AI evidence extraction

The impact of generative AI on the scholarly communications of early career researchers: An international, multi‐disciplinary study

David Nicholas, Marzena Świgoń, David Clark, Abdullah Abrizah, Jorge Revez, Eti Herman et al. · Learned Publishing · 2024

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

10/10

Relevance

Evidence

Citations

1.56

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1002/leap.1628

Methodology & findings

Study design

Scoping pilot study using in-depth qualitative interviews conducted across multiple countries to investigate the impact of generative AI on scholarly communications of early career researchers (ECRs).

Primary method

Qualitative analysis of in-depth interview data. No quantitative statistical methods are mentioned in the abstract.

Main result

The study found that "ECRs to be thinking, probing and, in some cases, experimenting with AI" and that "There was a general acceptance that AI will be responsible for the growth of low‐quality scientific papers, which could lead to a decline in the quality of research." Additionally, "The most widespread belief was AI would prove to be a transformative force and would exacerbate existing scholarly disparities and inequalities."

Reports effect sizes.

Research paradigm

Qualitative/Interpretivist

Author conclusions

The authors conclude that "Scholarly integrity and ethics were a big concern with issues of authenticity, plagiarism, copyright and poor citation practices raised." They also note that the research extends the Harbingers study trajectory to investigate "another potential agent of change: artificial intelligence," suggesting AI represents a significant factor in scholarly communications alongside previous generational and pandemic changes.

Risk of bias

Selection bias: Selective sampling strategy in UK/US suggests non-random participant recruitment; Self-selection bias: Participants who agreed to in-depth interviews may be more engaged or reflective on AI topics; Geographic bias: Data collected from limited countries (China, Malaysia, Poland, Portugal, Spain, and selectively UK/US); Sampling representativeness: No information provided on how ECRs were selected or whether sample is representative of broader ECR population; Potential sampling bias due to selective recruitment in UK/United States; Pilot study design may not be fully representative; Potential self-selection bias in qualitative interview recruitment; Generalizability concerns due to pilot study design

Open questions raised

The study aims to "fill the knowledge gap concerning ECRs whose millennial mindset may render them especially open to change" and extends investigation "to the arts and humanities," indicating gaps in understanding AI impact across disciplines and career stages.

Data: not_statedCode: not_statedExtracted from: pdf

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