Designing an Optimal Model for Artificial Intelligence Use in the News Cycle (Case Study: IRIB News Agency)
Keywords:
Artificial Intelligence, News Cycle, Grounded Theory, IRIB News Agency, Paradigmatic ModelAbstract
This study aims to design and conceptualize an optimal model for the application of artificial intelligence in the news cycle while preserving professional quality and operational efficiency. This applied-developmental study adopted a qualitative approach using grounded theory (Strauss & Corbin). Data were collected through library research and semi-structured interviews with 12 experts, including media managers, editors, and scholars in media and AI. Participants were selected through purposive sampling until theoretical saturation was reached. Data were analyzed via open, axial, and selective coding using MAXQDA24 software. The findings indicate that causal conditions such as transformation in content production, increased speed and accuracy, big data growth, and competitive pressure significantly influence the core phenomenon of AI application in the news cycle. Contextual conditions (organizational infrastructure and culture) and intervening conditions (legal and ethical challenges) shape strategic actions toward implementing an integrated intelligent news system. These strategies lead to improved news quality, enhanced newsroom agility, better audience engagement, and strengthened competitive advantage. Developing an integrated and stage-based AI model for the news cycle can simultaneously enhance efficiency, accuracy, and media credibility while mitigating professional and institutional risks.
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