Students in computer science programs are routinely required to make consequential decisions about coursework and career preparation without clear, evidence-based guidance connecting individual courses to evolving workforce demand. While prerequisite structures and degree requirements are well defined, the relevance of specific courses to skills and careers is often implicit, informal, or static. At the same time, job roles, required skills, and even occupational categories change on timescales that outpace curricular revision, leaving students to infer meaning from incomplete or biased sources. This dissertation investigates how natural language processing, semantic clustering, and large language models can be used to construct a machine-assisted framework for aligning computer science courses with evolving workforce skill demand. The investigation prioritizes informed student decision-making rather than automated prediction or recommendation. It emphasizes open-vocabulary skill discovery, interpretable intermediate representations, and human-in-the-loop refresh cycles that allow alignments to be revisited as workforce demand changes. Workforce signals are derived from job postings, from which skills are extracted using open-vocabulary methods. Extracted skills are canonicalized through alias-based normalization to reduce fragmentation while preserving the ability to incorporate emerging tools and practices. Skills are weighted based on prevalence and whether they are identified as required or preferred, producing role–skill profiles that balance stability with sensitivity to change. Course-level skill representations are constructed from syllabi, catalog descriptions, and prerequisite context, acknowledging the incompleteness and variability of instructional artifacts. Course–career alignment scores are then computed as comparative relevance signals intended to support exploration and reasoning. The framework was integrated into a deployed, student-facing system that visualizes prerequisite structure and overlays career relevance without prescribing outcomes. Evaluation examined technical properties such as coherence, stability across refresh cycles, and responsiveness to emerging skills, as well as student-reported understanding and confidence. A comparison against the ONET 29.1 reference database found that between 91% and 99% of extracted skills have no direct ONET equivalent, and that O*NET descriptors account for only approximately 31% of skill demand by posting volume, providing direct empirical justification for the open-vocabulary approach.