Thinking the unthinkable: Generative AI for collaborative, intra-active policy analysis

In this paper, we report an exploratory collaboration in data analysis between a human researcher and Generative AI (GenAI). Specifically, we reveal how human-AI collaboration might contribute to alternative interpretations in theory-informed policy analysis. We draw on Karen Barad’s philosophy of agential realism as an anchor point to consider the ethico-onto-epistem-ological affordances and limitations of incorporating GenAI in social sciences research to guide the human-AI collaborative analysis. The exploratory collaboration reported is based on a case involving how a human researcher (the first author) engaged with Copilot, drawing upon a relevant analytical method, to analyse a Chinese education policy document. Through this example, we illustrate how a more intra-active human-AI collaboration could enable the human researcher to think the unthinkable , that is to think beyond the discursive or conceptual boundaries of his own understanding of the policy logics through the analytical framework. We argue that we can benefit from the analytical conversational collaboration process between AI and human intelligence, rather than simply the AI-generated output per se. This study is significant as it demonstrates a much more ethical, interdependent, and indeed intimate form of human-AI collaboration, in which the agency between humans and GenAI is recognised and respected. This more intimate form of human engagement with GenAI might also yield new ways of knowing and being as researchers in conducting qualitative policy analysis.