AI at the knowledge gates: institutional policies and hybrid configurations in universities and publishers

Abstract

Introduction: This study examines how academic institutions conceptualize and regulate artificial intelligence in knowledge production, focusing on institutional strategies for managing technological disruption while preserving academic values. Methods: Using boundary work theory and actor-network approaches, we conducted qualitative content analysis of AI policies from 16 prestigious universities and 12 major publishers. We introduced analytical concepts of dual black-boxing and legitimacy-dependent hybrid actors to explore institutional responses to AI integration. Results: Institutions primarily address AI’s opacity through transparency requirements, focusing on usage pattern visibility. Boundary-making strategies include categorical distinctions, authority allocation, and process-oriented boundaries that allow AI contributions while restricting final product generation. Universities demonstrated a more flexible recognition of hybrid actors compared to publishers’ stricter authorship boundaries. Discussion: The study discusses how established knowledge institutions navigate technological change by adapting existing academic practices. Institutions maintain human authority through delegated accountability, showing a diversified approach to integrating AI while preserving core academic integrity principles.