Academic Catalog

LINC 51E: STUDENT-CENTERED ARTIFICIAL INTELLIGENCE PROJECTS

Foothill College Course Outline of Record

Foothill College Course Outline of Record
Heading Value
Effective Term: Spring 2026
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

Student Learning Outcomes

  • Design a multimodal, AI-enhanced project plan that amplifies student voice and creativity and aligns with Universal Design for Learning (UDL) principles.
  • Critically justify the selection and integration of generative-AI tools within a student-centered project, addressing issues related to ethics and inclusion.

Description

This course guides educators in designing and facilitating artificial intelligence (AI)-enhanced projects that foreground student voice, creativity, and authentic connection. Participants investigate generative AI tools for text, image, audio, and data visualization, then apply design-thinking and Universal Design for Learning principles to craft inclusive, culturally responsive experiences. Emphasis is placed on humanizing AI use through activities that position learners as designers, collaborators, creators, and evaluators.

Course Objectives

The student will be able to:

  1. Explore and compare generative artificial intelligence (AI) tools for text, image, audio, and data-visualization to determine their strengths, limitations, and classroom fit.
  2. Apply design-thinking processes to outline student-centered AI project workflows from empathy mapping through prototyping and reflection.
  3. Integrate Universal Design for Learning checkpoints and culturally responsive practices into AI-enhanced project plans to ensure broad accessibility and relevance.
  4. Identify and propose mitigation strategies for ethical, privacy, and bias concerns that may arise when students use generative-AI tools.
  5. Develop criteria and methods for assessing student creativity, voice, and collaboration within AI-driven projects.

Course Content

  1. Generative artificial intelligence (AI) tools for student projects
    1. Overview of text, image, audio, and data-visualization generators
    2. Strengths and limitations for K-12 use cases
    3. Screening criteria: cost, access, privacy, age appropriateness
  2. Design thinking workflow for AI-enhanced projects
    1. Empathy mapping and problem framing with students
    2. Ideation and low-fidelity prototyping using AI assists
    3. Iteration cycles, reflection prompts, and project timelines
  3. Universal Design for Learning (UDL) and culturally responsive integration
    1. Mapping AI features to UDL checkpoints (engagement, representation, action/expression)
    2. Leveraging AI for linguistic, cultural, and ability diversity
    3. Designing choice boards and flexible pathways to amplify student voice
  4. Ethics, privacy, and bias mitigation
    1. Data stewardship, consent, and privacy
    2. Bias detection and inclusive prompt-engineering strategies
    3. Classroom norms, policy alignment, and risk-mitigation plans
  5. Assessing creativity, voice, and collaboration
    1. Rubric frameworks for multimodal AI projects
    2. Process-oriented peer and self-assessment tools
    3. Using AI analytics judiciously for formative feedback and revision planning

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:

Development and presentation of multimodal, AI-enhanced project plans that align with UDL principles and foreground student voice and creativity
Critical justification briefs that address ethical, privacy, bias, and inclusion considerations for selected generative-AI tools
Sharing project drafts with peers to gather feedback and iteratively improve design choices
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

Fitzpatrick, Dan, Amanda Fox, and Brad Weinstein. The AI Classroom: The Ultimate Guide to Artificial Intelligence in Education. 2023.

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