They Think AI Can Do More Than It Actually Can: Practices, Challenges, & Opportunities of AI-Supported Reporting In Local Journalism

2026-02-26

AI Summary

This study addresses the challenges local news organizations face in harnessing the potential of AI-assisted data journalism, which are often constrained by cognitive biases, limited technical capacity, and difficulties in workflow integration. Drawing on semi-structured interviews with 21 local journalists in Germany, the research adopts a sociotechnical perspective combined with discursive design methods to systematically examine their cognitive limitations, practical approaches to AI use, and visions for ideal AI systems. Findings reveal that, despite limited understanding of AI capabilities, journalists widely anticipate its utility in data processing and story discovery. Building on these insights, the paper proposes design recommendations for AI support systems tailored to the local news ecosystem and embedded within existing journalistic workflows, offering both theoretical and practical pathways for human-AI collaboration in news production.

Technology Category

• Humans and AI: AI for Accessibility
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Declining newspaper revenues prompt local newsrooms to adopt automation to maintain efficiency and keep the community informed. However, current research provides a limited understanding of how local journalists work with digital data and which newsroom processes would benefit most from AI-supported (data) reporting. To bridge this gap, we conducted 21 semi-structured interviews with local journalists in Germany. Our study investigates how local journalists use data and AI (RQ1); the challenges they encounter when interacting with data and AI (RQ2); and the self-perceived opportunities of AI-supported reporting systems through the lens of discursive design (RQ3). Our findings reveal that local journalists do not fully leverage AI's potential to support data-related work. Despite local journalists' limited awareness of AI's capabilities, they are willing to use it to process data and discover stories. Finally, we provide recommendations for improving AI-supported reporting in the context of local news, grounded in the journalists' socio-technical perspective and their imagined AI future capabilities.

Problem

• AI-supported reporting
• local journalism
• data journalism
• AI capabilities
• newsroom automation

Innovation

• AI-supported reporting
• local journalism
• discursive design
• data journalism
• human-AI collaboration