All 28 Computer Science courses
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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.
Coding II - Algorithms
Coding agents now draft much of the routine control-flow and algorithmic code this course once asked students to write by hand, shifting the professional skill toward precise specification, decomposition, and independent verification of work the developer did not type. The course keeps algorithms and control structures as its subject but makes correctness reasoning and disciplined review of machine-generated implementations the graded evidence.
Fundamentals of Machine Learning
Machine learning is no longer practiced by hand-coding every model: industry practitioners now specify experiments, direct AI coding agents through data preparation and training loops, and remain accountable for results they did not type. The course keeps its theoretical core intact while making verification, reproducibility, and provenance of machine-generated work into graded skills rather than assumed ones.
Animation I
In industry, learned and generative tools now sit inside the animation pipeline itself, in concept art and previsualization, in assisted inbetweening and keyframe retiming, and in denoisers that cut render iteration, which shifts entry-level animation work toward art direction, review, and correction of output the animator did not hand-key. The course therefore keeps its full craft pipeline intact while adding the judgment, verification, and provenance practices studios now expect of junior artists.
Computational Dilemmas
Agentic AI has moved the hardest questions in professional computing ethics from the misuse of software to responsibility for work no human typed, making accountability, provenance, disclosure, and duty of care over autonomous systems the live dilemmas practitioners now face. The course keeps its vocation-centered moral framework and applies it to those cases rather than becoming a technical AI course.
User Experience and Interactive Systems
In industry, UX work has shifted from specifying deterministic screen behavior to designing around systems that generate probabilistic output, so practitioners now own disclosure, uncertainty display, correction paths, trust calibration, and the escalation points where an agent hands control back to a person. The course keeps its HCI identity and its research-prototype-test cycle, but the artifacts under study now include conversational and agentic features, and evaluation now has to measure recovery from confidently wrong machine output.
Database Fundamentals
Databases have become the grounding substrate of AI systems: retrieval quality, more than model choice, determines whether a generative application answers from authoritative records, and agents now both draft SQL and query production data directly. The course therefore keeps its relational core intact while adding vector and hybrid retrieval, disciplined verification of machine-generated SQL and migrations, and least-privilege governance of agent access to data.
Operating Systems
In industry the operating system has become the containment layer for AI: agent tool calls and machine-generated code are confined with namespaces, control groups, and system-call filters, while schedulers, paging, and I/O paths are tuned for accelerator-bound inference. The course therefore keeps its virtualization, concurrency, and persistence core intact and adds least-privilege confinement of agent workloads plus disciplined verification of AI-authored systems code.
Software Engineering
Software engineering is the discipline most directly reshaped by agentic AI: a large share of production code is now drafted by coding agents, moving the professional's work toward specification, decomposition, review of machine-generated output, verification, and accountability for code the engineer did not type. The course keeps its lifecycle-management identity but retrains its core practices - requirements, design review, quality assurance, and team process - around directing and verifying agent-assisted work.
Systems Programming
In industry, systems code is increasingly drafted by AI coding agents whose memory-safety faults, undefined behavior, and ABI mismatches survive passing tests, so the practitioner's work shifts toward verification, disassembly-level confirmation, and accountability for code no human typed. Systems programmers are also now the people who build the substrate AI runs on, so the course keeps its translation-software and hardware-interface core and adds sandboxing, resource control, and memory-mapped data movement for agent and inference workloads.
Programming Language Theory
In industry a growing share of source code is drafted by AI coding agents, which shifts the practitioner's leverage from typing code to specifying intent precisely and establishing that the delivered program means what was specified. That makes the language-level machinery this course already teaches - grammars, type systems, contracts, and formal semantics - the practical verification surface for machine-generated code, and it revalues declarative and functional styles as specification tools rather than historical curiosities.
Fundamentals of Artificial Intelligence
Artificial intelligence is this course's subject, but industry practice has moved from building single models to specifying, orchestrating, and verifying autonomous agents that call tools and retrieve their own context. The course keeps its theoretical and philosophical core while adding the agentic layer and the evaluation and accountability discipline that now governs professional AI work.
