  Research within sociotechnical domains, such as Software Engineering,
fundamentally requires a thorough consideration of the human perspective.
However, traditional qualitative data collection methods suffer from challenges
related to scale, labor intensity, and the increasing difficulty of participant
recruitment. This vision paper proposes a novel approach to qualitative data
collection in software engineering research by harnessing the capabilities of
artificial intelligence (AI), especially large language models (LLMs) like
ChatGPT. We explore the potential of AI-generated synthetic text as an
alternative source of qualitative data, by discussing how LLMs can replicate
human responses and behaviors in research settings. We examine the application
of AI in automating data collection across various methodologies, including
persona-based prompting for interviews, multi-persona dialogue for focus
groups, and mega-persona responses for surveys. Additionally, we discuss the
prospective development of new foundation models aimed at emulating human
behavior in observational studies and user evaluations. By simulating human
interaction and feedback, these AI models could offer scalable and efficient
means of data generation, while providing insights into human attitudes,
experiences, and performance. We discuss several open problems and research
opportunities to implement this vision and conclude that while AI could augment
aspects of data gathering in software engineering research, it cannot replace
the nuanced, empathetic understanding inherent in human subjects in some cases,
and an integrated approach where both AI and human-generated data coexist will
likely yield the most effective outcomes.
