AI Foundations provides a comprehensive yet accessible learning path that builds from core concepts through practical application. Learners gain concrete skills in prompt engineering, AI tool evaluation frameworks, and hands-on practice with generative AI across multiple professional use cases. The course delivers measurable competencies in AI literacy while building confidence through scenario-based learning and authentic task completion, preparing learners to leverage AI tools effectively and participate meaningfully in workplace AI discussions. With flexible deployment options, the course serves undergraduate students, working professionals, and educators seeking foundational AI knowledge without requiring technical or programming backgrounds.
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Course Overview
Course Outcomes & Content
Key Course Objectives
By the end of this course, participants will be able to:
- Develop fundamental knowledge of artificial intelligence and machine learning to understand how AI systems work, including their underlying mechanisms, which provides the critical foundation to evaluate AI capabilities, recognize their strengths and limitations, and make informed decisions about AI application
- Evaluate AI tool capabilities and limitations to make informed decisions about tool selection and appropriate use cases within professional workflows
- Design and implement effective prompts that leverage generative AI for analysis, content creation, and problem-solving across diverse professional contexts
- Apply ethical frameworks and security practices when using AI tools to ensure responsible use, data privacy, and awareness of bias and limitations
- Analyze business problems and apply AI solutions through structured approaches to market analysis, performance evaluation, and project management
- Assess AI's strategic implications for workforce transformation, teaching practices, and individual professional development
Topics Covered
| Topic Area | Content |
|---|---|
| 🏢 AI Business Context | Business challenges driving AI adoption, point solutions vs. end-to-end AI systems, workforce transformation implications |
| 🤖 AI Technical Foundations | Characteristics defining AI systems, narrow vs. general vs. generative AI, machine learning and neural network principles, large language model mechanisms |
| ⚠️ AI Limitations & Risks | Hallucinations, bias, context constraints, data security vulnerabilities |
| 🛡️ Ethical & Security Frameworks | Data privacy practices, bias recognition and mitigation, responsible AI use strategies |
| 🎨 Generative AI Applications | Research and information gathering, data analysis and insights, content creation and design, communication and presentation |
| 📚 Historical Context | AI development milestones from early computing to generative AI, breakthrough moments (machine learning, deep learning, transformers), patterns of advancement |
| ✍️ Prompt Engineering | Prompt components and structure, specificity and context management, approaches for different content types, common prompt patterns, iterative refinement techniques |
| 🔍 AI Tool Selection | Quadrant-based evaluation frameworks, functional categorization (generation, analysis, transformation, automation, reasoning), market positioning assessment, trade-off analysis |
| 📊 Domain-Specific Applications | Market analysis with AI (requirements gathering, context provision, visualization direction, validation prompting), performance analysis with privacy-conscious prompting, project management decomposition and planning |
| 👨🏫 AI for Educators | Administrative task automation, research lifecycle acceleration, teaching material enhancement, assessment and feedback support |
| 🎓 AI for Learning | AI as tutor, Socratic interviewer, research assistant, writing coach, task assistant, graphic designer |
Delivery & Logistics
| Aspect | Details |
|---|---|
| ⏱️ Duration | 7 hours of active learning across 12 modules |
| 📱 Platform | QuantHub Upskill platform with lesson plans that support in-classroom delivery for each module |
| 📅 Delivery Format | Online, self-paced learning with optional instructor-led components. Can be deployed as standalone short course, supplemental module for existing courses, or professional development offering |
| 🎓 Deployment Options | Full 7-hour course, individual chapters (ranging from 30 minutes to 3.5 hours), or individual modules (30-60 minutes each) for targeted learning objectives |
Module Structure
The course follows a modular progression from foundational concepts through applied practice to role-specific applications:
- Scenario Activities: Each module includes interactive learning articles with embedded visualizations, gamified scenario-based formative assessments (3-life system) for concept mastery
- Task Activities: Applied modules (Chapter 2) include authentic case-based summative tasks requiring external work with downloadable files, generative AI tools, and real-world deliverables
- Microlearning Structure: ~5 minutes per learning objective allows flexible pacing and targeted focus on specific competencies
- Classroom Support: Lesson plans and facilitation guides enable instructors to complement online learning with in-classroom activities, discussions, and collaborative exercises
- Assessment Model: Automated formative assessment with immediate feedback; performance-based summative assessment with weighted 0-3 star scoring (2+ stars = passing)
Course Chapters
The course is organized into four chapters with flexible deployment options. Chapter 1 establishes foundational AI knowledge essential for all learners. Chapter 2 provides hands-on practice with domain-specific applications. Chapters 3 and 4 offer role-specific content that can be assigned selectively based on learner needs—educators and learning professionals benefit from both, while other professionals typically focus on Chapters 1-2 and 4.
