Smart Education in Action: AI-Based Quality Assessment of Journalism and Media Teaching Practices using the Neutrosophic Cosine Similarity Measure
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...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
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University of New Mexico
2025-05-01
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| Series: | Neutrosophic Sets and Systems |
| Subjects: | |
| Online Access: | https://fs.unm.edu/NSS/28SmartEducation.pdf |
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| Summary: | 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. |
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| ISSN: | 2331-6055 2331-608X |