AI & the Research Cycle (Phase II: Data Collection & Analysis)
Scope
Artificial Intelligence is rapidly transforming every stage of the research cycle, from discovery to dissemination. This series will explore the evolving role of AI in shaping research workflows, addressing both the opportunities and challenges it presents. Join experts across various fields as they delve into how AI is enhancing research processes, improving efficiency, and raising new questions about ethics, transparency, and the future of knowledge creation. Join us as we build on our AI & the Research Cycle series with Phase II.
Confirmed speaker: Anna Aston, Section Manager, Imperial College of London and Andrea Jacobs, Director of Analytics, CAS, and Lynda Kellam, Snyder-Granader Director of Research Data & Digital Scholarship at Penn Libraries.
Qiana Johnson, Associate Dean of Libraries, Collections and Content, Dartmouth College will serve as the moderator for this program.
Event Sessions
Speakers
Qiana Johnson, Associate Dean of Libraries, Collections and Content, Dartmouth College, served as the coordinator and moderator for this program.
The following questions were posed to our speakers:
What does “AI in research” actually mean today, and how does it go beyond just using tools like ChatGPT?
How are AI tools currently being used for data collection and analysis in both quantitative and qualitative research workflows?
What ethical concerns do researchers and institutions need to consider when using AI—especially around privacy, consent, and data misuse?
How can researchers determine when to use off-the-shelf AI tools versus building custom or bespoke solutions for their projects?
What does effective AI literacy look like in research environments, and how can institutions support scalable, equitable training?
How should researchers evaluate the accuracy, transparency, and potential hallucinations in AI-generated outputs?
Should researchers disclose their use of AI in publications or grant applications, and what risks or biases might they face in doing so?
How is AI reshaping information-seeking behavior, and what effect might AI-assisted search engines have on scholarly citations and visibility?
How can institutions ensure equitable access to AI tools and avoid deepening divides between well- and under-resourced researchers or departments?
What role should interdisciplinary communities and peer collaboration play in shaping responsible, effective AI use in research?
Related Information and Shared Resources:
Digital Scholarship and Data Services - We provide instruction, offer consultations, and develop shared resources to facilitate research and prepare students to be educated citizens of the world. Collaborating with campus and community partners, we advocate for ethical scholarly practice throughout the data and digital scholarship lifecycles.
The RDDS Blog - From Penn Libraries - Search all work by title, tag, or author
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Event Dates
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Location
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