A sweeping new analysis of more than 1.1 million scientific papers and preprints finds that the use of large language models (LLMs) to write or edit manuscripts rose sharply after the launch of ChatGPT in late 2022, with roughly 22.5% of computer science abstracts showing statistical signs of LLM modification by September 2024. The study applied a word‑frequency model trained to detect subtle linguistic fingerprints left by AI tools, and it uncovered fast-growing use across many fields — a trend that poses practical questions for research integrity, peer review and academic practice in Thailand as research institutions and journals grapple with both the promise and the pitfalls of generative AI.
This finding matters to Thai readers because it is not only a global phenomenon: Thailand is actively building national AI capacity and increasing its research output across science and engineering disciplines. If one in five papers in a technology‑rich field like computer science contains AI‑modified text internationally, Thai universities, funders and journals must consider how to adapt policies, safeguard quality and harness AI to improve writing while avoiding the risks of hallucinated claims or undetected machine authorship. The rapid uptake observed by the international study suggests Thai researchers may already be using generative tools in drafting, editing and translation — sometimes transparently, sometimes not — and that has consequences for scholarly standards, public trust and the reproducibility of scientific work produced in Thailand.
The new analysis examined abstracts and introductions across preprint servers and selected journals from 2020 through September 2024, training a model on pairs of human‑written and LLM‑generated paragraphs to learn which words and phrases become disproportionately common in AI‑assisted text. Words such as “pivotal,” “intricate,” “showcase” and earlier red flags like “regenerate response” and “my knowledge cutoff” were among the markers used to flag likely AI influence. The researchers reported a pronounced jump in flagged content within months of ChatGPT’s release and found the pattern was strongest in disciplines most closely connected to AI itself: computer science topped the list at 22.5%, with electrical systems and engineering sciences close behind, whereas mathematics and some physical sciences showed lower but rising rates. Complementary analyses in biomedical literature suggest one in seven biomedical abstracts in 2024 may contain AI‑assisted text, reinforcing that no field is immune.
Experts responding to the study highlight a mix of admiration for the methodological rigor and concern about downstream effects. A research literacy and communications instructor at a U.S. university described the work as “really impressive,” noting that knowing which disciplines show the greatest uptake can help tailor detection and regulation efforts. The lead computational biologist on the project pointed out that the speed of the adoption — visible only months after a major LLM release — indicates researchers turned to these tools immediately in their writing workflows. A data scientist familiar with large bibliographic datasets called the statistical approach “very solid” while warning that actual AI use may be higher than measured; as particular words became recognized as AI fingerprints, authors began editing them out, making detection progressively harder.
For Thailand, the immediate implications fall into three interlocking areas: research integrity, language equity and capacity building. First, journals and research offices must decide whether to require transparent disclosure of AI assistance and how to verify such statements. Many international journals issued guidance quickly after 2022, forbidding listing an LLM as an author while allowing limited use if declared, but enforcement has been uneven. Thai journals and university presses that host local and regional scholarship may need clear, enforceable policies and practical workflows for screening submissions. Second, the uneven performance of AI detectors — which can falsely flag texts written by nonnative English speakers — raises equity concerns for Thai academics who use LLMs to polish English phrasing. Purely punitive approaches risk penalizing those seeking linguistic help rather than substantive intellectual shortcuts. Third, universities should treat LLMs as both a tool and a risk: training scholars in responsible use, documentation, citation of AI‑assisted drafting, and careful factual verification can help integrate generative AI without compromising scholarly standards.
A cultural context helps explain how these dynamics may play out in Thailand. Thai academic culture often emphasizes respect for seniority, harmonious relationships and institutional reputation. Those values can deter whistleblowing and public conflict, which makes internal oversight, transparent institutional policies and confidential reporting channels especially important. Buddhist ethics and family‑oriented social norms that prize honesty and community standing can be invoked to support norms of disclosure and mentorship when deploying AI: senior authors and supervisors should lead by example in declaring AI use and training junior researchers in verification and attribution practices. At the same time, Thailand’s ongoing national push to develop AI competencies across industry and government creates a countervailing pressure to adopt AI tools quickly; policymakers must balance innovation ambitions with safeguards that protect research reliability.
