Evidence map›Paper›PMID 41880293›Full record

ArticlePloS one2026

Man vs. machine: Multi-country experimental evidence on the quality and perceptions of AI-generated research blog content.

Michael Keenan, Naureen Karachiwalla, Jawoo Koo, Christine Mwangi, Clemens Breisinger, MinAh Kim

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Michael KeenanInternational Food Policy Research Institute, Washington, District of Columbia, United States of America.ORCID https://orcid.org/0000-0001-5191-9146
Naureen KarachiwallaInternational Food Policy Research Institute, Washington, District of Columbia, United States of America.
Jawoo KooInternational Food Policy Research Institute, Washington, District of Columbia, United States of America.ORCID https://orcid.org/0000-0003-3424-9229
Christine MwangiInternational Food Policy Research Institute, Washington, District of Columbia, United States of America.
Clemens BreisingerCurrent affiliation: Consultative Group on International Agricultural Research, Systems Organization, Nairobi, Kenya.
MinAh KimCurrent affiliation: Food and Agricultural Organization, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Academic research is not always available in a form that is accessible or engaging to a non-academic audience, hindering readers' engagement with it. Non-academics, even if highly educated and policy experts in their fields, tend to need research to be presented in a more accessible way than peer-reviewed articles - one example being non-technical blogs. However, writing these requires some effort from researchers. Artificial Intelligence (AI) tools can make academic research easier to understand by summarizing and simplifying academic papers much more quickly than researchers can, making it easier for researchers to produce such summaries. However, disclosure of AI use may lower readers' perceived quality of and trust in the blog, generating a trade-off for the researcher. In this paper, we evaluate an 11-country experiment cross-randomizing a blog's actual and reported author as AI or human. We find that research stakeholders rate the quality of AI-generated blogs marginally lower than human-written ones (p [Formula: see text] 0.1), but disclosure of AI use offsets the negative effect (p [Formula: see text] 0.1). The study sample consists of policy-relevant stakeholders who typically engage with academic research; they are highly educated and include thematic specialists. Indeed, findings indicate that this audience interprets "accessibility" differently, preferring slightly more technical summaries of research. The nature of the respondents may thus explain the particular findings in this study, suggesting that researchers should tailor their prompts for their intended audience. There are no effects on readers' reported likelihood of engaging with the blog or on beliefs about others predicted engagement with it. Consequently, we hypothesize that researchers can leverage AI to communicate their research more easily without a penalty from disclosing its use.

Indexed as

Artificial IntelligenceBloggingAcademiaHumansResearch Personnel

Identifiers

PMID41880293
PMCPMC13016282

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.