To the EVMS research community

You might already be informed about the ongoing conversations surrounding the utilization of generative AI tools, including ChatGPT, and its implications on academic and research activities.

These guidelines are pertinent to everyone in the research domain, including faculty, staff, students across all levels, guest researchers like unpaid volunteers, interns, visiting academics, as well as collaborators and consultants undertaking research at EVMS. Our aim with these guidelines is to set the foundation for the principled and judicious use of AI in academic research.

It's essential that you familiarize yourself with these guidelines and weave them into your academic and research activities. Adapt them where necessary to align with the conventions and practices of your scholarly discipline. We also urge mentors and advisors to regularly discuss with their students and other research trainees the role of generative AI in their research endeavors.

Keeping in mind the accelerated pace of development of new AI tools, we expect these guidelines to undergo periodic revisions.

Implementing guidelines

  1. Understanding AI operations:
    1. Be aware that the workings behind AI-generated outputs might be opaque to the user.
    2. Consequently, the content produced may not be easily validated against primary sources.
  2. Recognize biases and limitations:
    1. Understand that AI-generated content reflects the biases of the data it was trained on.
    2. Researchers should critically assess and acknowledge these biases. The output might sometimes be incorrect or wholly fabricated, even when it seems trustworthy.
  3. Privacy and confidentiality:
    1. Treat inputting private or confidential data into public AI tools as public disclosure.
    2. Understand that uploading information to these tools, including querying tools like ChatGPT, means releasing that data to a third party.
    3. Do not assume that generative AI tools adhere to privacy regulations like HIPAA and FERPA.
    4. Always exercise caution, as disclosing sensitive information can lead to privacy and security breaches including data breaches and exposing intellectual property.
    5. Generative AI might generate content that infringes on intellectual property or copyrighted material. Using such content might lead to accusations of plagiarism or misconduct against the researcher.
  4. Adherence to evolving norms:
    1. Recognize that standards for using generative AI continually change, varying by application, context, and discipline.
    2. Ensure that AI use aligns with the standards and policies of your research context and discipline.
    3. Be aware of the positions and guidelines from journals, publishers, and professional organizations regarding generative AI use.
  5. Researcher accountability:
    1. Stay updated on policies governing generative AI use in your research.
    2. You're accountable for the work you create and share, including ensuring accuracy, proper attribution, and disclosure of AI involvement.
    3. This responsibility extends to everyone in the research community, from faculty to research trainees.
  6. Transparent disclosure and documentation:
    1. Clearly indicate and record any use of generative AI in research activities.
    2. Note that documentation requirements can differ by context and discipline, but when in doubt, choose transparency.
  7. Open communication with teams:
    1. Supervisors and senior researchers should foster open discussions with their teams about the potentials and pitfalls of using generative AI in research.
  8. Continuous learning:
    1. Given the rapid advancements in AI technology and changing standards, keep yourself informed.
    2. Engage in professional development opportunities to strengthen your knowledge and skills related to AI integration in research.