, prohibited actions, approvals and logs for an agent.
Weekly assessment
Trace a RAG pipeline and find four unsafe agent permissions.
WEEK 18Responsible AI and Governance
Open lesson +
Class 1 — Understand
- ✓ Fairness, safety, privacy, inclusion and transparency
- ✓ Harm mapping, red-teaming and safeguards
Class 2 — Apply
- ✓ Human oversight, documentation and monitoring
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Compare group outcomes and discuss data and process causes.
Student assignment
Create a model card with purpose, data, metrics and limits.
Weekly assessment
Recommend safeguards and responsible owners for three cases.
WEEK 19Building and Deploying the Capstone
Open lesson +
Class 1 — Understand
- ✓ Interface, validation, architecture and accessibility
- ✓ Testing normal, edge and misuse cases
Class 2 — Apply
- ✓ Logs, drift, feedback, monitoring and versioning
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Add validation, uncertainty, fallback and limitations to a prototype.
Student assignment
Complete prototype, test table and responsible-AI checklist.
Weekly assessment
Teacher checkpoint on function, evidence, safety and usability.
WEEK 20Portfolio, Presentation and Career Readiness
Open lesson +
Class 1 — Understand
- ✓ Portfolio case study, README and demonstration
- ✓ Explaining technical choices and limitations
Class 2 — Apply
- ✓ AI roles, interview stories and continued learning
- ✓ Guided examples and teacher questioning
Practical lab demonstration
Present problem → users → method → evidence → risks → demo.
Student assignment
Publish the case study and a personal 90-day learning plan.
Weekly assessment
Final viva on choices, evaluation, safety and lessons learned.
Practical portfolio
Projects that prove what the learner can do.
AI Study Assistant
Turn approved notes into summaries, flashcards and quizzes; add a fact-check checklist.
Student Performance Explorer
Clean sample data, visualise patterns and train an interpretable risk model with fairness checks.
Enquiry Classifier
Categorise sample admission messages and evaluate errors across short, noisy and mixed-language text.
Image Classifier
Train or adapt a small classifier, document dataset consent and analyse failure cases.
Cited Notes Assistant
Answer only from approved course notes, show sources and refuse unsupported questions.
Human-approved Workflow
Classify a request, draft an action, pause for approval and log the final result.
FINAL CAPSTONE
Build a useful AI solution for a real local problem.
Teams discover a need, define success, prepare ethical data, create a prototype, evaluate quality and risks, add human oversight, document limitations and present a live demonstration.
- Problem brief & user research
- Working prototype
- Evaluation report
- Responsible-AI checklist
- Model/system card
- 5-minute presentation
Teacher support
A repeatable lesson structure for every class.
Connect
Begin with a familiar situation.
Explain
Teach one idea in plain language.
Demonstrate
Show a worked example live.
Practise
Let students complete a guided task.
Check
Use the quiz and exit ticket.
Teacher preparation checklist
- Test the tool and backup activity before class.
- Use only non-sensitive sample data.
- Explain what evidence students must save.
- Require verification, citation and AI-use disclosure.
- Support mixed ability with pair work and extension tasks.
- End by asking what failed, why, and how it could be safer.
Curriculum basis
Aligned with reliable, current learning guidance.
The sequence was researched and adapted for classroom use from established learning programmes and official guidance. Tool-specific features may change; the concepts, evaluation habits and responsible-use practices remain transferable.
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