How can AI agents support journalists' work? An experiment with designing an LLM-driven intelligent reporting system

• URL: http://arxiv.org/abs/2510.01193v1
• Date: Mon, 25 Aug 2025 14:56:59 GMT
• Title: How can AI agents support journalists' work? An experiment with designing an LLM-driven intelligent reporting system
• Authors: Vasileios Maltezos, Roman Kyrychenko, Aleksi Knuutila,
• Abstract summary: The integration of artificial intelligence into journalistic practices represents a transformative shift in how news is gathered, analyzed, and disseminated.<n>Large language models (LLMs), particularly those with agentic capabilities, offer unprecedented opportunities for enhancing journalistic practices.<n>This research explores how agentic LLMs can support journalists' filtering, based on insights from journalist interviews.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of artificial intelligence into journalistic practices represents a transformative shift in how news is gathered, analyzed, and disseminated. Large language models (LLMs), particularly those with agentic capabilities, offer unprecedented opportunities for enhancing journalistic workflows while simultaneously presenting complex challenges for newsroom integration. This research explores how agentic LLMs can support journalists' workflows, based on insights from journalist interviews and from the development of an LLM-based automation tool performing information filtering, summarization, and reporting. The paper details automated aggregation and summarization systems for journalists, presents a technical overview and evaluation of a user-centric LLM-driven reporting system (TeleFlash), and discusses both addressed and unmet journalist needs, with an outlook on future directions for AI-driven tools in journalism.

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