Course content
8 modules. Open a module to see what it covers.
1 Introduction to Artificial IntelligenceObjectives · topics · practical exercise
This module provides a foundational understanding of Artificial Intelligence (AI). Learners will explore the origin and evolution of AI, distinguish between basic automation and AI-driven intelligence, classify various types of AI, and analyze real-world applications alongside their key benefits and limitations.
Learning objectives
By the end of this module, learners will be able to:
- Define Artificial Intelligence and articulate its significance in modern technology.
- Distinguish between basic automation and adaptive AI systems.
- Classify AI into Narrow, General, and Super AI, as well as Reactive vs. Generative models.
- Identify everyday AI integrations across personal and professional environments.
- Evaluate the benefits and operational limitations of adopting AI.
Topics covered
- What is AI?
- History and evolution of AI
- AI vs. automation
- Types of AI (Narrow AI, General AI, Super AI; Rule-based vs. Machine Learning)
- Everyday applications of AI (e.g., recommendation engines, navigation, facial recognition)
- Benefits and limitations of AI
Practical exercise
- AI Identification Exercise: Mapping daily interactions with technology and classifying which ones rely on traditional automation vs. AI.
- AI Tool Exploration: Hands-on exploration of popular consumer AI applications (e.g., voice assistants, predictive text, recommendation systems).
- Group Discussion: Small group debate on the present impact and future expectations of AI within various industries and workplaces.
Expected outcome
- Clear conceptual clarity on AI capabilities and boundaries.
- Ability to audit personal and work environments for active AI technologies.
2 Artificial Intelligence Tools & Digital LiteracyObjectives · topics · practical exercise
This module builds practical digital literacy in navigating AI tools and interfaces. Learners will examine Generative AI, natural language processing models, visual/audio AI tools, and search engines, while adopting core digital safety practices when interacting with online AI platforms.
Learning objectives
By the end of this module, learners will be able to:
- Navigate key generative AI modalities: text, image, audio, and search assistant tools.
- Differentiate traditional keyword-based search from AI-driven conversational search.
- Interact effectively with user interfaces across different AI applications.
- Apply basic digital safety principles when creating accounts and inputting information into online tools.
Topics covered
- Generative AI concepts and capabilities
- Overview of AI assistants, chatbots, and generative platforms
- Search engines vs. AI search agents
- Text, image, and audio AI capabilities
- Understanding AI user interfaces (prompts, parameters, history, exports)
- Basic digital safety (account hygiene, data privacy, avoiding phishing)
Practical exercise
- Platform Onboarding: Setting up and navigating an account on an approved AI platform.
- Interactive Q&A Session: Formulating basic natural-language queries to retrieve structured information.
- Comparative Analysis Task: Running identical information requests through a traditional search engine and an AI search tool, then documenting the differences in speed, format, and depth.
Expected outcome
- Operational familiarity with primary AI tools and user interfaces.
- Ability to select the appropriate tool (traditional search vs. generative AI) based on task requirements.
- Confidence in applying basic cybersecurity precautions while using AI platforms.
3 Prompt Engineering EssentialsObjectives · topics · practical exercise
This module teaches the art and science of prompt engineering—framing instructions to get high-quality responses from generative AI models. Learners will master prompt structural components, iterative refinement techniques, and targeted prompting strategies across professional contexts.
Learning objectives
By the end of this module, learners will be able to:
- Identify and apply the core components of a structured prompt (Context, Role, Task, Format, Constraints).
- Craft clear, precise instructions to minimize ambiguity in AI outputs.
- Refine weak or vague prompts systematically through iterative testing.
- Tailor prompts for specific domains such as learning, business writing, and problem-solving.
Topics covered
- What is a prompt?
- Core structure of an effective prompt
- The power of clear instructions and role specification
- Providing context and specifying desired output formats
- Setting constraints (length, tone, style, excluded terms)
- Iterative prompt refinement and chain-of-thought prompting basics
Practical exercise
- Basic Prompting: Drafting initial prompts to solve simple informational queries.
