Framing human-AI dynamics: An epistemological perspective on generative AI practices
TL;DR
It is argued that this understanding of human-GAI engagement, as explained through epistemological beliefs, lays the foundation for alternative approaches to teaching and assessment, student interactions, professional development, and AI governance and policy, while noting that the framework remains an exploratory heuristic requiring empirical validation.
Abstract
In this paper, I introduce the theorising of seven human-generative artificial intelligence (GAI) paradigms, grounded in personal epistemological beliefs. Following a conceptual, theory-building approach, I begin by outlining the construct of personal epistemological beliefs, differentiating among the source of knowledge, certainty of knowledge, organisation of knowledge, control of knowledge acquisition and speed of knowledge acquisition by drawing on Schommer (1994) and Schommer-Aikins and Easter (2008)’s multidimensional model. I contend that these epistemological dimensions offer an understanding of human-GAI engagement. I proceed by introducing the dynamic human-GAI paradigms (guarded, possibility-focused, augmented, pioneering, symbiotic, values-based and equity), outlining each and examining them against the five epistemological dimensions. Rather than fixed traits held by individuals, the paradigms are conceptualised as enacted patterns of engagement that emerge across different disciplinary, socio-technical and institutional contexts. I conclude by arguing that this understanding of human-GAI engagement, as explained through epistemological beliefs, lays the foundation for alternative approaches to teaching and assessment, student interactions, professional development, and AI governance and policy, while noting that the framework remains an exploratory heuristic requiring empirical validation.