Course Content and Outcomes Guides (CCOG)

CCOG for CIS 212 archive revision 202604

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Effective Term:
Fall 2026

Course Number:
CIS 212
Course Title:
Introduction to AI Programming
Credit Hours:
4
Lecture Hours:
30
Lecture/Lab Hours:
0
Lab Hours:
30

Course Description

Introduces students to AI programming using existing AI models through released APIs. Covers the creation of AI-enabled applications with enhanced capabilities, including speech to text, text to speech, image and text generation, and computer vision. Covers installing and integrating existing AI modules and models, integration with Python application code, using Large Language Models (LLMs) as collaborative partners in the design and implementation of applications, and ethical considerations in AI development. Audit available.

Intended Outcomes for the course

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

  1. Integrate AI models into applications using released APIs.
  2. Develop AI-enabled applications that enhance user experience and functionality.
  3. Use AI ethically and with an awareness of biases in the models.
  4. Evaluate the performance and limitations of AI models in application contexts.
  5. Deploy AI technologies using the Python programming language.

Aspirational Goals

  • Use AI technology effectively in Industry.
  • Fine-tune pretrained models to meet specific application requirements.
  • Curate custom datasets that can be used for Retrieval Augmented Generation (RAGs) and Low-Rank Adaptation of Large Language Models (LoRAs).
  • Keep abreast of current developments in AI technology with an eye toward application enhancement and resilience in the face of a rapidly changing technological landscape.

Outcome Assessment Strategies

The course will include a large number of hands-on labs where students build AI-enabled applications using existing models. There will also be tests of factual knowledge and understanding of ethical issues related to AI use and the strengths, limitations, and biases of the various technologies.

Course Content (Themes, Concepts, Issues and Skills)

  • Introduction to AI Programming:

    • Overview of AI models and APIs.
    • Understanding AI model capabilities and limitations.

  • API Integration:

    • Accessing and using AI APIs (e.g., OpenAI, Google Cloud AI).
    • Practical exercises in API integration with Python.

  • Developing AI-Enabled Applications:

    • Designing applications that leverage AI capabilities.
    • Implementing AI features such as natural language processing, image recognition, and data analysis.
    • Enhancing the accessibility of applications using AI technology.

  • Ethical Considerations in AI Development:

    • Addressing bias and fairness in AI applications.
    • Privacy and security concerns in AI programming.
    • Responsible AI deployment and usage.
    • Using AI as a set of assistive technologies to build accessible applications.

  • Performance Evaluation:

    • Assessing AI model performance in real-world applications.
    • Identifying and addressing limitations and challenges.