- Prompt Upgrade Challenge: Analyzing poorly structured, vague prompts and rewriting them using the complete structural framework.
- Contextual Prompt Creation: Writing custom prompts tailored to specific workplace scenarios (e.g., drafting emails, explaining complex concepts, formatting raw data).
Expected outcome
- Mastery over prompt framing techniques to elicit precise, usable AI responses on the first try.
- A personalized toolkit of prompt templates for daily tasks.
4 AI for Learning & ProductivityObjectives · topics · practical exercise
Focusing on academic and personal productivity, this module demonstrates how to leverage AI as a research partner, study aid, and administrative assistant. Learners will practice summarization, structured planning, note taking, and writing support.
Learning objectives
By the end of this module, learners will be able to:
- Synthesize lengthy documents and articles into concise, actionable summaries.
- Use AI to design structured study schedules, project plans, and task workflows.
- Employ AI for creative brainstorming and conceptual mapping.
- Streamline note-taking and multilingual translation tasks.
Topics covered
- AI as a research assistant and information filter
- Document summarization and key-takeaway extraction
- AI-assisted note-taking and synthesis
- Brainstorming techniques and mind-mapping with AI
- Project planning and scheduling
- Drafting support and real-time translation
Practical exercise
- Document Summarization: Processing a multi-page article or document using AI to extract key insights, executive summaries, and action items.
- Study/Project Plan Generation: Generating a step-by-step 4-week learning roadmap or project execution schedule.
- Meeting Notes Synthesis: Transforming raw, bulleted meeting transcripts into formatted meeting minutes with clear action items.
- Workplace Checklist Construction: Creating an operational checklist for a complex routine task using AI.
Expected outcome
- Increased speed and efficiency in processing large amounts of text.
- Ability to generate structured plans, notes, and task management lists in minutes.
5 AI for Communication & Workplace TasksObjectives · topics · practical exercise
This module applies AI tools directly to workplace tasks and business communication. Learners will explore methods for drafting professional communications, building presentation structures, analyzing operational problems, handling customer support scenarios, and preparing job application materials.
Learning objectives
By the end of this module, learners will be able to:
- Draft professional emails, reports, and memos adjusted for specific corporate tones.
- Build presentation outlines and slide content structures using AI.
- Formulate customer service responses tailored to sensitive or complex inquiries.
- Organize raw workplace data into structured formats (tables, lists, key metrics).
- Utilize AI for career preparation, including resume alignment and interview practice.
Topics covered
- Drafting and polishing professional emails and business correspondence
- Structuring workplace reports and executive summaries
- Generating presentation outlines and slide copy
- Automating and refining customer service communication workflows
- Data cleaning, formatting, and organization via natural language prompts
- Career optimization: tailored resumes, cover letters, and interview preparation
- Systematic workplace problem-solving using AI frameworks
Practical exercise
- Email Optimization: Drafting a sensitive client email and utilizing AI to refine tone, conciseness, and clarity.
- Short Report Generation: Outlining and drafting a 1-page status report on a given business scenario.
- Presentation Outline Creation: Prompting AI to build a slide-by-slide structure for a 10-minute professional briefing.
- Customer Response Simulation: Crafting dynamic response templates for realistic customer service complaints.
Expected outcome
- Professional-grade written communication skills enhanced by AI tools.
- Reduced turnaround time for routine administrative and business drafting tasks.
6 Data, Accuracy, and Critical Thinking with AIObjectives · topics · practical exercise
This module focuses on critical thinking, information literacy, and output verification when using AI. Learners will examine how models process data, why hallucinations occur, how bias manifests in outputs, and how to rigorously fact-check AI-generated information against verified sources.
Learning objectives
By the end of this module, learners will be able to:
- Explain how training data impacts AI outputs and predictions.
- Identify AI hallucinations, factual errors, and logical inconsistencies.
- Conduct structured fact-checking using primary and credible secondary sources.
- Recognize implicit bias and limitations in AI-generated answers.
- Apply human-in-the-loop verification processes before using AI outputs.
