Smart Education in Action: AI-Based Quality Assessment of Journalism and Media Teaching Practices using the Neutrosophic Cosine Similarity Measure
Year: 2025
Type: article
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.
Author: Feifan Wang
Language: English
Field: Decision Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6930756375
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