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Large-enrollment undergraduate STEM courses present persistent challenges for student engagement, belonging, and success. While Learning Management Systems (LMS) are widely adopted, their use in face-to-face courses is often limited to grade posting and announcements, overlooking their potential to deliver timely, personalized support at scale. This study examined how automated Brightspace Intelligent Agent messages influence student-perceived support, belonging, and engagement in a large-enrollment microbiology course. A pre-post mixed-methods design was implemented in ABIO314 (Microbiology, 300-level), a participation-based course enrolling 213 majors and non-majors at the University at Albany (UAlbany), a Carnegie R1 SUNY institution, during Spring 2026. Three categories of messages were designed to deliver timely, targeted feedback supporting student self-regulation and engagement: (1) Encouragement and Recognition, (2) Safety Valve Standing Updates, and (3) Performance Alerts and Engagement Reminders. Student perceptions were assessed via anonymous surveys at mid- and end-of-semester, including Likert-scale items (1–5) across three constructs and open-ended questions. All study procedures were approved by the University at Albany Institutional Review Board. Consenting responses were collected from 143 (mid-semester) and 164 (end-of-semester) students. Composite mean scores remained consistently high across both time points (Perceptions: 4.43→4.34; Support & Belonging: 4.23→4.16; Motivation & Engagement: 4.24→4.13; neutral midpoint = 3.0). At the end of the semester, 88% of students agreed that messages had a positive course impact, and 57% rated exam-related support messages as “Very helpful.” Among end-of-semester respondents, 41% reported improved exam performance, and 78% indicated messages had some influence on their exam preparation and engagement. In conclusion, automated instructional messaging via Brightspace Intelligent Agents is a feasible, scalable strategy for reducing barriers to engagement and belonging in large-enrollment STEM courses. Student perceptions remained consistently positive, suggesting that timely, targeted automated feedback can support inclusive instructional design with minimal instructor burden.
