The CARE approach for academic librarians: From search first to answer first with generative AI

Students and faculty are increasingly beginning their research by asking AI systems for explanations rather than by searching library resources. Chatbots and AI enhanced search tools now deliver fluent answers before users ever see a list of sources. This commentary argues that this “answer first” environment changes the starting point of academic inquiry and calls for a corresponding shift in academic librarianship. Librarians need an answer first mindset that recognizes AI responses as texts that require interpretation. I propose two related constructs to support that stance, a brief “answer typography” that helps librarians notice what kind of work AI answers are doing, and the CARE approach (Classify, Assess, Review, Enhance), which articulates a critical way of engaging those answers with users. Together, these ideas position librarians as leaders in helping their communities read, question, and build upon AI generated answers in ways that keep human judgment and scholarly evidence at the center of inquiry.

https://doi.org/10.1016/j.acalib.2025.103186