Onboarding session to orient participants to Moringa School’s learning environment, ensure they are fully set up with the required platforms and tools, and establish a foundation for successful engagement in the course. Includes a Digital Safety Checklist covering what not to upload, how AI platforms use submitted content, and basic data hygiene for cloud-based AI tools.

Objective: Understand AI fundamentals, the evolution of the field, and how large language models generate text. Gain proficiency in generative AI basics, prompt engineering strategies, output evaluation, and the foundations of responsible AI use.

Content:

• AI Overview: Core concepts, timeline of development, and ways we’ve all interacted with AI.
• Generative AI: What it is, how it works, and why it matters.
• Popular Generative AI Tools: Key players in the ecosystem.
• Generative AI Overview: Free vs. Paid, settings, data controls, customization, memory, voice vs. text, and platform differences.
• Generative AI Hands-On: Using a generative AI assistant for writing, brainstorming, planning, research, light automation, and productivity support.
• Prompt Engineering: Zero-shot, few-shot, and chain-of-thought prompting.
• Other Generative AI Features: Archiving/deleting chats, sharing, memory on free accounts, temporary chat, and saved memories.
• Prompting Fundamentals: Applying prompting strategies for professional outputs (reports, summaries, plans).
• Output Quality Checks: Evaluating accuracy, bias, and reliability.
• Responsible AI Foundations: Introducing the UNESCO AI Principles, alongside data privacy and the limitations of generative AI as a foundation for responsible use.

Objective: Apply generative AI to everyday workplace tasks, manage documents and knowledge efficiently, automate routine work, and build personal AI agents while observing ethical and confidentiality standards.

Content:

• Generative AI in the Workplace: Why it matters and what it enables.
• Productivity Tools Comparison: Evaluating workplace AI tools.
• Generative AI Models: When and how to use them effectively.
• Workplace Organization: Using projects for workspace organization.
• Document Handling: Uploading, summarizing, comparing, transforming, and generating new content.
• Content Refinement with AI: Editing and improving clarity, tone, and accuracy using generative AI.
• Scheduling: Automating prompts with scheduled tasks and customizing notifications.
• Generative AI Agents: Exploring agentic AI tools entering the workplace and building personal AI agents using AI assistants.
• Ethical Workplace Use: Applying the UNESCO principles to confidentiality, data handling, and transparency when using AI for workplace tasks and assistants.

Objective: Build a trusted knowledge base, create professional presentations, and generate and refine compelling AI visuals while applying best practices for quality, attribution, and responsible content disclosure.

Content:

• Knowledge Management with NotebookLM: Definition, comparison with other generative AI tools, and limitations.
• NotebookLM Hands-On: Building a knowledge base and generating summaries, reports, insights, and visuals.
• Presentations with AI: Why presentations matter, and how to generate professional slide decks using generative AI.
• Quality Checklist: Ensuring presentation outputs meet professional standards.
• AI Visuals: Identifying AI-generated images and understanding image prompt structure.
• Visual Creation Hands-On: Refining images with generative AI, interpreting images for insights, and creating brand generators.
• Best Practices: Responsible and effective presentation and image generation.
• Ethics in Research and Visuals: Applying frameworks for source attribution, disclosure of AI-generated content, and avoiding misleading or deceptive visuals.

Objective: Analyze data, generate insights, and support organizational work using AI tools, applying transferable skills that carry across different AI platforms.

Content:

• Data Analysis with AI: Principles for working with data using AI, including data quality, validation, privacy, and structured analysis approaches.
• Data Visualization & Reporting: Creating charts, dashboards, and professional, audit-ready analytical reports, including executive summaries and data-driven narratives.
• Creating AI agents for data analysis: Using AI assistants.
• Ethical Data Practices: Applying frameworks for data privacy, bias detection, and honest representation of findings in AI-assisted analysis.
• Staying Current: Understanding the expected useful lifespan of today’s AI tools and building habits for continuous upskilling as the landscape evolves.