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.
Current description → proposed description
Foundations of Computer Science provides a survey and overview of Computer science via its grand ideas. The concept of a computer system as a combination of hardware, software, and people is explored in detail. The computer system as a tool for personal and professional problem solving is emphasized. Foundational computer science issues along with current technology, terminology, ethical issues, application, and hands-on computer use are explored. Students select a topic of interest as a term project to augment class discussion and laboratory experiences. CSC 1010 serves as the foundation for all further CSC courses and is suitable for all students as an introduction to the fascinating world of computer science. CSC satisfies course requirements in mathematics (except for CS/IT majors).
Foundations of Computer Science surveys the grand ideas of the discipline, including abstraction, algorithms, data representation, networks, and the computer system as a combination of hardware, software, and people, and emphasizes computing as a tool for personal and professional problem solving. Because information work is now routinely drafted, reviewed, and operated with AI agents, the course also develops the literacy practice requires: how machine learning systems are trained on data, what generative models and tool-using agents can and cannot reliably do, and how to specify a task, verify machine-generated work, and remain accountable for results one did not produce. Terminology, hands-on computer use, current technology, and the ethical questions raised by automated decision making, including privacy, bias, accountability, attribution, and the Christian understanding of vocation and stewardship, are explored throughout. Students select a topic of interest as a term project. CSC 1010 serves as the foundation for all further CSC courses and is suitable for all students as an introduction to the fascinating world of computer science. CSC satisfies course requirements in mathematics (except for CS/IT majors).
What changes
- AI literacy unit on models, agents, and their limits
- Algorithm design expressed as specification, pseudocode, or flowchart
- Verification and correction of machine-generated output
- Ethics broadened to bias, accountability, attribution, and vocation
- Term project requires an AI-use disclosure statement
6 proposed outcomes, mapped to 9 program outcomes
Each outcome below is written to be observable and assessable, and each is mapped to the program learning outcomes for which it produces evidence.
Students will be able to differentiate the hardware, software, data, network, and human components of a modern computer system, locating where machine learning and AI agent components now operate within that system and which application fields they influence.
PLO 2.1: the CLO requires students to identify the fields that computing and its AI components influence, which is precisely the awareness of the numerous fields Computer Science serves that this PLO names.
Students will be able to construct algorithmic solutions to small personal and professional problems, expressing each solution as an explicit written specification and a step-by-step procedure in pseudocode or a flowchart prior to any implementation.
PLO 5.1: the written specification and step-by-step procedure are graded written artifacts that must state the problem, the assumptions, and the intended behavior clearly and honestly before anything is built, which is the clear and transparent technical communication this PLO requires.
Students will be able to analyze how numbers, text, images, and sound are encoded as binary data in order to explain representation-level failure modes such as tokenization boundaries, floating-point and quantization precision loss, sampling rate limits, color depth, and lossy compression artifacts.
PLO 6.1: explaining why a language model miscounts the letters in a word or why a resampled image or audio clip degrades requires interpreting an observed AI or data output and tracing it to the underlying encoding, which is the foundational form of interpreting and explaining data outcomes.
Students will be able to evaluate the output of a generative AI tool or coding agent against a stated requirement, identifying what was verified, what was incorrect, and what the student changed before accepting the work.
PLO 4.1: judging machine-generated output against a requirement and marking what is wrong is direct critical analysis of a data-driven outcome. PLO 4.3: documenting what the student corrected before accepting the work establishes the habit of self-assessment and continuous improvement this PLO describes.
Students will be able to critique a current computing or AI application, such as algorithmic hiring, facial recognition, or an autonomous coding agent, explaining how the system's training data and design produce the behavior in question and assessing it on grounds of privacy, bias, accountability, and human dignity in light of a Christian understanding of vocation and stewardship.
PLO 6.3: assessing a named deployed application for bias, accountability, and effect on human dignity is exactly the fairness and moral-impact analysis this PLO requires. PLO 1.3: framing that critique through vocation and stewardship asks students to evaluate their own future role as stewards of the systems they will build. PLO 3.1: the CLO requires all three bodies of knowledge at once, an account of how the data and design produce the behavior, an ethical assessment of privacy, bias, and dignity, and a theological frame of vocation and stewardship, which is the introductory form of the interdisciplinary combination this PLO specifies.
Students will be able to articulate the findings of a term-project investigation of a computing topic to a non-specialist audience, disclosing any AI assistance used and distinguishing verified claims from claims the student could not verify.
PLO 5.3: delivering project findings to a non-specialist audience is the translation of technical knowledge for lay listeners that this PLO names. PLO 5.1: disclosing AI assistance and separating verified from unverified claims is the transparency and integrity component of communicative fluency.
Program outcomes this course reaches
Filled cells are program learning outcomes with at least one supporting course learning outcome in this course. Sparse coverage is expected — no single course carries all eighteen.