Topics covered
- Fundamentals of data input and model generation
- Understanding accuracy, reliability, and source attribution
- What causes AI hallucinations and how to spot them
- Verification and fact-checking strategies
- Algorithmic bias, historical data skew, and neutrality challenges
- The "Human-in-the-Loop" principle: ethics of automated decisions
Practical exercise
- Stress-Testing AI: Prompting AI with niche or complex questions to induce and identify factual errors or hallucinations.
- Inaccuracy Detection: Reviewing pre-drafted AI responses, identifying inaccuracies, and cross-referencing them against authoritative sources.
- Response Correction: Rewriting flawed AI outputs into fully verified, factually correct content using proper source citation.
Expected outcome
- A habit of critical evaluation toward all AI-generated content.
- Proven framework for fact-checking and correcting automated outputs before professional distribution.
7 Responsible & Ethical Use of AIObjectives · topics · practical exercise
Addressing the social, legal, and ethical dimensions of technology, this module covers privacy, data governance, intellectual property, cybersecurity, and workplace integrity. Learners will develop guidelines for responsible usage in personal, academic, and professional environments.
Learning objectives
By the end of this module, learners will be able to:
- Identify privacy risks and data security exposures when using third-party AI platforms.
- Navigate copyright, intellectual property, and attribution issues tied to AI content.
- Evaluate issues of fairness, diversity, and bias in automated decision systems.
- Adhere to academic and professional integrity standards regarding AI disclosures.
- Formulate clear acceptable-use policies for personal or workplace environments.
Topics covered
- Key principles of AI ethics
- Data protection, confidential information, and privacy policies
- Cybersecurity risks (prompt injection, sensitive data leakage, deepfakes)
- Copyright, intellectual property rights, and fair use in generative content
- Bias, fairness, and inclusivity considerations
- Academic and professional integrity standards
- Personal accountability and responsible implementation guidelines
Practical exercise
- Risk Identification Task: Analyzing real-world case studies to identify unsafe data inputs, privacy breaches, and ethical missteps.
- Workplace Scenario Review: Evaluating hypothetical situations involving employee AI usage and deciding on appropriate policy responses.
- Acceptable-Use Policy Drafting: Developing a comprehensive AI Acceptable-Use Checklist tailored to a specific office, school, or organization.
Expected outcome
- Awareness of legal, privacy, and security constraints surrounding AI adoption.
- Ability to establish and enforce safe, ethical AI practices in any organization.
8 AI Applications & Capstone ProjectObjectives · topics · practical exercise
The final module explores industry-specific implementations of AI across telecommunications, business, education, operations, and customer service. It examines emerging career trends and culminates in an integrated capstone project where learners apply all concepts mastered throughout the course to solve a real-world challenge.
Learning objectives
By the end of this module, learners will be able to:
- Analyze current and future AI applications across key economic sectors.
- Evaluate the changing landscape of work and emerging career paths in the AI space.
- Synthesize prompt engineering, research, workplace drafting, ethical auditing, and verification skills into a comprehensive final deliverable.
- Present an AI-assisted project solution clearly to stakeholders.
Topics covered
- Industry deep-dives: Telecom, Business, Education, Operations, and Customer Support
- The future of work: augmentation, automation, and human-AI collaboration
- Emerging job roles and career advancement opportunities
- Course review, synthesis, and key skill consolidation
- Capstone guidelines and project presentation frameworks
Practical exercise
- Industry Application Mapping: Selecting a target sector (e.g., Telecom or Business) and mapping out 3 concrete opportunities for AI workflow integration.
- Capstone Project Execution: Working individually or in teams to tackle a real-world problem statement—utilizing AI for research, planning, execution, output verification, and ethics auditing.
- Capstone Presentation: Presenting the final capstone deliverable, workflow breakdown, and prompt log to the class for peer and instructor review.
Expected outcome
- Industry-specific awareness of transformative AI trends.
- A portfolio-ready Capstone Project demonstrating end-to-end practical mastery of AI tools, prompting, critical auditing, and ethical compliance.
