Evidence map›Paper›PMID 40755655›Full record

ReviewCureus2025

The Use of Generative Artificial Intelligence (AI) in Academic Research: A Review of the Consensus App.

Olukayode E Apata, Oi-Man Kwok, Yuan-Hsuan Lee

Abstract readReview
In one paragraph

Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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

3 authors.

Olukayode E ApataEducational Psychology, Texas A&M University, College Station, USA.
Oi-Man KwokEducational Psychology, Texas A&M University, College Station, USA.
Yuan-Hsuan LeeDepartment of Learning Technologies, University of North Texas, Denton, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Consensus App is an academic search engine designed to change how researchers access and synthesize information. It helps researchers quickly browse the growing body of academic literature by offering insights at both the topic and paper levels. We evaluate the Consensus App's potential to transform academic research, its ethical implications, and the reasons behind its underrepresentation in academic literature. We seek to provide a balanced perspective on the app's current and future influence in academic research. This paper is based on a rapid review of the literature to see how the Consensus App is used and reported in the literature. Our review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We focused on identifying applications, benefits, and ethical concerns related to the Consensus App. The search was conducted on December 23, 2024, across 210 academic databases. The databases from which articles were retrieved include Web of Science (N=6), MEDLINE (N=2), Academic Search Ultimate (N=1), and Fuente Académica Plus (N=1). In addition to the database searches, five additional editorials were identified through targeted manual searches of high-impact journals. In total, 10 papers were included in the final review. ChatGPT-4.5 was used to assist in synthesizing key themes across the articles, focusing on application, benefits, and ethical concerns related to the Consensus App and the broader use of artificial intelligence (AI) in scholarly work. The reviewed articles revealed that the use of the Consensus App is surprisingly low, which may suggest underreporting by its users. Researchers may also not be aware of it. These studies showed how the app has been limitedly used in the literature. Despite its advantages, we identified ethical concerns in the reviewed studies. Despite its potential, the Consensus App remains underutilized and significantly underreported in academic literature. Therefore, it is important for academic institutions, journal editors, and researchers to collaboratively develop standardized reporting guidelines when AI is involved in the process of manuscript development. The eventual goal is to lead to a more transparent reporting of AI usage in research.

Indexed as

academic integrityconsensus appethical concernsreporting practicestransparency

Identifiers

PMID40755655
PMCPMC12318603

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.