Smart Education in Action: AI-Based Quality Assessment of Journalism and Media Teaching Practices using the Neutrosophic Cosine Similarity Measure

Abstract

The evolution of artificial intelligence (AI) has sparked a transformative shift in 
journalism and media education, urging academic institutions to reevaluate traditional 
pedagogies. As journalism integrates with intelligent tools—from automated news writing to 
data-driven content curation, there is a rising demand to align teaching quality with emerging 
media practices. This study presents a comprehensive decision-making framework to assess the 
quality of teaching practices in journalism and communication programs, addressing the growing 
intersection of AI and media education. Through multi-criteria decision-making (MCDM) 
techniques, this research captures the complexities of integrating smart technologies in 
curriculum delivery, student engagement, and pedagogical innovation. Eight evaluation criteria 
and eight representative teaching models or institutions are analyzed to offer a holistic view of 
intelligent teaching quality. The Neutrosophic Cosine Similarity Measure is used to deal with 
uncertainty information. Two MCDM methods are used, such as DEMATEL method to show the 
criteria weights and the MARCOS method to rank the alternatives. 

DOI: 10.5281/zenodo.15171353