Abstract
Rapid advances in generative artificial intelligence are unsettling long‑standing assumptions about how people learn, work, and remain employable. This portfolio interrogates that disruption from complementary vantage points—university‑level computer‑science training, K–12 pedagogy, and labor‑market ethics—to propose an integrated blueprint for education in the AI epoch.
The technical report diagnoses inefficiencies in the University of Virginia’s current Bachelor of Science and Bachelor of Arts computer‑science pathways and designs a new, stand‑alone School of Computer Science organized around rigorous entrance screening and six focused tracks (e.g., Artificial Intelligence, Cybersecurity). By trimming tangential general‑education requirements and embedding project‑based capstones, the redesign promises deeper expertise, stronger portfolios, and clearer signals to employers.
The STS research paper reaches farther downstream, tracing how schools navigated three prior industrial revolutions to distill lessons for today’s AI upheaval. Anchored in Schön’s theory of reflective practice and examined through the Social Construction of Technology lens, the study argues that pairing project‑based learning with metacognitive scaffolds can harness AI’s efficiencies while safeguarding critical thinking and self‑learning.
Finally, the sociotechnical synthesis knits these threads together, contending that effective AI‑era curricula must balance two imperatives: (1) targeted specialization that equips graduates with domain‑specific depth, and (2) adaptive, ethically informed habits of mind that transcend any single tool or platform. Taken as a whole, the portfolio offers educators, administrators, and policymakers actionable strategies for designing learning experiences—and the institutional structures that support them—that keep pace with, and help to steer, the accelerating arc of artificial intelligence.
