Course Outline of Record

Item	Value
Eff Term	Fall 2026
Curriculum Committee Approval Date	11/14/2025
Top Code	070200 - Computer Information Systems (CTE)
Units	0 Total Units
Hours	27 Total Hours (Lecture Hours 27)
Total Outside of Class Hours	0
Total Student Learning Hours	27
Course Credit Status	Noncredit (N)
Material Fee	No
Basic Skills	Not Basic Skills (N)
Repeatable	Yes; Repeat Limit 99
Open Entry/Open Exit	No
Grading Policy	P/NP/SP Non-Credit (D),  Letter Non-Credit (L)

Course Description

This course explores the ethical and responsible use of artificial intelligence (AI) in personal, educational, and professional settings. Topics include AI bias, privacy, misinformation, deepfakes, and the importance of human oversight when using AI tools. Not Transferable.

Course Level Student Learning Outcome(s)

1. Identify ethical risks related to AI use, including bias and misinformation.
2. Describe responsible practices when using AI-generated content.
3. Evaluate the credibility and limitations of AI tools.

Course Objectives

• 1. Define core ethical principles related to artificial intelligence, including fairness, transparency, and accountability.
• 2. Identify common risks associated with AI use, such as algorithmic bias, surveillance, and misinformation.
• 3. Analyze real-world examples of ethical dilemmas involving AI tools in education, business, and personal contexts.
• 4. Evaluate AI-generated content for accuracy, credibility, and potential for harm (e.g., misinformation, deepfakes).
• 5. Demonstrate responsible practices for using AI tools while respecting privacy, consent, and data protection.
• 6. Discuss the role of human oversight in mitigating risks and ensuring ethical decision-making when using AI.
• 7. Propose strategies for promoting digital citizenship and ethical behavior when interacting with AI technologies.

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Lecture Content

1. Introduction to AI Ethics and Human Oversight
2. Algorithmic Bias – Causes and Consequences
3. Misinformation and AI-Generated Content
4. Privacy and Data Collection by AI Systems
5. Transparency and Explainability in AI
6. Responsible Use of AI in Daily Life
7. Evaluating AI Credibility and Limitations
8. Ethical Use of AI in Education and Work
9. Regulation and Public Policy Around AI
10. Ethical Dilemmas and Group Scenarios
11. Building an Ethical AI Toolkit

Method(s) of Instruction

• Enhanced NC Lect (NC1)
• Live Online Enhanced NC Lect (NC9)
• Online Enhanced NC Lect (NC5)

Instructional Techniques

This course will utilize a combination of lecture, system simulators, classroom discussion with student interactions, problem-solving techniques, quizzes, exams, and troubleshooting assignments to achieve the goals and objectives of this course. All instructional methods are consistent across all modalities.

Reading Assignments

Read open educational resource materials, journal articles and corporate reports, news articles, interactive career websites, and privacy policies.

Writing Assignments

Analyze ethical issues related to AI use, evaluate real-world case studies, and reflect on responsible decision-making when applying AI tools in personal and professional contexts.

Out-of-class Assignments

Research examples of AI bias or misinformation, evaluate AI-generated content for credibility and ethical concerns, and write short reflections or case analyses that apply responsible use principles to real-world scenarios.

Methods of Student Evaluation

• Short Quizzes
• Written Assignments
• Projects (Individual/Group)
• Problem Solving Exercises
• Skills Demonstration

Demonstration of Critical Thinking

Analyze real-world ethical dilemmas, evaluate AI-generated content for credibility and bias, and form reasoned judgments about responsible technology use.

Required Writing, Problem Solving, Skills Demonstration

Analyze case studies, evaluate AI-related ethical issues, and apply responsible practices to real-life scenarios involving bias, privacy, and misinformation.

Resources Subscreen

• Open Education Resource: J.J. Sylvia IV. The Data Renaissance: Analyzing the Disciplinary Effects of Big Data, Artificial Intelligence, and Beyond. (September 2025).

Eligible Discipline(s)

• Computer information systems (computer network installation, microcomputer technology, computer applications): Any bachelor’s degree and two years of professional experience, or any associate degree and six years of professional experience.
• Computer service technology: Any bachelor’s degree and two years of professional experience, or any associate degree and six years of professional experience.
• Computer science: Master’s degree in computer science or computer engineering OR bachelor’s degree in either of the above AND master’s degree in mathematics, cybernetics, business administration, accounting or engineering OR bachelor’s degree in engineering AND master’s degree in cybernetics, engineering mathematics, or business administration OR bachelor’s degree in mathematics AND master’s degree in cybernetics, engineering mathematics, or business administration OR bachelor’s degree in any of the above AND a master’s degree in information science, computer information systems, or information systems OR the equivalent. Note: Courses in the use of computer programs for application to a particular discipline may be classified, for the minimum qualification purposes, under the discipline of the application. Master's degree required.