This work presents a case study of a Large Language Model based system for automated classification of student survey responses. The system processes 22,286 open-text responses collected from 2062 students across 12 academic programs and 21 nationalities spanning the years 2010–2025. The system architecture has been deployed on institutional servers for security, while integrating databases, an asynchronous task queue for processing, a web-based service layer, and distributed background workers that interact with remote LLM inference services. This work provides a practical reference framework for educational institutions aiming to responsibly and effectively operationalize LLMs in real-world applications.
Gutiérrez-Leal et al. (Sun,) studied this question.
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