Neutrosophic Latent Semantic Uncertainty Mapping for Evaluating Interpretive Vocabulary in University Fine Art Classroom Teaching Analysis

Abstract

This paper introduces a novel statistical-neutrosophic framework titled Neutrosophic 
Latent Semantic Uncertainty Mapping (NLSUM), designed to quantify and model the 
interpretive uncertainty embedded within students' descriptive vocabulary in university 
fine art classrooms. Unlike conventional assessment techniques that rely on rubrics or 
scoring matrices, NLSUM analyzes the semantic ambiguity, truthfulness, and 
contradiction of art-related terminology used by students in their critiques, reflective 
writings, and verbal interpretations. Each descriptive term is transformed into a triplet
valued uncertainty vector reflecting its truth alignment with standard academic 
interpretations, its inherent semantic indeterminacy, and its potential deviation from 
artistic intent. We present a complete mathematical model of NLSUM, define uncertainty 
operators over lexical structures, and introduce a semantic entropy measure for ranking 
conceptual clarity. Using real data from student evaluations, we generate visual 
neutrosophic maps of linguistic deviation and identify latent zones of high instructional 
confusion. This framework introduces a new way to interpret fine art education through 
the lens of probabilistic semantics, offering practical applications in curriculum design, 
personalized instruction, and linguistic skill development for art students. 

DOI: 10.5281/zenodo.16754954