StuLAC : An Adaptive LLM-Driven Framework for Scalable Student Feedback Analysis in Software-Driven Educational Systems

With the growing scalability challenges in higher education, automated student feedback analysis has become crucial for course evaluation and pedagogical improvements. However, traditional methods struggle to handle mixed sentiments, adapt to evolving feedback trends, and maintain computational efficiency. To address these challenges, we propose StuLAC, a Software Engineering-driven framework that integrates Large Language Models (LLMs) with Adaptive Template-Based Caching (ATC). StuLAC employs hierarchical matching for fine-grained classification and dynamically updates feedback templates through context-aware cache refinement. Empirical results on 80,000 student feedback entries demonstrate that StuLAC-generated summaries improve overall quality by 10.5% compared to manually generated reports, while also achieving faster processing times. Additionally, StuLAC attains an 86.4% accuracy and an 86.24% F1-score in sentiment detection. StuLAC’s Feedback Summary Generation provides actionable insights that enhance data-driven decision-making in educational settings. These findings establish StuLAC as a scalable and adaptive solution for improving AI-driven educational feedback systems.