CMSY 109 Artificial Intelligence Foundations
This course introduces students to artificial intelligence (AI) as both a technological and socio-cultural phenomenon, with a focus on how to find, evaluate, and ethically use information. Students will learn practical strategies to formulate researchable questions, locate and assess authoritative sources (including technical and popular media), and apply AI tools responsibly within their academic discipline. Topics include AI fundamentals, data and model literacy, credibility and misinformation, privacy and intellectual property, bias and fairness, governance frameworks, and discipline-specific applications of AI. Assessments culminate in a disciplinary project that demonstrates ethical information use and clear evaluation of sources.
Prerequisite
Pre- or corequisite:
ENGL 121
Hours Weekly
3
Course Objectives
- Formulate inquiry questions about AI and refine them through iterative investigation to support real‑world problem solving.
- Apply purposeful search strategies across databases, datasets, model repositories, standards, and policy sources to locate relevant information.
- Analyze and evaluate diverse information sources—including research literature, datasets, model cards, news, and policy—for authority, credibility, purpose, bias, limitations, and relevance.
- Interpret AI‑related claims by examining data, methods, metrics, and documentation with attention to reproducibility, evidence quality, and uncertainty.
- Apply ethical frameworks—such as fairness, privacy, intellectual property, accessibility, and accountability—to assess AI use cases and justify discipline‑specific decisions.
- Produce and communicate responsible, well‑documented work that includes proper citation, AI‑use disclosure, and accessible explanations for both lay and specialist audiences.
Course Objectives
- Formulate inquiry questions about AI and refine them through iterative investigation to support real‑world problem solving.
Learning Activity Artifact
Procedure for Assessing Student Learning
- Information Literacy Rubric
- Apply purposeful search strategies across databases, datasets, model repositories, standards, and policy sources to locate relevant information.
Learning Activity Artifact
Procedure for Assessing Student Learning
- Information Literacy Rubric
- Course rubric
- Analyze and evaluate diverse information sources—including research literature, datasets, model cards, news, and policy—for authority, credibility, purpose, bias, limitations, and relevance.
Learning Activity Artifact
- Peer Review
- Hands-on exercise
Procedure for Assessing Student Learning
- Information Literacy Rubric
- Course rubric
- Interpret AI‑related claims by examining data, methods, metrics, and documentation with attention to reproducibility, evidence quality, and uncertainty.
Learning Activity Artifact
- Writing Assignments
- Lab exercise
Procedure for Assessing Student Learning
- Information Literacy Rubric
- Course rubric
- Apply ethical frameworks—such as fairness, privacy, intellectual property, accessibility, and accountability—to assess AI use cases and justify discipline‑specific decisions.
Learning Activity Artifact
- Other (please fill out box below)
- A writing assignment with peer review.
Procedure for Assessing Student Learning
- Information Literacy Rubric
- Produce and communicate responsible, well‑documented work that includes proper citation, AI‑use disclosure, and accessible explanations for both lay and specialist audiences.
Learning Activity Artifact
- Writing Assignments
- Lab Exercise
Procedure for Assessing Student Learning
- Information Literacy Rubric
- Course rubric