Research
Research focus
I study how AI is changing the ways research communities learn, share practices, make judgments, and maintain scientific integrity. I approach these communities as sociotechnical systems in which people, institutions, technologies, networks, and shared resources jointly shape knowledge.
My developing PhD research examines how researchers encounter, evaluate, adapt, reject, and transmit AI-mediated practices. I am interested in how these processes relate to collective intelligence, scientific integrity, and human judgment.
I understand collective intelligence as a community’s capacity to combine distributed expertise, preserve productive disagreement, detect and correct error, and improve its practices over time. This perspective shifts attention from individual AI use alone to the larger relationships and conditions through which research practices become visible, credible, questioned, revised, or shared.
Questions guiding my work
Several broad questions orient this research:
- How do AI-mediated practices move through research communities?
- How do researchers decide when to trust, verify, revise, question, or decline AI assistance?
- How do social networks and institutional settings shape collective learning and scientific self-correction?
Methods and computational development
My established methodological experience includes data analytics, social network analysis, exponential random graph models, survey research, interviews, mixed methods, and reproducible quantitative workflows in R.
During my doctoral training, I am extending this foundation through cultural-evolutionary approaches, computational modeling, and agent-based modeling. These are developing directions that I am learning to use to examine how individual interactions and decisions may contribute to larger patterns in human-AI systems over time.
Master’s thesis foundation
My master’s thesis used thematic analysis and social network analysis to examine interdisciplinary collaboration within Boise State University’s Grand Challenges initiative. The project examined collaboration structure, disciplinary clustering, and the roles that connect otherwise separated parts of a research network. It established my interest in knowledge networks and in the conditions that support or limit collective research.
My current direction also grows from practical interdisciplinary work documented on my Human-Centered AI Collaborations and RISE Fellowship pages.
Work in development
I am currently refining the empirical scope and computational approaches for this research program. I will add project-specific methods, public artifacts, and findings as the work reaches appropriate public milestones.