As generative artificial intelligence becomes embedded in digital education, universities face a governance problem that institutional guidance alone cannot resolve: how should acceptable AI use be defined within particular courses and assessments? This study examines syllabi as formal governance texts through which university principles become student-facing rules.
Using qualitative comparative document analysis, the study analyzed 35 syllabi collected from a large U.S. public research university, including 22 from Education and 13 from other fields. Course-level statements were compared with the institutional governance framework. The analysis distinguished primary governance stances and examined institutional alignment, policy rationales, discourse registers, and variation across course contexts.
The institutional framework delegated substantial authority to instructors while emphasizing communication, attribution, verification, and student responsibility. Course-level enactment was highly heterogeneous: 11 syllabi were silent on student AI use, 11 were prohibitive, five permitted specified uses, two broadly permitted AI with responsibility safeguards, and six treated AI or machine learning as an object of pedagogical or professional learning. Six syllabi were explicitly aligned with institutional guidance, seven implicitly aligned, eight elaborated the institutional framework, three provided minimal guidance, and 11 remained silent; none directly contradicted a specific institutional requirement. Authentic or independently produced work was the most common rationale, appearing in 18 of 24 non-silent syllabi. Governance patterns crossed disciplinary boundaries, while all pedagogical cases were concentrated in AI-adjacent courses.
The findings challenge simple disciplinary explanations and show that course-level AI governance is shaped by the interaction of assessment design, authorship expectations, course purpose, AI adjacency, and instructor discretion. Variation is best understood as an outcome of delegated digital governance whose educational value depends on clarity, justification, and alignment with the intellectual work students are expected to perform.
Generative artificial intelligence is often framed in higher education as a problem of academic integrity, assessment security, or technology adoption. This framing is necessary but insufficient for doctoral education, where writing, reading, coding, synthesizing literature, and interpreting evidence are not merely academic tasks but formative practices through which students become scholars.
Based on qualitative interviews with twenty-one doctoral students at a large research university in the United States, this study examines how doctoral students understand and negotiate generative AI in their scholarly work. The study began with students in education and was extended through purposive and snowball recruitment to include students across a range of other disciplines, so that the account would reflect more than one scholarly context; interviews were semi-structured.
The findings show that AI functions as an access infrastructure, lowering linguistic barriers for some students and technical barriers for others depending on the demands of their scholarly work. At the same time, students engage in careful boundary work between assistance and authorship, distinguishing grammar support, translation, coding help, and conceptual orientation from intellectual substitution. The analysis further suggests that, among these participants, generative AI is shifting doctoral labor from production toward verification: students' distinctive responsibility increasingly lies in judging the accuracy, legitimacy, ownership, and defensibility of machine-assisted work. Under conditions of policy ambiguity, doctoral students also become primary governors of their own AI use, managing disclosure, caution, verification, and risk.
The article argues that the leadership of digital education should move beyond broad AI policies toward context-sensitive guidance, verification literacy, transparent disclosure norms, and process-based assessment, including the culminating site of doctoral assessment, the dissertation defense. These claims are offered as analytic propositions grounded in a single-site interpretive study rather than as generalizable findings. Generative AI has not made doctoral education less necessary; it has made its purposes more urgent.