NLP-Based Personalized Feedback in Education: A Systematic Review of Sentiment Analysis and Civic Disposition - Shofi Nurul Fath; Taat Wulandari; Shara Rafiqa Nurulcholillah Sukri (Jurnal Kewarganegaraan, 2026)
This study aims to examine the development of Natural Language Processing (NLP) in education, particularly in sentiment analysis and personalized feedback, as well as its relevance to strengthening students’ civic disposition. The research employed a qualitative approach using the Systematic Literature Review (SLR) method with PRISMA guidelines. Data were collected from Scopus, ScienceDirect, ERIC, and Google Scholar databases covering publications from 2021–2025. The findings indicate that NLP has been widely used to analyze students’ emotions, learning engagement, and adaptive feedback systems in digital learning environments. However, most studies still focus on academic and emotional dimensions, while the integration of NLP with civic disposition remains limited. This study concludes that NLP has significant potential to support more humanistic, adaptive, and civic-oriented learning through reflective feedback systems that encourage empathy, tolerance, and democratic participation among students.