Fundamentals of AI
Establish core understanding of AI characteristics, capabilities, limitations, and practical considerations. This foundational chapter covers business applications, technical mechanisms, ethics and security, use case categorization, historical development, prompt engineering basics, and tool selection frameworks—providing the essential knowledge base for all subsequent AI work.
Chapter Focus
Build comprehensive AI literacy by understanding how AI systems work, recognizing their capabilities and limitations, evaluating AI tools strategically, and applying ethical frameworks for responsible use.
Modules (7 × 30 minutes)
- Addressing Business Challenges with AI: Identify business challenges driving AI adoption, understand point solutions vs. end-to-end AI systems, and communicate workforce transformation implications
- Characteristics and Mechanisms of AI: Identify fundamental AI characteristics, analyze differences between narrow, general, and generative AI, describe machine learning and neural network principles, explain large language model mechanisms, and recognize AI limitations
- Ethical and Security Considerations: Identify ethical issues in AI use, describe appropriate data security practices, recognize biases in AI-generated content, and explain strategies for responsible AI use
- Generative AI Use Cases: Identify and categorize generative AI applications across research, data analysis, content creation, and communication contexts
- Historical Development: Identify key milestones in AI development, analyze breakthrough moments (machine learning, deep learning, transformers), evaluate how historical developments led to current capabilities, and recognize advancement patterns
- Intro to Prompt Engineering: Identify effective prompt components, explain how specificity affects outputs, understand prompt structure importance, distinguish approaches for different content types, and identify common prompt patterns
- Selecting AI Tools: Describe quadrant-based evaluation frameworks, categorize AI tools by function, identify market positioning, compare AI tool trade-offs, and apply evaluation criteria to determine appropriate tools for specific workflows
Applied Generative AI
Develop hands-on competency through domain-specific applications with authentic professional tasks. Apply prompt engineering techniques to real-world scenarios in market analysis, performance evaluation, and project management. Each module includes external work tasks requiring interaction with generative AI tools and completion of professional deliverables that mirror actual workplace challenges.
Chapter Focus
Master practical prompt engineering by completing authentic professional tasks across diverse business contexts, developing reusable prompt strategies, and demonstrating measurable AI application competencies.
Modules (3 × 60 minutes)
- GenAI for Market Analysis: Structure requirements-gathering prompts for ambiguous business requests, provide rich context to improve analysis accuracy, apply iterative prompting techniques, direct AI to generate targeted visualizations, design validation prompts, and create reusable prompt sequences. Includes 4 hands-on tasks creating structured market analysis reports.
- GenAI for Performance Analysis: Design privacy-conscious prompts for sensitive data, structure comparative analysis prompts for metrics evaluation, develop data visualization prompts, implement compliance-constrained prompting for regulatory contexts, and create audience-specific output prompts. Includes 5 hands-on tasks analyzing student performance data with privacy and compliance considerations.
- GenAI for Project Management: Use AI to decompose complex projects into structured frameworks, generate realistic project timelines with constraints, create personalized communication matrices using persona-based prompting, implement systematic risk brainstorming and mitigation, and create audience-specific project reports. Includes 4 hands-on tasks developing comprehensive project plans for AI implementation.
The Augmented Professor
Explore educator-specific AI applications designed to enhance teaching effectiveness and efficiency. Learn to leverage AI for administrative task automation, research lifecycle acceleration, teaching material development, and assessment support. While designed for educators, this content is equally relevant for trainers, instructional designers, and learning and development professionals.
Chapter Focus
Apply AI strategically across the full spectrum of academic responsibilities—from routine administrative work to research, teaching material creation, and student assessment—to reclaim time for high-value educational activities.
Module (1 × 30 minutes)
- AI as Your Teaching Partner: Use AI to streamline administrative, communication, and organizational tasks; accelerate the research lifecycle from discovery through dissemination; innovate and enhance course materials and assessments; and leverage AI to streamline assessment, generate actionable feedback, and analyze course data for instructional improvement
AI as Your Learning Partner
Discover AI's versatile role in personal learning and professional development through multiple learning modalities. Understand how to leverage AI as a tutor for skill building, Socratic interviewer for critical thinking, research assistant for information gathering, writing coach for content improvement, task assistant for productivity, and graphic designer for visual communication.
Chapter Focus
Transform AI from a passive tool into an active learning partner by understanding six distinct roles AI can play in supporting individual learning, development, and productivity across professional and personal contexts.
Module (1 × 30 minutes)
- AI as Your Learning Partner: Understand how to leverage AI as your tutor (skill development and concept mastery), Socratic interviewer (critical thinking and problem exploration), research assistant (information gathering and synthesis), writing coach (content improvement and editing), task assistant (productivity and workflow support), and graphic designer (visual communication and ideation)