Emerging Technologies in Artificial Intelligence
Emerging AI practice has moved from training and calling models to composing them into agents that retrieve, call tools, and act, so the industry skill set now centers on specification, grounding, evaluation, guardrails, and accountability for machine-generated work. The course keeps its survey-of-the-frontier identity but makes agentic architecture, evaluation harnesses, and operational risk the frontier it surveys.
Cybersecurity
Security practice now runs in both directions at once: AI systems and the autonomous agents wired into enterprise tooling are themselves a new attack surface (prompt injection, data poisoning, over-privileged agent credentials, model and connector supply chains), while defenders increasingly work from machine-generated triage, detection rules, and incident timelines they must verify rather than trust. The course therefore keeps its enterprise-assurance identity and adds securing AI-enabled systems and verifying AI-assisted defensive work as first-class topics.
Networks & Security
Network engineering and defense are increasingly automated: telemetry is triaged by machine-learning detectors, configuration and remediation are drafted by AI agents, and those agents become part of the attack surface through tool-calling integrations and supply-chain exposure. The course keeps its networking identity while adding the verification, threat-modeling, and detection-evaluation skills practitioners now need.
Animation II
Machine learning has moved into exactly the stages this course teaches, including automated joint placement and skin-weight prediction, learned deformers and simulation approximations, markerless performance capture, and render denoising, so the character artist's work is shifting from producing every result by hand toward directing, testing, and repairing machine-generated output. The course keeps its identity as a Maya character animation studio course and adds the review, verification, and provenance practices that current production work requires.
Advanced Database and Web Development
In industry, database-backed web applications are now largely scaffolded by AI coding assistants and are increasingly consumed by software agents as well as by people, shifting the developer's work toward specification, retrieval design, review of generated code, and a vulnerability surface that now includes prompt injection and over-permissioned tool endpoints. The course keeps its web, data, and media identity while adding grounded retrieval, verification of work the developer did not type, and agent-era security and provenance practice.
Theoretical Computer Science
Theory has become the discipline that bounds AI claims: undecidability, reduction, and complexity results determine what automated verification can never guarantee, while formal language theory and information theory underpin constrained decoding, output validation, and tokenization. The course keeps its classical content intact but reframes proof and counterexample as the professional skill for accepting or rejecting work a human did not personally write.
Coding III - Data Structures
Agentic coding tools now emit textbook data structure implementations on demand, so the scarce professional skill has moved from writing them to specifying them precisely, choosing the right one for a workload, and verifying machine-generated code against its invariants and cost model. At the same time data structures have become AI infrastructure - inverted indexes, tries, and approximate-nearest-neighbor graphs are what make retrieval-augmented systems work - so the course keeps its classical core and adds both the verification discipline and the indexing structures industry now depends on.
Applied Artificial Intelligence
Advanced AI is no longer studied chiefly as a set of standalone models; in industry it now ships as autonomous, tool-calling agents that plan, retrieve, and act, so the practitioner's work has shifted toward specification, instrumentation, evaluation, and accountability for behavior no human authored line by line. The course therefore keeps its classical content on probabilistic reasoning, planning, natural language processing, robotic navigation, and deep learning while adding agent architectures, evaluation harnesses, guardrails, and provenance as graded practice.
Penetration Testing
Offensive security now faces two AI-shaped fronts: AI-enabled and agentic applications are themselves a live target class whose failure modes — prompt injection, tool-call abuse, excessive agency, poisoned retrieval — are invisible to traditional network and web testing, and testers increasingly work with AI-assisted reconnaissance, payload generation, and report drafting whose output must be verified before it is executed or delivered. The course keeps its red team identity and adds those targets and that verification discipline rather than becoming an AI course.
Internship
Interns now enter workplaces where AI agents draft, review, and operate software, so the professional skill being supervised has shifted toward specifying work, verifying machine-generated output, and answering for code the intern did not type. Placements that restrict AI tooling are equally instructive, since the intern must then justify the verification and attribution practices used in its place.
Capstone Project
Industry capstone-scale work is now delivered with AI coding agents, so the graduating skill is no longer typing every line but specifying, decomposing, verifying, and taking accountability for machine-generated output. The capstone therefore keeps its role as the synthesis of the major while adding justified selection among candidate designs (including AI-driven ones), evidence of verification, and provenance for AI contributions.
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