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

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.