Course Content and Outcomes Guides (CCOG)

CCOG for CIS 112 archive revision 202604

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Effective Term:
Fall 2026

Course Number:
CIS 112
Course Title:
AI Literacy
Credit Hours:
4
Lecture Hours:
30
Lecture/Lab Hours:
0
Lab Hours:
30

Course Description

Provides an introduction to Artificial Intelligence (AI) for non-programmers, focusing on practical applications and ethical considerations. Covers prompt engineering, using various AI models (including large language models, image generation, and text-to-speech), and working with integrated AI tools like Microsoft Office Copilot. Surveys the strengths and weaknesses of AI technologies. Recommended: digital literacy skills or prior completion of CIS 120. Audit available.

Intended Outcomes for the course

Upon successful completion of the course, students should be able to:

  1. Create effective prompts for AI models to achieve specific outcomes.
  2. Identify and utilize appropriate AI models for various tasks, including assistive technology.
  3. Integrate MS Copilot into personal and professional workflows.
  4. Discuss ethical implications and potential biases in AI technologies.
  5. Evaluate the strengths and limitations of different AI tools.
  6. Apply AI tools responsibly in various contexts.

Aspirational Goals

  • Understand the business impact of AI technology.
  • Be prepared for new developments and advancements in Artificial General Intelligence (AGI).

Outcome Assessment Strategies

Outcome assessment will include a mix of hands-on labs, knowledge self-checks and formal quizzes, and online or in-person discussion.

Course Content (Themes, Concepts, Issues and Skills)

Course Content:

  • Introduction to AI Concepts:

    • Overview of AI, machine learning, and deep learning.
    • Historical context and evolution of AI.
    • Applications of AI in different industries.

  • Prompt Engineering:

    • Principles of crafting effective prompts.
    • Techniques for refining prompts to enhance AI outputs.
    • Practical exercises with different AI models.

  • AI Tools and Applications:

    • Large Language Models (LLMs): capabilities and use cases.
    • Image Generation and Editing: creating and modifying images.
    • Text-to-Speech: converting text to speech.
    • Speech-to-Text: converting human speech to text.
    • MS Copilot: enhancing productivity with AI.

  • Ethical Considerations in AI:

    • Understanding bias in AI algorithms.
    • Privacy and security issues.
    • Frameworks for responsible AI development.

  • Strengths and Weaknesses of AI:

    • Assessing AI model performance.
    • Recognizing AI limitations and challenges.
    • Exploring future trends in AI.