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AI evidence extraction

Towards a Semi-Automated Approach for Systematic Literature Reviews

Tim Denzler, Martin Enders, Patricia Akello · Journal of the Association for Information Systems · 2021

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

9/10
Relevance
0/4
Quality (LMQS)
D
Evidence
4
Citations
2.02
FWCI
Top 10%
Impact

Methodology & findings

Study design

Design Science Research approach including continuous evaluation

Primary method

Design Science Research

Main result

The authors present an artifact designed to support systematic literature review processes holistically. They state: "we developed a flexible and modifiable artifact that aims to support systematic literature review processes from a holistic point of view" and anticipate that "our artifact to be a first step towards semi-automation of systematic literature reviews, which will gain relevance in the near future, as the trend of rising scientific literature output is expected to continue."

Research paradigm

Design Science Research

Author conclusions

The authors conclude that "we developed a flexible and modifiable artifact that aims to support systematic literature review processes from a holistic point of view" and that "Our development process follows a Design Science Research approach including continuous evaluation," positioning this work as foundational for semi-automated systematic literature reviews.

Open questions raised

  • The authors identify that existing solutions for systematic literature reviews "are often restrained to a single aspect of the process or lack interoperability" and that "researchers may not be able to efficiently leverage recent promising advancements in Machine Learning and Text Analytics."
  • The authors identify that existing solutions for systematic literature reviews are "often restrained to a single aspect of the process or lack interoperability" and that "researchers may not be able to efficiently leverage recent promising advancements in Machine Learning and Text Analytics."
  • The authors identify that existing solutions to support systematic literature reviews are "often restrained to a single aspect of the process or lack interoperability" and that researchers need better leverage of "recent promising advancements in Machine Learning and Text Analytics."
Data: not_statedCode: not_statedExtracted from: pdfAgreement 86%

Explore related topics

Systematic Review AutomationLiterature DiscoveryDesign Science

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