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.