Human Competencies at the Edge of Automation: A Qualitative Study of AI Integration in Frontline Journalism

The integration of AI into journalism has intensified debates about the future of news production, yet existing scholarship has focused predominantly on AI’s capabilities rather than on irreplaceable human competencies. This study shifts analytical focus from replacement to complementarity, investigating the boundaries of AI through the perspectives of both journalists and AI developers. Ten participants—including field reporters, news anchors, broadcast journalists, and AI developers—were interviewed through in-depth, semi-structured interviews. Thematic analysis revealed three core dimensions of irreplaceable human competency: embodied presence and rapport-building, contextual judgment and meaning-making, and investigative initiative requiring moral agency. Practitioners and developers converged on AI’s persistent limitations in factual reliability, emotional authenticity, and ethical accountability. Based on these findings, a three-tier human–AI collaborative model is proposed, allocating computational tasks to AI while preserving human authority over editorial judgment, source relationships, and ethical decisions. These findings contribute to human–machine communication theory, extend algorithmic journalism literature beyond capability assessments, and offer practical implications for newsroom workflow design, journalism education, and AI governance. Findings are situated within the Korean media context and should be interpreted accordingly, with implications that may extend to other broadcasting-oriented journalism cultures.