Academic Catalog

LINC 51C: ARTIFICIAL INTELLIGENCE LITERACY & ETHICS IN EDUCATION

Foothill College Course Outline of Record

Foothill College Course Outline of Record
Heading Value
Effective Term: Winter 2025
Units: 3
Hours: 3 lecture per week (36 total per quarter)
Degree & Credit Status: Degree-Applicable Credit Course
Foothill GE: Non-GE
Transferable: CSU
Grade Type: Letter Grade (Request for Pass/No Pass)
Repeatability: Not Repeatable

Description

This course introduces educators to the foundational concepts of artificial intelligence (AI), emphasizing its practical and ethical implications in educational settings. Students will explore the evolution of AI, its current applications in learning environments, and critical ethical issues such as data privacy, bias, and equity. The course combines theoretical learning with case studies and practical exercises to enhance AI literacy among educators. Special emphasis is placed on developing strategies to integrate AI responsibly in education, ensuring alignment with ethical standards and educational equity.

Course Objectives

The student will be able to:

  1. Understand foundational concepts and evolution of artificial intelligence (AI).
  2. Analyze current AI applications in education.
  3. Identify and address ethical implications of AI in education.
  4. Develop strategies for responsible AI integration.
  5. Implement AI literacy activities with learners.
  6. Critically assess AI-related policies and practices.

Course Content

  1. Foundational concepts and evolution of artificial intelligence (AI)
    1. History and evolution of AI
    2. Key principles of AI and its various branches (e.g., machine learning, natural language processing, computer vision)
    3. Basic terminology and concepts related to AI
    4. Significance and impact of AI on education
  2. Current applications of AI in education
    1. Case studies and real-world examples of AI use in classrooms
    2. Analysis of AI tools currently being used in education
    3. Benefits and challenges of integrating AI in educational settings
  3. Ethical implications of AI in education
    1. Data privacy and security concerns
    2. Addressing bias in AI algorithms
    3. Ensuring equity in AI applications
    4. Ethical decision-making frameworks for AI in education
  4. Strategies for responsible AI integration
    1. Developing ethical guidelines for AI use in education
    2. Creating AI integration plans that promote equity and inclusivity
    3. Implementing ethical AI practices in schools
  5. Implementing AI literacy activities with learners
    1. AI literacy skills for educators
    2. Teaching AI concepts to students
    3. Resources and tools for AI education
    4. Strategies for staying updated with AI developments
  6. Assessing AI-related policies and practices
    1. Overview of current policies on AI in education
    2. Evaluating the effectiveness and fairness of existing AI policies
    3. Proposing improvements and new policies to ensure responsible AI use

Lab Content

Not applicable.

Special Facilities and/or Equipment

1. When offered on/off campus: Lecture room equipped with projector, whiteboard, and a demonstration computer connected online.
2. When taught via the internet: Students must have current email accounts and ongoing access to computers with web browsing capability and internet access.

Method(s) of Evaluation

Methods of Evaluation may include but are not limited to the following:

Development of AI projects and activities that follow ethical guidelines and promote equity and inclusivity
Sharing projects with peers to gather feedback and make improvements
Making constructive contributions to class discussions and peer reviews
Assignments will be evaluated based on a detailed rubric, and students will have the opportunity to revise and resubmit their work to demonstrate improvement based on feedback

Method(s) of Instruction

Methods of Instruction may include but are not limited to the following:

Lecture presentations delivered in student-centered learning style, during which students take notes, follow demonstrations, or complete an activity
Facilitated discussions of live presentations, readings, or video presentations
Student presentations in small group and whole class situations
Structured peer review sessions to provide and receive constructive feedback
Ongoing reflection and self-assessment to connect course content to real-world applications and experiences

Representative Text(s) and Other Materials

Instructor-assigned notes, materials, and resources, including instructional materials, open education resources, multimedia, and websites.

Types and/or Examples of Required Reading, Writing, and Outside of Class Assignments

  1. Reading assignments include analysis of texts, selected examples, and student projects.
  2. Writing assignments include multiple developmental projects, reflections, discussion responses, and peer feedback on projects.
  3. Outside assignments include project development, participation in online forums, and collaborative group work.

When taught online, these methods may take the form of multimedia and web-based presentations. Assignments will be submitted online as well.

Discipline(s)

Instructional Design/Technology