Theorising notions of searching, (re)sources and evaluation in the light of generative AI

Abstract

Introduction. The introduction of publicly available large language models (LLMs) since 2022 has significantly challenged traditional library and information studies (LIS) core concepts. This paper argues that LIS needs to rethink aspects of its conceptual framework to address the challenges posed by the proliferation of AI-generated content and how this content is produced. Analysis. The study employs a theoretical analysis to critically examine the LIS concepts of search, sources and evaluation in the light of an increasingly AI-infused information infrastructure. Results. The main argument of the paper is that due to changes in the information infrastructure, sources are becoming increasingly invisible for people when they look for information. This has profound implications for how searching for and evaluation of information can be conceptualised. Conclusion. The paper is concluded by offering conceptual insights on how to theoretically navigate the rapidly evolving information landscape.