SIPS 2025 Online

Researchers are commonly advised to expect a project to take longer than originally planned. By the same token, it is frequently impossible to reproduce a study without communicating with the original authors. We argue that both problems originate with a lack of understanding of the labor involved in completing a project. By labor we mean everything that needs to be done to address a research question. However, this is a theoretical postulation. In this hackathon we will attempt to map out all of the labor involved in completing a research project. We will use the Heliocentric Model of Open Science Documentation to identify the components of the project, and to structure the labor required to complete them. We will also use narrative structure and problem solving schemas to describe the labor/outputs for comprehension by 3rd parties. Future directions will be determined by the group at the hackathon.

Integrating artificial intelligence (AI) in qualitative research offers academic scholars a variety of opportunities and challenges. AI can streamline qualitative analysis by using natural language processing and machine learning. However, ethical considerations such as AI bias, privacy, (lack of) theoretical underpinnings, and preservation of human-centered analysis demand critical examination.
This hackathon invites researchers from all stages to collaboratively explore the role of AI in advancing qualitative methodologies. We aim to produce a paper that identifies AI’s capabilities and limitations within qualitative research, emphasizing the impact on methodological rigor and data integrity. Through this collaboration, we will identify the implications of and propose strategies for ethical AI use in qualitative research. We will discuss usage of open science practices to investigate AI in qualitative research. By fostering interdisciplinary dialogue and critical collaboration, this hackathon seeks to shape the future of ethical and rigorous AI integration in qualitative research methodologies.