A Taxonomy of LLM-Supported Tasks for Critical AI Literacy in Journalism

This paper develops a task-based taxonomy for integrating Large Language Models (LLMs) into journalism education, drawing on task definitions from machine learning and natural language processing alongside journalism studies. Organised around four stages of the news workflow, newsgathering, sensemaking, editing, and publication/distribution, it translates journalistic activities into computationally informed tasks linked to baseline prompts and ethical risks. The taxonomy is designed to help students use LLMs critically while maintaining editorial standards. It addresses key risks, including bias, hallucinations, and overreliance on automation. More broadly, the approach promotes AI literacy in journalism by combining structured practice with ethical reflection and emphasising the importance of human oversight in generative AI–assisted workflows. While grounded in journalism, the taxonomy’s underlying logic is transferable to other knowledge-intensive professions where accuracy, source evaluation, and accountability are at stake, offering a conceptual basis for developing similar taxonomies in other postdigital professional training contexts.