Foundations of Intelligent Systems

This unit grounds learners in realistic expectations for intelligent systems and conversational AI by distinguishing practical capabilities from overhyped claims. Students will dispel common myths, assess risks of operating without a design strategy, and produce concise guidance to inform ethical, technical, and design decisions in preparation for later engineering and implementation units.

Learning Objectives

• Analyze hyperautomation by identifying at least three core components, two realistic enterprise use cases, and two common sources of hype, supporting distinctions with technical criteria
• Evaluate common misconceptions about conversational AI by classifying at least five myth statements as myth or fact and providing evidence-based justifications addressing technical limits, dataset concerns, or deployment constraints
• Differentiate human-centered design strategy from operating without design strategy by describing at least three operational risks and proposing a minimum of three mitigations aligned to privacy, reliability, and user trust
• Synthesize lesson learnings into a one-page design brief that outlines when to apply intelligent systems, recommended governance controls, and three actionable next steps for responsible prototyping