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Mar 24, 2026
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AAIT 110 - Artificial Intelligence Essentials
CREDIT HOURS MIN: 3
LECTURE HOURS MIN: 3
TOTAL CONTACT HOURS MIN: 48
This course introduces students to the basics of Artificial Intelligence (AI) and helps them feel confident using AI tools and systems in different types of jobs. Students will learn what AI is, how it works, and the major areas it covers, such as using data and managing the full AI process. Through hands-on activities, they will practice creating better prompts for AI to improve writing, data analysis, and problem-solving. Students will also learn how to use AI responsibly and think about how it affects society and the workplace. By working on real-life examples and projects, they will gain the skills to design, test, and improve AI solutions that are useful, fair, and sustainable.
MAJOR COURSE LEARNING OBJECTIVES: Upon successful completion of this course the student will be expected to:
- Define the scope, disciplines, and systems of Artificial Intelligence (AI) to establish a foundational understanding of key concepts and terminology.
- Differentiate AI-ready data strategies that use diverse data types and ensure compliance with data governance standards.
- Classify the components of the AI ecosystem.
- Illustrate the complete lifecycle of AI models from problem definition through model development, evaluation, and deployment.
- Apply effective prompt engineering strategies to optimize interactions with generative AI tools for tasks such as content generation, data analysis, and problem-solving.
- Investigate AI reliability, security, and privacy through performance metrics, hallucinations, and risk mitigation.
- Demonstrate responsible and ethical AI principles for model development and usage to ensure fairness, transparency, and accountability.
- Investigate the societal, workforce, and environmental impacts of AI technologies to anticipate potential risks, benefits, and opportunities for innovation.
COURSE CONTENT: Topical areas of study include -
- Foundations and Scope of AI
- The AI Ecosystem and Core Disciplines
- Data Readiness and Governance for AI
- Machine Learning Fundamentals and the AI Lifecycle
- Generative AI Applications and Prompt Engineering
- Responsible and Ethical AI Practices
- AI Safety, Reliability, and Security
- Societal, Workforce, and Environmental Impacts of AI
- Cloud-Based AI Tools and Platforms
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