AI tools are becoming valuable assistants in the research process, particularly during the literature review and manuscript preparation stages. These tools can:
Extract key insights from large volumes of scientific literature.
Highlight inconsistencies in arguments or conclusions.
Suggest more reliable or original sources over secondary ones.
Provide real-time alerts about retractions or problematic citations (e.g., through Chrome plugins or databases).
While AI is not a replacement for human judgment, it can enhance the accuracy and efficiency of literature screening and support a more rigorous review process.
One promising area for AI in scientific review is automated statistical analysis checking. Certain tools can:
Scan a paper and identify which statistical tests were used.
Determine whether those tests were appropriate for the data and study design.
Flag potential misinterpretations of results (e.g., p-hacking, misuse of t-tests, or misleading correlations).
This kind of review is especially useful for readers without advanced statistical training, allowing them to assess methodological validity with more confidence.
Example tool: Statcheck automatically detects statistical reporting errors in psychology papers and could inspire similar tools in other fields.
AI has a complicated relationship with misinformation in science:
On the one hand, generative AI tools can inadvertently create or amplify inaccuracies if they are trained on flawed or biased data.
On the other hand, AI-powered fact-checking and citation analysis tools can help detect and reduce the spread of misinformation by alerting users to:
Retracted studies
Incorrect claims based on faulty logic or evidence
Misused sources or citations
As misinformation spreads quickly—especially through secondary or review articles—AI can assist in identifying and avoiding unreliable research.
To learn more about using AI for research and avoiding misinformation, visit this LibGuide.
PubPeer is a powerful post-publication peer review platform, but it’s just one part of a broader research integrity toolkit. Similarly, AI tools are helpful, but they are not infallible.
Best practice is to:
Use PubPeer to identify community concerns or discussions around a paper.
Use AI tools to surface potential issues, but verify them through manual reading and context.
Remember that a flagged paper isn't automatically invalid—but it does deserve a closer look.
Encouraging critical engagement—rather than over-reliance on any one tool—leads to better scholarship.
