IDEIA: A Generative AI-Based System for Real-Time Editorial Ideation in Digital Journalism

The preprint operates within the contemporary paradigm of digital journalism, which acknowledges the transformative impact of technology on news production and consumption. This paradigm emphasizes the need for speed, thematic relevance, visual appeal, and adaptability in journalistic practices. Within this broader framework, the study specifically addresses the emerging sub-paradigm of AI-assisted journalism, focusing on the integration of generative AI tools for content ideation and creation.

The preprint presents IDEIA, an AI-powered tool developed to enhance editorial efficiency in digital journalism. It integrates Trends and API to automate topic suggestion and content drafting, addressing a clear need within the existing paradigm. The study demonstrates a "normal science" approach by:

1. Articulating a problem within the accepted paradigm: The slow and manual process of content ideation in digital journalism, contrasted with the need for speed and efficiency.

2. Proposing a solution based on established tools and techniques: Leveraging readily available APIs and agile development methodologies.

3. Evaluating the solution's effectiveness within the paradigm's framework: Measuring time savings, workflow improvements, and user feedback, aligning with the paradigm's values of speed and efficiency.

While IDEIA leverages cutting-edge AI technologies, its primary contribution lies in applying these within the existing paradigm of digital journalism, optimizing established practices rather than fundamentally challenging them. Hence, the classification as "Normal Science" is deemed most appropriate. There is a subtle nuance of model drift, which could be considered a secondary classification. By introducing AI-driven automation into the editorial workflow, IDEIA potentially initiates a shift in practices, potentially leading to further development and refinement of journalistic processes. However, as the study focuses primarily on enhancing current workflows, the dominant characteristic remains aligned with "normal science".