Journal Editors, Generative AI, and the Future of Academic Writing

Description

Generative artificial intelligence is rapidly reshaping academic writing and publishing, raising fundamental questions about authorship, responsibility, transparency, and the conditions under which scholarly knowledge is produced. While much of the current debate focuses on tools, detection, or individual author behaviour, less attention has been paid to how generative AI interacts with existing structural pressures in global higher education, including publication metrics, peer review capacity, and widening institutional inequalities.

 

This interview examines generative AI not as a discrete technological disruption, but as an amplifier of long-standing tensions in academic publishing, particularly between speed and rigour, productivity and epistemic care. It explores how generative AI differs from earlier forms of digital writing support by intervening in cognitive and interpretive aspects of scholarly work, thereby complicating established assumptions about contribution and accountability.

 

Drawing on empirical insights from a qualitative study of applied linguistics journal editors, the interview then turns to the perspective of those responsible for curating the scholarly record. It examines how editors are navigating ambiguous policies, increased submission volumes, reviewer fatigue, and growing uncertainty around acceptable and unacceptable uses of generative AI. The discussion highlights why transparency, while necessary, is insufficient in the absence of shared, discipline-sensitive norms.

 

The interview concludes by arguing that debates about generative AI in academic writing must move beyond questions of compliance and detection, towards collective governance, disciplinary responsibility, and a rethinking of what responsible knowledge production should look like in an AI-mediated publishing landscape.