Analysis of Emotional Impact in Educational Recommender Systems Using Neutrosophic Psychology

Abstract

This study aimed to analyze how neutrosophic psychology can model the emotional impact in educational recommender systems within virtual learning environments, seeking to enhance personalization by capturing ambiguous or contradictory student emotions. A systematic review based on the PRISMA method was conducted, analyzing articles published between 2013 and 2023 in databases such as IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, ERIC, and Web of Science, using search strings that combined terms such as “recommender systems,” “emotional impact,” and “neutrosophic psychology.” The results revealed that neutrosophic models, by integrating truth, falsity, and indeterminacy, improve the accuracy of recommendations by considering complex emotions, such as motivation combined with anxiety. However, challenges remain, including the scarcity of emotional data, computational complexity, and institutional resistance. It is concluded that neutrosophic psychology offers an innovative framework to optimize recommender systems, fostering more empathetic and adaptive education. Future perspectives point toward the integration of artificial intelligence and real-time analysis to overcome technical and pedagogical barriers, with empirical studies recommended to validate these models in real educational contexts.