A growing focus on student mental health is shifting toward behavioral early-warning approaches that connect engagement data across siloed systems. One article argues campuses can identify disengagement risks—such as reduced LMS activity, missed advising, and pauses in financial-aid steps—before grades or withdrawal signals appear. The approach positions AI as a connector across student information systems, learning management platforms, and support interactions, aiming to surface patterns for proactive outreach rather than crisis response. It emphasizes that the signals are behavioral indicators, not demographic predictions. For student success leaders, the move reflects a practical response to retention breakpoints: if students disengage silently, counseling-center usage rates remain too low. Early detection strategies may therefore become a key lever in helping students stay enrolled and engaged.