AI is turning research into a scientific monoculture | Communications Psychology

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AI is turning research into a scientific monoculture

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Human behaviour<br>Publishing<br>Research management

Generative AI deserves scientific attention. But the rush to study it is producing a feedback loop of topical and methodological convergence, flattening scientific imagination and crowding out the pluralism needed to keep research adaptive, resilient, and intellectually generative.

Conferences, journals, and funding calls in the social and behavioural sciences are increasingly dominated by (generative) AI1. Many academics have rebranded themselves as “AI researchers”. Every project finds its “AI angle.” This shift is understandable and important: generative AI is a consequential technological development, and psychologists and behavioural scientists are well-positioned to examine its impacts2. But this focus is becoming all-encompassing. The New Yorker recently argued that AI is “homogenizing our thoughts”3: that by repeatedly surfacing the most probable continuations of human thought, these systems are nudging human reasoning toward conformity. Ironically, scientific culture is drifting toward a meta-version of that claim. While earlier work warned that increasing AI-adoption may lead to a scientific monoculture4, empirical evidence now suggests this process is underway5. In studying AI, research practices are themselves becoming more uniform - converging not only in what is studied, but in how questions are framed, investigated, and evaluated. Understanding this convergence as a feedback loop rather than an unavoidable trend opens the possibility of targeted interventions to preserve scientific diversity before monocropping becomes fully entrenched.

The Rush Effect<br>Across social science disciplines, a race has emerged to show what AI can do1. The logic is partly pragmatic: funders, journals, and institutions reward topicality. But it is also cultural. To not work on AI is increasingly perceived as a missed opportunity, or worse - irrelevance. Ironically, AI tools themselves amplify this acceleration. Recent evidence shows AI-augmented papers are cited more, AI-adopting researchers publish more, and career advancement accelerates5. As scientists increasingly use LLMs to generate ideas and synthesise literatures4 - often on the topic of AI itself - the technology feeds its own growth, increasing the pace of production6, while narrowing the space for slow, divergent thinking4,5.

The feedback loop<br>What follows is a self-reinforcing cycle of topical, methodological, conceptual, and linguistic convergence (see Figure 1). The AI-monoculture feedback loop spans multiple levels, from broad techno-cultural salience and institutional incentives to methodological practices and epistemic feedback. Recent large-scale evidence shows that several components of this loop are already in motion: for example, AI adoption is associated with strong individual-level incentives, shared methodological uptake, and system-level outcomes such as reduced topical breadth and scientific engagement5. Together, these findings indicate that the dynamics underpinning scientific monocropping are no longer hypothetical but actively unfolding.<br>Fig. 1: The AI monoculture feedback loop. Full size image

A As AI becomes culturally prominent, characterised by hype, urgency, and intensified societal focus, it attracts attention and legitimacy. This salience is translated into institutional signals about what counts as relevant, timely, and consequential research. B These signals are formalised through research incentives, including funding priorities, journal norms, and career pressures, which reward alignment with AI-centred topics and approaches. As a result, pursuing non-AI work may increasingly carry reputational and career risk. C Incentive structures increasingly favour the use of AI as a general-purpose research instrument—used to generate data, conduct analyses, synthesise findings, and scale scholarly output. As these tools become normalised as default research infrastructure, analytic workflows and methodological practices converge around shared AI-driven pipelines and templates. D As methods converge, so too do the linguistic and conceptual frames used to define and explain research problems. Diverse phenomena are increasingly reframed through a common AI lens and language, as methods shape what is easy to articulate, justify, and evaluate. E AI systems are then used to generate ideas, synthesise literatures, frame research questions, and provide...