Artificial intelligence and organizational learning in universities: decision premises, paradoxes, and institutional stability

Journal article

Julio Labraña, Emilio Rodríguez-Ponce
Journal of Organizational Change Management, 2026, Advanced online publication

DOI: 10.1108/JOCM-02-2025-0157

Cite

APA Click to copy
Labraña, J., & Rodríguez-Ponce, E. (2026). Artificial intelligence and organizational learning in universities: decision premises, paradoxes, and institutional stability. Journal of Organizational Change Management, Advanced online publication. https://doi.org/10.1108/JOCM-02-2025-0157

Chicago/Turabian Click to copy
Labraña, Julio, and Emilio Rodríguez-Ponce. “Artificial Intelligence and Organizational Learning in Universities: Decision Premises, Paradoxes, and Institutional Stability.” Journal of Organizational Change Management (2026): Advanced online publication.

MLA Click to copy
Labraña, Julio, and Emilio Rodríguez-Ponce. “Artificial Intelligence and Organizational Learning in Universities: Decision Premises, Paradoxes, and Institutional Stability.” Journal of Organizational Change Management, 2026, pp. Advanced online publication, doi:10.1108/JOCM-02-2025-0157.

BibTeX Click to copy

@article{julio2026a,
  title = {Artificial intelligence and organizational learning in universities: decision premises, paradoxes, and institutional stability},
  year = {2026},
  journal = {Journal of Organizational Change Management},
  pages = {Advanced online publication},
  doi = {10.1108/JOCM-02-2025-0157},
  author = {Labraña, Julio and Rodríguez-Ponce, Emilio}
}

Abstract

The purpose of this paper is to identify and explain the organizational conditions under which artificial intelligence adoption in universities leads to structural change rather than incremental adaptation. By integrating Luhmann’s theory of decision premises with Argyris and Schön’s concept of organizational learning loops, the study conceptualizes artificial intelligence (AI) adoption as a process mediated by institutional structures and mechanisms of invisibilization and proposes strategies to foster double-loop learning that enable universities to surface and address organizational paradoxes, thereby creating the conditions for meaningful transformation in teaching, research, and governance.