AI Education

Goal:
Equip learners with enough understanding to confidently use and critically evaluate AI tools.
Level:
Basic
Target Audience:
Students, professionals, and community members with no prior AI background

Topics:

1. 1. A Brief History of AI
   2. What is AI and Its Many Branches
   3. How Machines Learn
   4. Generative AI & LLMs
   5. Fundamentals of Prompt Engineering
   6. Context Engineering
   7. Foundational Models and Tools
   8. Sample Use Cases

Goal:
Develop critical thinking around the social, ethical, and responsible use of AI.
Level:
Basic
Target Audience:
Students, professionals, policymakers, and community leaders

Topics:

1. 1. Bias, Misinformation, and Explainability
   2. Data Quality and Privacy
   3. Ethics & Responsible AI
   4. AI Policy, Governance, and Regulation (local + global trends)
   5. Human + AI Collaboration (augmented intelligence vs. replacement)
   6. AI’s Impact on Work, Society, and Education

Goal:
Empower learners with the practical skills to strategically integrate AI tools into their work to boost productivity and efficiency.
Level:
Intermediate
Target Audience:
Students, staff, and professionals seeking efficiency in their daily tasks

Topics:

1. 1. Introduction to AI or Productivity
   2. AI for Text and Writing
   3. Image and SmartArt Generation
   4. Slide and Presentation Generation
   5. Email Management
   6. Meeting Recording and Summarization 

Goal:
Equip educators with the knowledge and skills to strategically integrate generative AI into every phase of the teaching and learning process through effective prompt engineering and responsible instructional design.
Level:
Intermediate
Target Audience:
Educators and faculty at all levels

Topics:

1. 1. Pedagogical-Didactic Planning
   2. Class Design and Development
   3. Preparation of Assignments and Exams
   4. Class Delivery
   5. Student Support
   6. Assessment and Feedback
   7. Reflection and Improvement
   8. Ethical and Responsible AI Use 

Goal:
Introduce the Exploratory Data Analysis workflow using standard methods and its full development using GenAI tools.
Level:
Intermediate
Target Audience:
Undergraduate/graduate students and professionals entering data analysis

Topics:

1. 1. Introduction to Data Analysis, AI Tools, and Careers
   2. Effective Prompting and Communication with AI
   3. Data Analysis Workflow and Descriptive Data Analysis
   4. Data Visualization and Design Principles
   5. Data Storytelling and Dashboard Design
   6. Ethical and Responsible AI Use
   7. Final Data Analysis Project with AI Integration

Goal:
Introduce how to develop the basics of computer programming using AI copilots.
Level:
Intermediate
Target Audience:
Students new to programming, especially in engineering, computing, or related fields

Topics:

1. 1. Introduction to AI-Assisted Coding Tools and Environment Setup
   2. Advanced Code Generation and Auto-completion Techniques
   3. AI-Powered Debugging and Error Resolution
   4. Code Refactoring and Optimization with AI
   5. IDE Integration and Ethical Considerations in AI Coding

Goal:
Identify real problems, design AI-enabled solutions, build an MVP, validate a scalable business model, and develop soft-skills.
Level:
Intermediate
Target Audience:
 Leaders, executives, policymakers, and senior professionals

Topics:

1. 1. AI Literacy for Founders (GenAI, LLMs, copilots, data/compute basics)
   2. Design Thinking for Rapid Ideation and Prototyping
   3. Lean Startup: Hypotheses, Experiments, and Build-Measure-Learn Loops
   4. Customer Discovery and Validation (interviews, surveys, insight synthesis)
   5. Value Proposition and Business Model Canvas (channels, revenue, costs)
   6. AI-Assisted Product Development (prompting, workflow automation, no-/low-code)
   7. MVP Planning and Iteration (scope, sprints, feedback cycles, usability)
   8. Data Strategy, Privacy, and Responsible/Legal Use of AI
   9. Go-to-Market Strategy (positioning, pricing, traction metrics, growth)
   10. Pitch Deck and Delivery (storyline, slides, demo, Q&A)

Goal:
Introduce the Machine Learning (ML) workflow using standard methods and its full development using GenAI tools.
Level:
Advanced
Target Audience:
Students and professionals with prior knowledge of data analysis or programming

Topics:

1. 1.  Introduction to Machine Learning, AI Tools, and Careers
   2. Effective Prompting and Communication with AI
   3. ML Workflow and Data Preprocessing
   4. Supervised Learning: Classification and Regression
   5. Unsupervised Learning and Advanced ML Techniques
   6. Model Evaluation
   7. Ethical and Responsible AI Use
   8. Final ML Project with AI Integration