AI evidence extraction

Democratizing Knowledge Creation Through Human-AI Collaboration in Academic Peer Review

Suprateek Sarker, Anjana Susarla, Ram D. Gopal, Jason Bennett Thatcher · Journal of the Association for Information Systems · 2024

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

9/10

Relevance

Evidence

Citations

5.78

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.17705/1jais.00872

Methodology & findings

Study design

Opinion piece / conceptual analysis - This is not an empirical study but rather a position paper offering preliminary ideas on human-AI collaboration in academic peer review.

Primary method

None - this is a conceptual/opinion piece without empirical statistical analysis

Main result

The authors argue that "the growing collaboration between humans and AI will disrupt how academics assess scholarly manuscripts and disseminate published works in a way that facilitates the closing of gaps among diverse scholars as well as competing scholarly traditions." The piece presents preliminary ideas on how human-AI collaboration will change peer review processes and underscores "the potential for democratizing academic culture worldwide."

Reports effect sizes.

Research paradigm

Interpretivist/argumentative

Author conclusions

The authors conclude that "Such human-AI collaboration is not a distant reality but is unfolding before us, in part, through the development, application, and actual use of AI, including language learning models (LLMs)." They advocate that this collaboration offers "the potential for democratizing academic culture worldwide" while highlighting both benefits and possible bottlenecks in the peer review process.

Risk of bias

Opinion-based rather than evidence-based - no systematic empirical validation; Speculative framing about future impacts without empirical grounding; No discussion of potential negative consequences or risks from AI in peer review; Potential optimism bias regarding democratization claims

Limitations

The authors acknowledge this is an "opinion piece" that "offers preliminary ideas" rather than a comprehensive empirical analysis
No formal limitations section is provided in the abstract
The work appears to lack empirical validation or systematic evidence synthesis of the proposed claims.

Open questions raised

The paper identifies the need for further exploration of how human-AI collaboration will change peer review processes, the benefits and bottlenecks of such collaboration, and the mechanisms through which it can democratize academic culture.

Data: not_statedCode: not_statedExtracted from: pdf

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