Description:
Generative AI is quickly being adapted to research activities, but we are still in the early phases of learning its limits. Exploring the “light side” and the “dark side” of this force as a qualitative research tool can help scale data collection efforts. A non‑programmer adapted an open‑source, Python‑based AI chatbot and spent several months comparing OpenAI and Anthropic APIs to identify the most consistent option for interview tasks. When implemented in research, the chatbot demonstrated several strengths. In one instance, it autonomously recovered a lost connection during an interview, preserving data while limiting demands on the participant. In another, it continued an interview with a participant who responded exclusively in Korean even though it produced only English text. The system also revealed weaknesses, such as repeating questions despite coded safeguards intended to prevent participant fatigue. Join to uncover the opportunities and pitfalls of using AI to scale qualitative data collection.
About this event:
Presenters:
- Dr. Andrea Pellegrini, Assistant Director, University Bursar, University of Illinois System Student Money Management Center
Track:
Research: The Final Frontier — Unite teams to explore strange new data and boldly publish where none have published before.
Experience Needed:
All Levels
Learning Outcome:
Identify when AI “droids” support qualitative research and when they may drift toward the dark side.
Analyze how unexpected model behaviors can knock an interview off its intended trajectory.
Discuss ethical & methodological risks to ensure research stays aligned with the Light Side.
Apply lessons from the open‑source platform to design more reliable AI‑assisted workflows in qualitative research or chatbot applications.
Maximum Capacity:
No maximum capacity
Additional Keywords:
artificial intelligence, AI, qualitative interviews, non-programmer, open source chatbot
Where and When:
June 4, 2026 from 2:00 pm to 2:45 pm- Quad