Artificial Intelligence (AI) for Graduate Students and Researchers Guide Ask Us

This guide is intended to support researchers, including graduate students, staff and faculty at UNB make informed decisions about the use of GenAI throughout the research process. Please keep in mind that this is a constantly evolving area, and while this guide is updated regularly, please still double check publisher or funder websites for the most up to date information.

For basic AI definitions, see the Artificial Intelligence (AI) for Undergraduate Students Guide.

Research Integrity

Using AI uncritically, or without acknowledgement can result in problems with research integrity. This is important to consider both as a researcher, and a consumer of research.

As a Researcher

You are responsible to ensure any research you publish is accurate, up to date, and properly acknowledges sources, and tools used. To ensure that you're adhering to best practices, review the tabs on AI in Grant Preparation & Evaluation and Manuscript Preparation & Peer Review.

As a Consumer of Research

The use of AI does not necessarily indicate that research is low-quality or problematic. However, there are a growing number of papers that are entirely AI generated, or have not been significantly evaluated or vetted by human experts. Therefore, it is more essential than ever to be critical of the articles you are reading. If you have concerns about the validity of a research output, use the below resources to evaluate the output.

As a consumer of research, you can also contribute to the above resources by posting concerning articles or contributing your disciplinary expertise to conversations on the validity of research.

Further Reading

• Scientific sluths spot dishonest ChatGPT use in papers. 
• The latest "crisis": Is the research literature overrun with ChatGPT - and LLM-generated articles
• ChatGPT listed as author on research papers: many scientists disapprove.
• 'Stamp out paper mills' - science sleuths on how to fight fake research
• Nonhuman “Authors” and Implications for the Integrity of Scientific Publication and Medical Knowledge

AI can support you in finding relevant sources to include in literature reviews, or inform your methodological approach. Once you have some initial sources, you can also use AI tools to 'map' the papers, and find additional relevant papers.

Literature Searching Tools

UNB Libraries provides access to a number of AI driven research tools. As opposed to more generalized AI tools like ChatGPT or Microsoft Copilot, these are tools specifically tailored to academic research. The below descriptions and videos outline how to access and use these tools. 

Scopus AI

See the Elsevier Guide to Scopus AI for further details.

Web of Science Research Assistant

JSTOR Research Assistant 

Consensus AI

Non-UNB Subscription Tools

It's important to keep in mind that there are important differences between traditional library databases with AI tools, and AI tools aimed at the general public. Look out for:

• Where the tool is searching. If you're looking for academic literature, it's preferable to use tools that pull from academic sources, rather than the internet more broadly.
• Transparency. One of the key issues with AI is bias in training data, so look for tools that are transparent about where they pull results from, and how they are ranked.
• Diversity. Each tool will search a different collection of resources, or rank these results differently, and there is no single best way to do this. Therefore, combine the outputs you get from multiple tools/databases for the most comprehensive picture.

Research Mapping Tools

Going beyond searching, you can take an initial collection of articles, and utilize AI suggest additional resources that are related to what you've already collected. This can be based on citation relationships, or content overlap.

If you are planning to use GenAI for any part of your data cleaning, processing or interpreting, keep in mind that oftentimes GenAI can be a black box. Essentially, inputs enter a closed system/application in which a process that we can't fully see, describe, or sometimes replicate occurs inside the black box, which results in an output. In research, this could be problematic, as not having an in-depth understanding of the methods used, and calculations undertaken would detract from the transparency and replicability of your work. Opt for applications and models that are transparent in their functions, and always double check your target journal or publication ahead of adopting the technology. 

Existing data analysis tools are introducing AI agents or assistants to support analysis.

As of November 18, 2024, the Government of Canada has released a new guidance document on the use of Artificial Intelligence in the development and review of research grant proposals. The key aspects of the documents are:

• GenAI can be used in the preparation of grant applications if authors
  • Ensure information is true, accurate and complete
  • Properly acknowledge/reference information*
    • Remember that some GenAI applications do not provide sources for their information, making proper attribution difficult
• Publicly available GenAI cannot be used for evaluating grant applications
  • These tools could result in breaches of privacy and the loss of custody of intellectual property
    • Entering a piece of work that you do not hold the intellectual property rights to into GenAI applications (asking it to summarize an academic article) may be a breech of the owner's IP

CIHR has banned the use of AI tools, including those that transcribe/summarize spoken words, in meetings focused on peer-reviewing funding applications. The rationale for this decision is based on the fact that some AI tools do store their transcripts and may use these to train their models, which does not adhere to CIHR guidelines on handling peer-review documents.

Manuscript Preparation

Before utilizing GenAI to contribute writing to a manuscript draft, ensure you are familiar with the GenAI policies of your target journal/publisher. 

In most cases, ChatGPT and other LLMs should not be listed as authors on a manuscript. Although there is no universal list of requirements for authorship, and different publishers may have varying regulations, authors generally:

• Contribute to the design, analysis or interpretations of the article
• Draft/review the article
• Give final approval for an article to be published
• Agree to be held accountable for the article (see ICMJE Authorship Definitions, or your target journal/publisher policies for more information)

These requirements are not all typically met by GenAI, especially because models like ChatGPT cannot be held accountable in the same way a person can. Human input or manipulation is required to generate the types of outputs that would be included in a manuscript, meaning that while ChatGPT can be used as a tool, it has not made a significant enough contribution to be considered an author. 

Please review the individual policies of the journal/publisher you are interested in publishing with. Some major publishers are listed below, and for assistance in finding/navigating publisher policies, contact the Library.

• Springer-Nature
• Elsevier
• Taylor & Francis
• Wiley
• Sage

Peer Review

While AI can assist reviewers in identifying issues like formatting or language (which are typically addressed in the copy editing stage anyways), it cannot replace or replicate the expert assessment conducted by researchers with in-depth knowledge of their field, including patterns and methodologies. 

Additionally, inputting another person's work into a Generative AI tool can be a breach of their intellectual property, as many models will then use this content for training purposes. Therefore, many publishers explicitly ban the use of GenAI tools in the peer review process, or putting in any section of a manuscript into a GenAI application.