Every course changes when the machine can write the code.
Agentic AI has moved from a topic inside the curriculum to a condition surrounding all of it. This working draft pairs all 28 Concordia University Wisconsin Computer Science courses with a proposed revision, gives each one a fresh set of course learning outcomes, and maps every outcome to the program learning outcomes and, through them, to Concordia's six university learning outcomes.
Not every course is an AI course. Every course is changed by AI.
A revision that turns Animation, Computer Architecture or Operating Systems into an artificial-intelligence course would be a bad revision. What has actually changed is the practice of each discipline: who writes the first draft, what a professional is accountable for, and which judgements cannot be delegated. Each proposal below keeps the course's subject intact and reflects that shift in how the subject is worked.
What the revisions add
- Specification and decomposition as the primary authoring skill
- Review, verification and testing of machine-generated work
- Professional accountability for output the student did not type
- Evaluation, guardrails and failure analysis appropriate to the subject
- The vocational and ethical question of what should be automated at all
How AI weight is distributed
Courses whose subject is artificial intelligence carry it as content. The rest carry it as practice. Both are visible here.
Six university outcomes, eighteen program outcomes
Concordia University Wisconsin holds every graduate to six university learning outcomes. The Computer Science program expresses each of them as three program learning outcomes, and every course learning outcome in this draft maps to at least one of those eighteen.
Christian Faith
“Our graduates are grounded in the Christian faith while also recognizing other major worldviews and how they differ from a Christian understanding of the world.”
Service and Global Citizenship
“Our graduates are globally-minded citizens.”
Integrated Disciplinary Knowledge
“Our graduates integrate insights from a wide range of disciplines.”
Critical Thinking / Creative Problem Solving
“Our graduates think rationally, critically, and creatively.”
Communicative Fluency
“Our graduates communicate effectively.”
Analytical Fluency
“Our graduates work with data effectively.”
Which outcomes the revised curriculum actually reaches
Each bar counts the courses whose proposed outcomes provide evidence for that program learning outcome. An outcome carried by many courses is well served; one carried by few is a question for the department, not necessarily a defect.
Table view
| PLO | University outcome | Courses | CLOs |
|---|---|---|---|
| PLO 1.1 Understanding Biblical Foundations | ULO1 Christian Faith | 4 | 4 |
| PLO 1.2 Faith in Professional Practice | ULO1 Christian Faith | 17 | 18 |
| PLO 1.3 Vocation and Purpose | ULO1 Christian Faith | 14 | 14 |
| PLO 2.1 Service through Technology | ULO2 Service and Global Citizenship | 6 | 6 |
| PLO 2.2 Engagement with Diversity | ULO2 Service and Global Citizenship | 5 | 5 |
| PLO 2.3 Stewardship of AI Resources | ULO2 Service and Global Citizenship | 16 | 18 |
| PLO 3.1 Interdisciplinary Problem-Solving | ULO3 Integrated Disciplinary Knowledge | 7 | 7 |
| PLO 3.2 Real-World AI Applications | ULO3 Integrated Disciplinary Knowledge | 11 | 13 |
| PLO 3.3 Collaboration in AI Projects | ULO3 Integrated Disciplinary Knowledge | 6 | 6 |
| PLO 4.1 Data and Ethical Analysis | ULO4 Critical Thinking / Creative Problem Solving | 27 | 37 |
| PLO 4.2 Innovative AI Solutions | ULO4 Critical Thinking / Creative Problem Solving | 18 | 26 |
| PLO 4.3 Reflective Decision-Making | ULO4 Critical Thinking / Creative Problem Solving | 17 | 19 |
| PLO 5.1 Clear and Ethical Communication | ULO5 Communicative Fluency | 27 | 34 |
| PLO 5.2 Interpersonal Skills in AI Collaboration | ULO5 Communicative Fluency | 6 | 6 |
| PLO 5.3 Engagement with Broader Audiences | ULO5 Communicative Fluency | 21 | 21 |
| PLO 6.1 Data Interpretation in AI | ULO6 Analytical Fluency | 23 | 30 |
| PLO 6.2 Quantitative and Qualitative Analysis | ULO6 Analytical Fluency | 22 | 27 |
| PLO 6.3 Critical Ethical Analysis | ULO6 Analytical Fluency | 16 | 17 |
Start anywhere
Every course page shows the current catalog description, the proposed revision, what changed and why, and the proposed course learning outcomes with their mapping.
Foundations of Computer Science
Every area this survey introduces is now practiced with AI assistance, so the entry-level skill is no longer only operating a computer but specifying work for a machine, verifying what comes back, and owning the result. CSC 1010 therefore becomes the program's AI-literacy on-ramp while remaining a broad survey of computing's grand ideas rather than an AI course.
Computer Communication
Every application this course teaches now ships a generative assistant, so the entry-level office skill in industry has shifted from producing the memo, spreadsheet, or deck to specifying it, steering the assistant, and verifying and owning what comes back. The course keeps its business-communication identity and adds the verification, confidentiality, and disclosure habits employers now expect of anyone who submits AI-assisted work.
Theory and Fundamentals of Computer Science
In industry the routine digital work this course covers — scripting, spreadsheet and document automation, web production, basic troubleshooting — is now typically started by an AI assistant rather than typed from scratch, so the differentiating skill has shifted to stating the task precisely and verifying what comes back. The course keeps its applied-fluency identity but makes review, verification, and honest attribution of machine-generated work a graded part of every unit.
Literature and Inquiry in Computing
Reading, source-finding, and technical writing are now routinely performed with AI assistants that summarize confidently and fabricate citations, so the professional skill in this area has shifted from locating sources alone to locating them, verifying machine-generated work against them, and disclosing assistance honestly. The course keeps its identity as the department's reading-and-writing seminar while making direct search, verification, provenance, and disclosure the literacy it teaches.
Coding I - Fundamentals
Entry-level programming work in industry now often begins with a specification handed to an AI coding assistant, so the durable professional skill is reading, testing, and being accountable for code a person did not write. The course therefore still teaches Python fundamentals in full, but frames them as the literacy required to review and verify machine-generated work rather than only to produce it.
Computer Architecture
AI has not changed what a processor is, but it has changed which parts of the machine decide performance: tensor and NPU datapaths, reduced-precision arithmetic, and memory bandwidth now drive industry hardware decisions, and low-level code is increasingly drafted by AI tools that a human must verify against the actual datapath. The course keeps its hardware identity and adds accelerator organization, measured performance analysis, and verification of machine-generated assembly and intrinsics.