Learning to report with AI: Journalism students’ professional identity and boundary work in the age of generative tools

Abstract

This article examines how journalism students in China learn to report with generative AI tools while trying to uphold core journalistic values. It asks how students describe AI in relation to what they see as central journalistic values and how they draw boundaries between acceptable assistance and unethical substitution. Working within an interpretivist approach, the study uses semi-structured interviews with 46 journalism students and thematic analysis informed by journalism as occupational ideology and boundary work in journalism. Findings show first that students rework core journalistic values by treating diligence as legitimate human labour, truth seeking as human verification, originality as authorship and voice, and public accountability as a non-transferable human responsibility. Second, they construct a layered boundary regime that separates clearly acceptable support, conditional structural help and clearly unacceptable substitution, reinforced by personal feelings, peer talk and institutional rules. To capture this pattern, the article develops the concept of effort anchored assistance ethics (EAAE). It reveals how the unique educational setting drives students to project the academic valuation of human effort onto professional boundary work, establishing cognitive diligence as the primary jurisdictional defense against automated semantic generation.