Will the widespread use of large language models in scientific writing undermine scientists’ critical thinking?

Checked 30 Sept 2026. Open license (CC-BY, CC-BY-SA, CC0, public domain): full text indexed and used in synthesis.

Artificial intelligence (AI) is rapidly transforming scientific writing by expanding access and efficiency, yet it risks decoupling writing from thinking. Scientific writing is a core cognitive and epistemic practice that must be cultivated and preserved alongside AI use.

• Bietti & Bangerter (2026) cite prevalence estimates of AI-generated text in biomedical papers, consistent with the excess-vocabulary work, and describe LLM use as swift and largely unregulated, shifting debate toward disclosure norms and accountability. Trace →
• Language support is a recurring rationale for LLM use and may bear on where adoption grows fastest. Kobak et al. 2025 and Bietti & Bangerter 2026 both note help for writing in English. He & Bu 2026 observe the highest growth in non-English-speaking countries. Trace →
• Detecting LLM text is methodologically fraught. Bietti & Bangerter 2026 note that detection systems face significant limitations. Kobak et al. 2025 argue that prior detection studies relied on potentially biased ground-truth corpora, which their direct excess-vocabulary approach avoids. Trace →
• Beyond accuracy, several authors warn of an epistemic cost. Messeri & Crockett 2024 caution that AI may lead science to produce more but understand less. Bietti & Bangerter 2026 argue that outsourcing writing risks decoupling it from thinking and eroding core reasoning skills. Trace →
• Messeri & Crockett 2024 and Bietti & Bangerter 2026 raise complementary epistemic alarms about AI in science: the former warn of a phase of enquiry in which we produce more but understand less, while the latter argue that outsourcing writing to LLMs risks decoupling writing from thinking, since scientific writing is itself a form of thinking. Trace →
• Even papers warning about generative AI's risks concede a democratizing benefit: Bietti & Bangerter 2026 note LLMs can support non-native English speakers by reducing linguistic barriers, and Bechky & Davis 2024 observe that generative AI lowers barriers for non-native speakers to produce reviewer-palatable text, democratizing access to publication. Trace →
• Bietti & Bangerter 2026 report that a recent study estimated more than one in 10 biomedical papers published in 2024 contained AI-generated text, often without disclosure, consistent with Kobak et al. 2025's lower bound of at least 13.5% of 2024 abstracts. They argue that using such tools can risk decoupling writing from thinking. Trace →
• Messeri & Crockett 2024 warn that the proliferation of AI tools in science risks a phase of scientific enquiry in which we produce more but understand less, anticipating Bietti & Bangerter 2026's contention that scientific writing is not merely communication but a form of thinking. Trace →
• The language-equity rationale for LLM-assisted writing recurs across papers: Kobak et al. 2025 note that LLMs can help translate to English, Bietti & Bangerter 2026 note that LLMs can support non-native English speakers by reducing linguistic barriers, and He & Bu 2026 find that non-English-speaking countries exhibit the highest growth rates in AI writing tool use. Trace →
• Bietti & Bangerter 2026 anchor their warning that LLMs risk decoupling writing from thinking in prevalence estimates — more than one in ten biomedical papers published in 2024 and over 20% in some fields — consistent with the lower bounds Kobak et al. 2025 derived from excess vocabulary, showing how measurement studies now feed the normative debate. Trace →
• Two perspective pieces converge on the epistemic stakes of AI-assisted science: Messeri & Crockett 2024 warn that proliferating AI tools risk a phase of enquiry in which we produce more but understand less, while Bietti & Bangerter 2026 argue that outsourcing writing to LLMs risks decoupling writing from thinking. Trace →
• Fortenbach et al. 2026 find that LLM-generated text is now common in ophthalmology: by 2025, 25.7% of sampled research articles and 21.6% of commentary articles carried AI-likelihood scores more than two standard deviations above baseline, and none of these outlier publications disclosed AI use. Trace →
• Bietti & Bangerter 2026 and Messeri & Crockett 2024 articulate a shared epistemic worry: because scientific writing is itself a form of thinking, outsourcing it to LLMs risks a mode of science that produces more text while understanding less. Trace →
• The democratizing promise of LLMs for non-native English writers, acknowledged even by critics such as Bietti & Bangerter 2026, is consistent with He & Bu 2026's large-scale finding that non-English-speaking countries exhibit the highest growth rates in AI-assisted writing. Trace →
• Two commentaries converge on an epistemic risk of LLM adoption in science: Messeri & Crockett 2024 warn of a phase in which we produce more but understand less, and Bietti & Bangerter 2026 argue that outsourcing writing to LLMs risks decoupling writing from thinking. Trace →
• There is a fairness tension between detection and equity: Bietti & Bangerter 2026 note LLMs can support non-native English speakers by reducing linguistic barriers, yet the detection tools used to police LLM use are biased against non-native English writers (Liang et al., cited in She 2026). Trace →
• Both Binz et al. 2025 and Walters & Wilder 2023 caution that verification costs may erode LLM efficiency gains: the trust routinely placed in ordinary software is inappropriate for generative AI, and time saved in text generation may be offset by the time required to verify the output. Trace →

• Research integrity and LLMs