Looking ahead, several likely developments will affect Thai research ecosystems. Journals worldwide are expected to refine submission checklists to include standardized AI‑use statements and may experiment with random screening of manuscripts’ non‑analysis sections. Detection tools will continue improving but will not be foolproof; as the study notes, authors can scrub telltale phrases and new models may mimic human variability more effectively. A more worrisome prospect is the “feedback loop” risk: if LLMs increasingly generate literature reviews or phrasing that then become part of future model training sets, the originality and factual grounding of literature summaries could erode over time, producing homogenized and potentially less reliable narrative scaffolds for new research. For Thailand, whose research corpus is smaller than larger publishing nations, this feedback effect could be magnified if locally produced AI‑assisted text is incorporated into training data for regional models without verification.
There are also practical opportunities. Carefully managed, LLMs can help Thai researchers overcome language barriers, accelerate manuscript drafting, translate technical concepts for public communication, and help busy clinicians or educators produce clearer patient information or teaching materials. Used as an editing assistant rather than an author, AI can increase productivity and message clarity. However, the crux is verification: LLMs often “hallucinate” — inventing plausible but false citations, data or claims — so any factual content produced or polished by AI must be checked against primary sources and raw data before publication. Thai research funders and universities can convert this tension into an asset by investing in training programs that teach critical prompt design, fact‑checking workflows, and responsible disclosure norms.
What concrete steps should Thai stakeholders take now? Academic journals in Thailand should adopt a simple, mandatory declaration form for submissions that asks authors to state whether and how they used generative AI in drafting, editing or translating text. Editorial offices should train editors and reviewers to spot suspicious patterns, while avoiding bias against nonnative English writing by offering authors a grace period and access to verified language‑editing resources. Universities and graduate programs should incorporate short courses on responsible AI use into research ethics curricula and mentorship programs; supervisors must explicitly discuss acceptable and unacceptable AI practices with students and postdocs during project planning and thesis preparation.
Funding agencies and national policymakers can support infrastructure and standards by funding a national guideline on AI in research, establishing a central helpline for ethical questions about AI use in manuscripts, and sponsoring open tools that help Thai researchers check AI outputs without exposing unpublished data to third‑party platforms. Because detection tools may suffer from biases, public investment in locally validated detection and audit tools — developed in Thai academic collaborations — would help ensure fairness and contextual relevance. Collaboration with ASEAN partners on harmonized standards would also help Thai journals and universities manage cross‑border submissions and shared datasets.
For individual researchers and authors, practical best practices include: use LLMs only for drafting, language polishing and formatting, not for generating primary analyses or novel claims; keep detailed records of prompts and AI outputs used during manuscript preparation; verify every fact, reference and table produced or edited by an AI against original sources; and include a transparent statement in the manuscript’s methods or acknowledgements that describes the nature and extent of AI assistance. Supervisors should require students to submit prompt logs along with drafts during thesis review. Peer reviewers should treat AI‑help declarations as a standard part of review, focusing their assessment on originality, methodological rigor and reproducibility rather than style alone.
Public communication and media outlets in Thailand should also adapt. When reporting scientific findings, journalists and public officials should probe whether major claims were independently verified and whether methodology and raw data are accessible. Given public sensitivity to health and technology claims, especially during crises, clear labeling of AI‑assisted text and a cultural norm of verification before wide dissemination will build public trust. Community education campaigns that explain what AI can and cannot do for scientific writing — emphasizing the difference between language assistance and substantive intellectual contribution — will help readers evaluate claims and reduce misinformation spread.
Finally, no policy will succeed without attention to incentives. Thai institutions should align promotion, hiring and funding criteria to value verified, reproducible science over sheer publication quantity. Reward systems that emphasize transparent practices — including disclosure of AI use — will encourage adoption of responsible workflows. Senior scholars and research leaders should model disclosure and mentorship, reinforcing the cultural values of integrity and collective responsibility that underpin Thai academic life.
The international study’s headline finding — that one in five computer science abstracts showed signs of LLM modification by late 2024 — is both a wake‑up call and an opportunity. For Thailand, the moment calls for calibrated responses that preserve scientific rigor while embracing tools that can raise clarity and productivity. With clear policies, practical training, fair detection systems and a culture of transparent disclosure rooted in academic mentorship and public accountability, Thai research can harness generative AI’s benefits while guarding against its risks.