Event Recap: AI Chatbots as Research Assistants

From June 15–17, the Consortium for Translational and Precision Health’s Training and Workforce Development Program, in collaboration with Baylor College of Medicine’s Master Teacher Fellowship Program, hosted a three-day workshop, “AI Chatbots as Research Assistants,” at Baylor College of Medicine. The event was held both in person and virtually, with about 300 attendees.

The workshop was facilitated by Blaz Zupan, Ph.D., professor of artificial intelligence and machine learning at the University of Ljubljana in Slovenia and a visiting faculty member in the Huffington Department of Education, Innovation & Technology. Zupan led a lecture series titled “Three Short Lectures on Chatbots, Models and Artificial Intelligence.”

Throughout the three-day event, participants focused on using AI, delegating tasks to AI, and building a deeper understanding of how AI functions. Zupan emphasized the importance of structure when working with AI tools, encouraging researchers to be intentional in how they design prompts.

A key function of the Training and Workforce Development Program is to build researchers’ skills and help them effectively use specialized tools.

This effort is particularly important for researchers in translational science and precision health, where integrating AI into their work is becoming increasingly essential.

“Uses of AI, particularly generative AI technologies, are ubiquitous in research, clinical settings and industry,” said Nancy Moreno, Ph.D., co-lead of the Consortium for Translational and Precision Health’s Training and Workforce Development Program. “It is important for current and future members of translational science teams to stay up to date on AI tools for a wide range of tasks, both to enhance their own productivity and to better understand the work of others.”

While researchers must consider ethical implications, most institutions provide guidelines for the responsible use of AI that can serve as a starting point. Moreno emphasized that researchers should “be aware of risks related to data privacy, protection of intellectual property, bias or inaccuracy in generated information, and inadvertent plagiarism.”

She added that “most academic institutions now require the use of internal versions of generative AI tools so that data and sensitive information are not shared broadly. Many funders and publishers also prohibit or restrict the use of AI to write or review submissions.”

The workshop reflected growing efforts to equip researchers with practical AI skills while promoting responsible and effective use of emerging technologies.