Leveraging Large Language Models for Scalable Analysis of the End-of-the-Course Student Feedback

Jelena Jovanović1, Irena Vodenska2, Vladan Devedžić3,1,4

  1. Faculty of Organisational Sciences, University of Belgrade
    Jove Ilica 154, 11000 Belgrade, Serbia
    jelena.jovanovic@fon.bg.ac.rs, vladan.devedzic@fon.bg.ac.rs (corresponding author)
  2. Administrative Sciences Department, Metropolitan College, Boston University
    1010 Commonwealth Avenue, Boston, MA 02215
    vodenska@bu.edu
  3. Key Laboratory of Computing Power Network and Information Security, Ministry of Education,
    Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
  4. Serbian Academy of Sciences and Arts, Knez Mihailova 35, Belgrade, Serbia

Abstract

Analyzing open-ended student feedback in course evaluations is a labor-intensive task due to the unstructured and complex nature of natural language. While Large Language Models (LLMs) offer significant potential for automation, a well-defined methodology for their application in analyzing student feedback remains underdeveloped. This paper addresses this gap by proposing an LLM-based feedback analytics pipeline designed to transform students’ open-ended feedback into structured, actionable insights. The pipeline consists of three sequential stages: (i) segmenting student feedback into semantic units and assigning polarity (sentiment) to those units; (ii) topical classification of semantic units, and (iii) summarization of units within each topical category. By systematizing these processes, the proposed method enables educators and course managers to efficiently derive meaningful patterns from vast datasets of student opinions. We evaluated the proposed method using a comprehensive dataset from several editions of a U.S. university course, yielding encouraging results of the method’s effectiveness. This research provides a scalable, generic methodology for (semi-)automated feedback analysis, ultimately supporting data-informed improvements in teaching and course management.

Key words

Student Feedback, Course Evaluation, Natural Language Processing, Large Language Models, Qualitative Coding

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS260120032J

Publication information

Volume 23, Issue 4 (September 2026)
Year of Publication: 2026
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Jovanović, J., Vodenska, I., Devedžić, V.: Leveraging Large Language Models for Scalable Analysis of the End-of-the-Course Student Feedback. Computer Science and Information Systems, 23(4) (2026). https://doi.org/10.2298/CSIS260120032J