The authors are careful to frame the work as a hypothesis-generating perspective rather than a controlled study. Its claims, they state, should be read as propositions for testing, not as demonstrated findings. The question grew out of unsystematic expert observations made in teaching and editorial work, observations that motivate the hypothesis but do not test it.

The starting point is a recognizable linguistic signature. Large language models produce text that differs from much human academic prose in word choice, sentence shape, and discourse rhythm. The paper describes candidate linguistic markers of this AI-associated style and asks what happens when readers encounter it repeatedly in published papers, reviews, and other scholarly material.

To make the question precise, the authors define passive exposure as reading text that carries AI-associated features without using an AI tool on one’s own writing. They explicitly separate this from AI-assisted editing and from hybrid human-AI authorship, both of which involve deliberate use of the technology and could confound any observed effect.

The proposed pathway runs from exposure through internalization to convergence. Several established mechanisms could carry it, according to the authors: statistical and implicit learning, mere exposure and frequency effects, linguistic alignment, and experience-dependent neuroplasticity. Each of these processes is documented in the broader cognitive literature, but the paper stresses that their application to AI text remains untested.

Supporting the plausibility of the trend, large-scale corpus analyses show a sharp post-2022 rise in AI-associated vocabulary in published abstracts and in conference peer reviews. Whether that rise reflects changing writing habits, direct AI use, or something else is among the alternative explanations the authors weigh, along with competing human input and the confound with direct AI use.

If convergence does occur, the consequences could reach beyond style. The authors consider what it would mean for assessment, for stylistic diversity in scholarly writing, and for cognitive engagement with the material authors produce.

To move the question from speculation to evidence, the paper closes with ten testable predictions, together with their practical limits and propositions for writing education.

Subject of Research: A hypothesis-generating perspective on whether passive exposure to AI-generated text may change human academic writing.

Article Title: Passive exposure to AI-generated text may reshape human academic writing through established cognitive mechanisms

Article References: Dergaa, I., Ben Saad, H., Chamari, K., Larnaout, A., Ceylan, H. İ., Stefanica, V., Muntean, R.-I., & Dhahbi, W. (2026). Passive exposure to AI-generated text may reshape human academic writing through established cognitive mechanisms. Discover Psychology. https://doi.org/10.1007/s44202-026-00954-7

Image Credits: AI Generated

DOI: 10.1007/s44202-026-00954-7

Keywords: AI-generated text, academic writing, passive exposure, large language models, linguistic alignment, implicit learning, frequency effects, neuroplasticity, corpus analysis, scholarly communication, Discover Psychology, hypothesis