General Education in the Age of Artificial Intelligence: Assessing Student Judgment Beyond the Final Product

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Generative AI challenges the evidence that universities use to certify general education learning. A polished final product can no longer show, by itself, that a student can reason, write, evaluate information, use data, or make responsible judgments. This paper argues that general education should move from course completion alone to a competency-and-practice model. In this model, students first demonstrate core abilities without AI assistance. They then use AI in structured assignments where they must disclose use, verify outputs, revise results, and explain their decisions. The paper applies this model to thinking, communication, quantitative and data reasoning, AI and information literacy, ethical and civic judgment, and integrative learning. It also proposes a dual-condition assessment design that separates independent competence from documented AI-assisted performance.