Much of higher education’s conversation about generative AI has focused on academic integrity. That makes sense. Faculty have real questions about authorship and what it means for students to demonstrate their own learning. These are important questions, and universities need clear guidance. But if our institutional conversation about AI begins and ends with cheating, we are answering too small a question.
Students are not only opening generative AI when an essay is due. They are turning to it between classes, late at night, before difficult conversations, and in the middle of decisions they are not sure how to make. It helps them rehearse, reword, sort through options, and steady themselves. For many students, generative AI is becoming part of daily life.
That means AI is not only a classroom issue. It is becoming part of the student experience.
This raises questions that belong squarely in student success and student affairs. How are students using AI when they feel stuck, overwhelmed, isolated, or unsure where to turn? What happens when a student asks a chatbot for help interpreting a university policy, preparing for a difficult conversation with an instructor, choosing a major, managing conflict with a roommate, or deciding whether a concern is serious enough to bring to a person? These uses may not appear in an academic misconduct report, but they can still shape whether students feel supported, confused, connected, or alone.
The point is not that universities should monitor students’ private AI use. Nor is it that AI should be treated as a substitute for advising, counseling, mentoring, or care. The point is that students are already bringing AI into moments that have consequences for belonging, persistence, confidence, and decision-making. The question is at what point does an institutional response to AI acknowledge this broader use?
Faculty and staff need support for thisconversation. Instructors cannot manage AI entirely through syllabus statements and detection tools, and student affairs professionals should not be left out of the discussion because AI first arrived on campus as a plagiarism concern. We need shared language for helping students understand when AI may be useful, when it may be unreliable, and when the next step should be a human conversation.
The work ahead is not simply to catch misuse. It is to understand how AI is entering the ordinary places where students seek direction, reassurance, language, and support. That makes AI a learning issue, but also a student success issue.
A whole-student approach to generative AI asks different questions. Not only, “Did the student use AI?” but “Where are students turning when they need help?” “What kinds of guidance are they receiving?” “Where might AI reduce friction?” “Where might it increase risk?” “How do we help students stay connected to people, resources, and communities that can actually support them?”
Universities need to pay attention to how students are really using these tools and build guidance that reflects the whole student, not just the submitted assignment.