FutureAI Industry Readiness Internship
6 comprehensive modules
1. Welcome to the AI world
In class: orientation, use cases, baseline quiz, and student AI career interests.
Student output: Starting quiz + AI interest card
2. AI vs ML vs GenAI
In class: Sort everyday examples; explain AI, ML, deep learning, and GenAI.
Student output: One-page AI concept map
3. Safe and fair AI
In class: Spot mistakes, bias, hallucinations, and privacy risks in prepared examples.
Student output: Five-point responsible-AI checklist
4. Design thinking
In class: Use 5W1H, empathy mapping, and a small community/SDG problem.
Student output: Two realistic project ideas and an empathy map
5. First Python notebook
In class: Open a school-approved notebook; print, run, save, and name files.
Student output: First executable notebook
END-OF-WEEK SUCCESS CHECK
Weekly check: Students can explain common AI ideas without jargon and can open, run and save their first code file.
1. Python essentials
In class: Variables, types, inputs, expressions, and printing results.
Student output: Small personal budget or marks calculator
2. Logic and debugging
In class: Conditions, loops, reading errors, and correcting simple bugs.
Student output: Short quiz or decision-making program
3. Functions and collections
In class: Lists, dictionaries, simple functions, and reusable code.
Student output: Useful mini-program using a function
4. Tables and CSV files
In class: Load and explore a public/synthetic CSV in Pandas.
Student output: Dataset exploration notebook
5. Make data reliable
In class: Find missing, duplicate, or inconsistent records using guided tools.
Student output: Cleaned CSV + change log
END-OF-WEEK SUCCESS CHECK
Weekly check: Students can explain their Python code, describe data columns and show what they cleaned.
1. Statistics and visuals
In class: Averages, variation, bar/line charts, and telling a data story.
Student output: Two labelled charts + conclusions
2. Query thinking
In class: SELECT, WHERE, and GROUP BY concepts on simple practice tables.
Student output: Three basic data-query exercises
3. How models learn
In class: Understand input features, output, training, testing, and success measures.
Student output: Machine-learning flow diagram
4. Regression lab
In class: Run a guided example predicting a number; discuss sensible limits.
Student output: Prediction notebook + short interpretation
5. Classification lab
In class: Use a prepared k-nearest-neighbours example to predict categories.
Student output: Classification output + error observation
END-OF-WEEK SUCCESS CHECK
Weekly check: Students connect a chart or prediction to a clear question and can explain that models can be wrong.
1. Clustering basics
In class: Group similar sample items using a visual or guided k-means activity.
Student output: Cluster figure and plain-English note
2. Evaluate an AI result
In class: Discuss incorrect predictions, sample metrics, data leakage, and fairness.
Student output: Model test-and-limitations card
3. Introduction to NLP
In class: Try text sorting, sentiment labels, or a rule-based chatbot.
Student output: Safe text or chatbot mini-lab
4. Inside Generative AI
In class: Understand LLMs, patterns, strengths, limits, and AI-created media.
Student output: Generative AI strengths/limitations sheet
5. Prompting with purpose
In class: Use context, examples, constraints, and formats; compare and verify results.
Student output: Before/after prompt worksheet
END-OF-WEEK SUCCESS CHECK
Weekly check: Students can improve a prompt and explain why they must verify an AI-generated answer.
1. Reliable, grounded answers
In class: Explore source-checking and the concept of retrieval-augmented generation (RAG).
Student output: Diagram of a source-grounded assistant
2. APIs, tools, and agents
In class: At the awareness level, explain tools, APIs, agents, and human approval.
Student output: Simple agent workflow with checkpoints
3. AI safety and cyber hygiene
In class: Recognize prompt injection, suspicious content, and unsafe data sharing.
Student output: Student-friendly AI risk checklist
4. Build and review like a team
In class: Introductions to file structure, README, Git concepts, testing, and code review.
Student output: Project README + two sample tests
5. Agree on the project
In class: Groups set user needs, scope, safe data, roles, and success criteria.
Student output: Mentor-approved capstone project charter
END-OF-WEEK SUCCESS CHECK
Weekly check: Every team has a realistic, approved project that can be demonstrated within the remaining sessions.
1. Build sprint 1
In class: Use approved starter data and templates to create a first working project.
Student output: Prototype version 0.1
2. Build sprint 2
In class: Improve the prototype with feedback and peer testing.
Student output: Prototype version 0.2 + feedback log
3. Quality and documentation
In class: Run test cases; write limitations, sources, ethics note and instructions.
Student output: Final prototype + README + test log
4. Career and presentation day
In class: Practise explaining the project and create a one-year learning roadmap.
Student output: Personal roadmap + presentation slides
5. Demo Day and final review
In class: Team showcase, viva, closing quiz and individual reflection.
Student output: Demo + viva + learning reflection
END-OF-WEEK SUCCESS CHECK
Weekly check: Students show what they built, explain their personal work, and identify the project’s real limitations.
Career certifications validate expertise in a specific field, enhancing credibility and job prospects.
