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.
Rughiniș et al. (Tue,) studied this question.