Cited by 218Ethics and Social Impacts of AIResearch, Science, and AcademiaArtificial Intelligence Applications

Speeding up to keep up: exploring the use of AI in the research process

Open access
Chubb, Jennifer, Cowling, Peter, Reed, Darren·2022·AI & SOCIETY

Abstract

There is a long history of the science of intelligent machines and its potential to provide scientific insights have been debated since the dawn of AI. In particular, there is renewed interest in the role of AI in research and research policy as an enabler of new methods, processes, management and evaluation which is still relatively under-explored. This empirical paper explores interviews with leading scholars on the potential impact of AI on research practice and culture through deductive, thematic analysis to show the issues affecting academics and universities today. Our interviewees identify positive and negative consequences for research and researchers with respect to collective and individual use . AI is perceived as helpful with respect to information gathering and other narrow tasks, and in support of impact and interdisciplinarity. However, using AI as a way of ‘speeding up—to keep up’ with bureaucratic and metricised processes, may proliferate negative aspects of academic culture in that the expansion of AI in research should assist and not replace human creativity. Research into the future role of AI in the research process needs to go further to address these challenges, and ask fundamental questions about how AI might assist in providing new tools able to question the values and principles driving institutions and research processes. We argue that to do this an explicit movement of meta-research on the role of AI in research should consider the effects for research and researcher creativity. Anticipatory approaches and engagement of diverse and critical voices at policy level and across disciplines should also be considered.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s00146-021-01259-0

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • There is limited empirical research on the effects of large language models (LLMs) and LLM-based chatbots on science and scientific practice.

    “While the impact on education has been a primary focus, there is limited empirical research on the effects of large language models (LLMs) and LLM-based chatbots on science and scientific practice.”
  • To date, scholars have primarily focused on the implications of LLMs on education, with limited attention to their impact on science and scientific practices.

    “To date, scholars have primarily focused on the implications of LLMs on education (e.g., Perkins 2023; Fyfe 2023) with limited attention being paid to their impact on science and scientific practices (for exceptions, see Chubb et al. 2022;Ribeiro et al. 2023).”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 101 · Cited by 218

Research works printed but not linked: 5

  1. AJE (2018) Peer review: how we found 15 million hours of lost time. https://www.aje.com/arc/peer-review-process-15-million-hours-lost-time/. Accessed 18 Aug 2021
  2. Bourdieu P Homo academicus 1988 Stanford University Press
  3. Cave S Dihal K Dillon S AI narratives: a history of imaginative thinking about intelligent machines 2020 Oxford University Press
  4. Hicks et al. (2015) https://www.nature.com/news/bibliometrics-the-leiden-manifesto-for-research-metrics-1.17351. Accessed 12 Mar 2021
  5. Van Belkon R (2020b) AI no longer has a plug: about ethics in the design process. https://www.researchgate.net/publication/343106745_AI_no_longer_has_a_plug_about_ethics_in_the_design_process. Accessed 16 Nov 2020

Authors
Chubb, J., Cowling, P., & Reed, D.
Affiliations
  • University of York (GB)
  • Queen Mary University of London (GB)
Corresponding author
Chubb, Jennifer
Venue
AI & SOCIETY
Published
2021-10-15
Volume
37
Issue
4
Pages
1439-1457
ISSN
  • 0951-5666 (ISSN-L)
  • 1435-5655
OpenAlex
W3206896625
Cited by
218
References
101
Citations by year
2026: 54 · 2025: 72 · 2024: 61 · 2023: 24 · 2022: 7
FWCI
28.76
Type
journal article
Language
en
Open access
Locations
  • AI & Society (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion, other-oa)
  • Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
Funders
  • University of Sheffield
  • Engineering and Physical Sciences Research Council (EP/M023265/1)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Decision Sciences › Information Systems and Management › Research, Science, and Academia
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
artificial intelligence in research, research practices, academic culture, researcher creativity, research evaluation, research metrics, bureaucratic procedures, meta-research, science policy, information gathering, interdisciplinarity, research impact

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review