Algorithmic scholarship and academic evaluation: governance misalignment in the age of generative AI
IntroductionGenerative artificial intelligence (AI), particularly large language models, is rapidly becoming embedded in academic research and scholarly publishing. These systems assist with drafting, literature synthesis, and analytical writing, increasingly contributing to the production of...
IntroductionGenerative artificial intelligence (AI), particularly large language models, is rapidly becoming embedded in academic research and scholarly publishing. These systems assist with drafting, literature synthesis, and analytical writing, increasingly contributing to the production of academic text. This shift raises a central question: how should higher education institutions evaluate scholarly contribution when parts of research production become technologically mediated?MethodsThis paper examines governance misalignment between publication standards and institutional evaluation systems in higher education. Drawing on an exploratory qualitative survey of 18 journal editors and associate editors across business-related disciplines, we analyze editorial perspectives on AI-assisted manuscript preparation, authorship, accountability, productivity, and academic evaluation.ResultsWe identify three recurring concerns: policy fragmentation, ambiguity surrounding authorship and accountability, and apprehension about AI-enabled productivity acceleration. We introduce the concept of metric distortion to describe the weakening relationship between measurable scholarly outputs and the intellectual labor those outputs are assumed to represent under conditions of AI-assisted production. As drafting and textual production become more technologically scalable, publication-based indicators may become less reliable proxies for conceptual contribution and interpretive effort.DiscussionBuilding on scholarship on academic capitalism, audit culture, and digital governance, the paper argues that generative AI functions as a stress test for existing evaluation regimes. We propose a process-based framework emphasizing disclosure, documented intellectual contribution, and institutional alignment between editorial governance and tenure evaluation systems.Source: Frontiers AI — Published — Category: Research