Teaching · Fall 2026

CSCI 400: Computers and Society

Delivering the CSCI 200 content: Artificial Intelligence, Data, and Society

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Fall 2026 offering: The course is listed as CSCI 400 (Computers and Society) while delivering the CSCI 200: Artificial Intelligence, Data, and Society curriculum proposed for the 2027 program change. For assessment announcements and the current schedule, enrolled students should consult VIULearn.

Course overview

An introduction to artificial intelligence for students across disciplines, with an emphasis on responsible use, privacy, and social impact. Students study foundational AI methods, data collection and surveillance, fairness and professional ethics, and emerging AI governance, applying these ideas to practical case studies.

Prerequisite: A minimum of 3 credits of university-level coursework (any discipline), or instructor permission. No prior computer science or mathematics background required.

Class schedule · Fall 2026

  • Tuesday lecture: 1:00–2:30 p.m., in person, 200/106 (both sections).
  • Thursday module: 1:00–2:30 p.m., asynchronous through VIULearn (both sections).
  • F26N01 seminar: Monday, noon–1:00 p.m., 315/114.
  • F26N02 seminar: Wednesday, noon–1:00 p.m., 315/114.

There are no required course activities during the November 9–13, 2026 study break.

Topics

  1. What is AI and why now?
  2. Core AI paradigms: rules, search, machine learning, generative AI and reinforcement learning.
  3. Data, privacy, and surveillance infrastructures.
  4. Ethics, professional responsibility, and data governance.
  5. Fairness, bias, and explainability in AI.
  6. AI in critical domains including health, education and public life.
  7. Work, environment, and AI governance futures.
  8. Synthesis and student projects.

Learning outcomes

Students learn to describe foundational AI concepts, map data flows and privacy risks, apply ethical and privacy frameworks, assess bias and reliability, and formulate practical guidance for responsible AI use in their disciplines.

Learning combines in-person lectures, asynchronous activities and section-specific discussion seminars.

Evaluation

Fall 2026 evaluation components
Component Weight
Weekly online quizzes 15%
AI-in-Context reflections 15%
Midterm test 20%
Group AI case study and impact brief 25%
Seminar participation 5%
Final exam or take-home exercise 20%

To pass: complete the group project and earn at least 50% on the combined midterm and final exam components.