• Duration: 6 Sessions
• Sessions: 1 per week (60 minutes each)
• Learning Style: Hands-on, research-driven, project-based
• Capstone Project: Research, build, and deploy an AI assistant solving a real-world problem
• Best For: Students, entrepreneurs, and future AI creators

This course flows like an AI development journey, ensuring that each student understands every step of the process before building their final project.

Think of this as building AI step-by-step:

• Week 1: Understand AI history and research AI innovations
• Week 2: Learn how AI uses data and structure training data
• Week 3: Train AI to think, speak, and respond effectively
• Week 4: Build and design the AI assistant’s interface
• Week 5: Test, refine, and improve AI responses
• Week 6: Showcase and deploy AI assistant

Goal: Understand the evolution of AI assistants and research cutting-edge AI applications.

Tools Used:

• Perplexity and DeepSeek – AI-powered research and real-time fact-checking
• OpenAI (ChatGPT) – Conversational exploration of AI history

Key Topics:

• The AI Assistants That Started It All – From Ancient Cultures to ELIZA to Siri and ChatGPT
• AI’s Funniest Mistakes – When chatbots failed spectacularly
• Why AI Assistants Work the Way They Do – The science behind NLP and voice recognition
• Future of AI Assistants – How AI is shaping industries in 2025

Hands-On Activity:

• Use Perplexity to uncover rare AI history facts and create a timeline in ChatGPT.
• Debate Session: "What’s the best AI assistant of all time?"
• Team Challenge: Predict what AI assistants will look like in 2030.

Outcome: Students will grasp AI evolution and understand how technology impacts AI interactions today.

Goal: Learn how AI assistants use structured and unstructured data and how to prepare AI training sets.

Tools Used:

• Perplexity 
• DeepSeek 

Key Topics:

• What is Data? – The difference between structured data (tables, labeled text) and unstructured data (chat logs, audio, images).
• Where Does AI Get Its Knowledge? – How AI assistants pull from datasets, APIs, and real-time interactions to generate responses.
• Data Cleaning and Labeling – The importance of removing noise, categorizing responses, and structuring datasets for improved accuracy.
• Training AI with High-Quality Data – Why "garbage in, garbage out" is a key principle in machine learning and chatbot design.
• The Ethics of AI Data – Ensuring privacy, reducing bias, and maintaining transparency in AI training.

Hands-On Activity:

• Use Perplexity to analyze how chatbots like ChatGPT source and process data.
• Google Sheets Data Exercise:
• Sort and label chatbot training data into categories (questions, intent recognition, conversational pathways).
• Create "if-then" logic pathways based on structured responses.

Outcome: Students will understand how structured, well-organized data improves AI assistant responses.

Goal: Teach AI assistants how to understand questions, provide meaningful answers, and sound more human-like.

Tools Used:

• MindStudio – AI training and dataset management
• Re:Tune – Voice and personality training for AI assistants
• OpenAI API – Adjusting AI models for better response accuracy

Key Topics:

• How AI Understands Language – Breaking down NLP and machine learning
• Training AI to Answer Correctly – Fine-tuning prompts for better responses
• AI Personality and Voice Training – Making AI sound unique and engaging
• Common AI Mistakes and Fixing Them – How AI assistants misunderstand context

Hands-On Activity:

• Train an AI model in Open AI and MindStudio using real-world chatbot conversations.
• Use Re:Tune to make the AI assistant’s voice more expressive and human-like.
• Challenge: Program an AI assistant that can handle at least 20 different user requests.

Outcome: Students will train an AI assistant that can process user queries, understand intent, and respond intelligently.

Goal: Develop a fully functional AI assistant with custom UI and chatbot features.

Tools Used:

• Re:Tune – No-code AI chatbot building and UI prototyping
• OpenAI API – Integrating AI logic into assistants
• MindStudio – Exploring real-world AI chatbot automation

Key Topics:

• Designing AI Assistants for Businesses, Schools and Communities
• Using OpenAI API to Power Chatbots and Voice Assistants
• Customizing UI and Chat Flows with MindStudio
• Testing AI Assistants with Real User Inputs

Hands-On Activity:

• Build a chatbot interface in MindStudio and connect it to OpenAI API.
• Use Intercom’s chatbot testing tools to simulate real user interactions.
• Challenge: Develop an AI assistant that can handle customer service inquiries.

Outcome: Each student will have a fully functional AI assistant ready for real-world use.

Goal: Improve AI assistants by testing user interactions and refining responses.

Tools Used:

• Open AI – Real-world chatbot testing and feedback loops
• MindStudio – Fine-tuning chatbot responses
• Re:Tune – Adjusting AI assistant voice behavior

Hands-On Activity:

• Run real-world AI assistant tests using Intercom’s simulation tools.
• Improve AI assistant’s responses based on real-time user feedback.
• Challenge: Refine your AI assistant so it answers 90 percent of user requests correctly.

Outcome: Students optimize AI responses based on real user interaction.

Goal: Deploy AI assistants and present final projects to industry experts.

Final Tools Used:

• MindStudio – Showcasing AI chatbot interactions
• OpenAI API – Live chatbot demonstration
• Re:Tune – AI voice assistant showcase

Outcome: Students launch their AI assistants and present their projects to mentors and industry leaders.