Academic Catalog

LINC 51I: ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR EDUCATORS: UNDERSTANDING LARGE LANGUAGE MODELS

Foothill College Course Outline of Record

Foothill College Course Outline of Record
Heading Value
Effective Term: Summer 2026
Units: 0.5
Hours: 6 lecture per quarter (6 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

Student Learning Outcomes

  • Evaluate the capabilities and limitations of AI tools and apply them to support instructional practice

Description

This short course introduces educators to the basics of artificial intelligence (AI) and large language models (LLMs). Participants will explore what AI is and is not, examine common myths, and gain first-hand experience with core tools. Emphasis is placed on practical classroom applications and developing a critical awareness of AI's capabilities and limits.

Course Objectives

The student will be able to:

  1. Explain the core concepts of artificial intelligence and large language models and evaluate their implications for teaching practice.

  2. Apply AI tools to design classroom lessons and resources, while maintaining instructional quality.

Course Content

  1. Core concepts of artificial intelligence (AI) and large language models
    1. What AI is and isn't
    2. How large language models work
    3. Common myths and misconceptions
    4. Implications for teaching practice
  2. Applying AI in lesson and resource design
    1. Co-designing lessons with AI tools
    2. Time-saving tasks and resource generation
    3. Maintaining teacher voice and quality control
    4. Practical classroom applications

Lab Content

Not applicable.

Special Facilities and/or Equipment

When taught via the internet: Students must have current email accounts and/or ongoing access to internet capable computers or tablets.

Method(s) of Evaluation

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

Course project that demonstrates achievement of Student Learning Outcomes
Sharing drafts with peers to gather feedback and iteratively improve
Constructive contributions to class discussions and peer-review sessions
Major assignments will be evaluated against a detailed rubric, with opportunities to revise and resubmit work based on instructor and peer feedback

Method(s) of Instruction

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

The student will engage with course concepts through multimodal instructional materials offered in accessible formats, supplying multiple means of representation
The student will observe instructor-guided demonstrations and then apply skills using a modality of their choice (e.g., digital, visual, or written), providing multiple means of action and expression
The student will co-construct knowledge by participating in synchronous or asynchronous discussions, peer feedback, and collaborative activities that honor diverse cultural and linguistic assets, ensuring multiple means of engagement

Representative Text(s) and Other Materials

Cash, Scottye J.. The Educator's Essential Guide to AI. 2025.

Douglas, Andy. The AI-Powered Classroom. 2024.

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 planning and development, participation in online peer collaboration activities, and project development through an iterative process.

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