CS-Guide: Leveraging LLMs and Student Reflections to Provide Frequent, Scalable Academic Monitoring Feedback to Computer Science Students

Abstract: Computer Science (CS) departments often serve large student populations, making timely academic monitoring and personalized feedback difficult. While the recommended counselor-to-student ratio is 250:1, it often exceeds 350:1 in practice, leading to delays in support and interventions. We present CS-Guide, which leverages LLMs to deliver scalable, frequent academic feedback. Weekly, students interact with CS-Guide through self-reported grades and reflective journal entries, from which CS-Guide extracts quantitative and qualitative features and triggers tailored interventions (e.g., academic support, health and wellness referrals). Thus, CS-Guide uniquely integrates learning analytics, LLMs, and actionable interventions using both structured and unstructured student-generated data. We evaluated CS-Guide on a four-year, ~20K-entry longitudinal dataset, and it achieved up to a 97% F1 score in recommending interventions for first-year students. This shows that CS-Guide can enhance advising systems with scalable, consistent, timely, and domain-specific feedback.