Introducing the Neutrosophic Reflexivity Operator: Modeling Recursive Truth Dynamics in Intelligent Teaching of Journalism and Communication Curricula under AI-Mediated Ambiguity

Abstract

This paper proposes a novel mathematical construct in neutrosophic theory: the 
Neutrosophic Reflexivity Operator (NRO), designed to model recursive uncertainty 
within evolving information systems. The operator captures higher-order dynamics 
where the truth, indeterminacy, and falsity of a proposition are themselves subject to 
variation due to source shifts, temporal distortions, or interpretive recursion. We formally 
define NRO as a self-mapping on neutrosophic vectors and analyze its algebraic 
properties. The model is then applied to the evaluation of journalism and communication 
curricula in the context of AI-media convergence, where epistemic ambiguity is amplified 
by conflicting digital narratives and algorithmic mediation. A practical case study 
evaluates student information processing across recursive media loops. The results 
demonstrate the model’s ability to uncover latent instability in knowledge perception, 
offering educators a novel analytic tool for curricular refinement. 

DOI: 10.5281/zenodo.16734105