Algorithmic Governance and Strategic Value of Open Large Language Models in University Administrative Decision-Making: A Systematic Review (2019–2025)

Iván Miguel García López; Jessica Nájera Ochoa

doi:10.12804/revistas.urosario.edu.co/empresa/a.15889

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Gobernanza algorítmica y valor estratégico de los modelos de lenguaje abierto a gran escala en la toma de decisiones administrativas universitarias
una revisión sistemática (2019-2025)

DOI: https://doi.org/10.12804/revistas.urosario.edu.co/empresa/a.15889

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Publicado: jul 27, 2026

Edição: v. 28 n. 51esp (2026): Experiencias latinoamericanas en gestión de la innovación y vínculos entre universidad y empresa

Seção Artículo de investigación científica y tecnológica

Palavras-chave: Inteligência Artificial Generativa Modelos de Linguagem Aberta em Grande Escala governança algorítmica confiança organizacional competência digital ensino superior

Autores

Iván Miguel García López
Universidad La Salle

Jessica Nájera Ochoa
Universidad La Salle

Resumo

Objetivo: este estudo revisa sistematicamente a literatura recente (2019–2025) para identificar padrões, benefícios, desafios e fatores de confiança associados à adoção de Open Large Language Models (OLLMS) na tomada de decisões administrativas universitárias. Metodologia: utilizando uma abordagem de Revisão Sistemática de Literatura (SLR) baseada em prisma, foram analisados 183 artigos revisados por pares indexados no Scopus e Web of Science. Resultados principais: indicam que os ollms são usados principalmente para aumentar a eficiência operacional e automatizar processos administrativos, enquanto as aplicações estratégicas e de governança permanecem subdesenvolvidas. As principais barreiras incluem a lacuna de competência digital e a falta de estruturas regulatórias, que restringem a confiança organizacional na inteligência artificial. O estudo articula três construtos explicativos — governança algorítmica, competência digital e valor estratégico — para interpretar a maturidade institucional da adoção de ollms. Conclusiones: e a integração efetiva dessas tecnologias requer políticas de transparência, treinamento de liderança e mecanismos de auditoria algorítmica para fortalecer a legitimidade e a qualidade da tomada de decisão em instituições de ensino superior.

Como Citar

García López, I. M., & Nájera Ochoa, J. (2026). Gobernanza algorítmica y valor estratégico de los modelos de lenguaje abierto a gran escala en la toma de decisiones administrativas universitarias: una revisión sistemática (2019-2025). Revista Universidad Y Empresa, 28(51esp), 1–27. https://doi.org/10.12804/revistas.urosario.edu.co/empresa/a.15889

Referências

Acosta-Enriquez, B. G., Ballesteros, M. A. A., De Los Angeles Guzman Valle, M., Angaspilco, J. E. M., Blanco-García, L. E., Ventura, G. C., Requejo, J. D. C., & Torre, M. C. (2025). Determinants of ai use in university teachers: The role of leadership, teaching concerns, and constructivist pedagogical beliefs. Human Behavior and Emerging Technologies, 2025(1), 4834893. https://doi.org/10.1155/hbe2/4834893 DOI: https://doi.org/10.1155/hbe2/4834893

Afroogh, S., Akbari, A., Malone, E., Kargar, M., & Alambeigi, H. (2024). Trust in ai: Progress, challenges, and future directions (Version 3). arXiv. https://doi.org/10.1057/s41599-024- 04044-8 DOI: https://doi.org/10.1057/s41599-024-04044-8

Antonopoulou, K., Begkos, C., & Zhu, Z. (2023). Staying afloat amidst extreme uncertainty: A case study of digital transformation in higher education. Technological Forecasting and Social Change, 192, 122603. https://doi.org/10.1016/j.techfore.2023.122603 DOI: https://doi.org/10.1016/j.techfore.2023.122603

Bach, T. A., Khan, A., Hallock, H., Beltrão, G., & Sousa, S. (2024). A systematic literature review of user trust in ai-enabled systems: An hci perspective. International Journal of Human– Computer Interaction, 40(5), 1251–1266. https://doi.org/10.1080/10447318.2022.2138826 DOI: https://doi.org/10.1080/10447318.2022.2138826

Bannister, P., Alcalde Peñalver, E., & Santamaría Urbieta, A. (2023). Transnational higher education cultures and generative ai: A nominal group study for policy development in English medium instruction. Journal for Multicultural Education. https://doi.org/10.1108/ JME-10-2023-0102

Baque Guerra, V. E., Zavala O’Brien, M. I., Mendoza Bajaña, V. P., Recalde Villacrés, E. B., Nevares Pacheco, M. D. J., Castillo Rodríguez, N. E., & Barreto Zúñiga, W. W. (2024). Percepciones y experiencias de docentes universitarios sobre la inteligencia artificial: Transformación, ética y desafíos en el uso académico por estudiantes. latam Revista Latinoamericana de Ciencias Sociales y Humanidades, 5(6). https://doi.org/10.56712/latam.v5i6.3204 DOI: https://doi.org/10.56712/latam.v5i6.3204

Barus, O. P., Hidayanto, A. N., Handri, E. Y., Sensuse, D. I., & Yaiprasert, C. (2025). Shaping generative ai governance in higher education: Insights from student perception. International Journal of Educational Research Open, 8, 100452. https://doi.org/10.1016/j.ijedro.2025.100452 DOI: https://doi.org/10.1016/j.ijedro.2025.100452

Beñalet, C. E. C., Jr., M. E. P., Baldezamo, R. C., Vicente, R. S., Reomero, J. I., & Junsay, M. D. (2023). A systematic review on educational strategic management. International Journal of Research and Innovation in Social Science, vii(X), 2371–2383. https://doi.org/10.47772/IJRISS.2023.701177 DOI: https://doi.org/10.47772/IJRISS.2023.701177

Bick, A., Blandin, A., & Deming, D. (2024). The rapid adoption of generative ai (nber Working Paper No. 32966). National Bureau of Economic Research. https://doi.org/10.3386/w32966 DOI: https://doi.org/10.3386/w32966

Börekci, C., & Çelik, Ö. (2024). Exploring the role of digital literacy in university students’ engagement with ai through the Technology Acceptance Model. Sakarya University Journal of Education, 14(Special Issue-ai in Education), 228–249. https://doi.org/10.19126/suje.1468866 DOI: https://doi.org/10.19126/suje.1468866

Cai, Y., Ma, J., & Chen, Q. (2020). Higher education in innovation ecosystems. Sustainability, 12(11), 4376. https://doi.org/10.3390/su12114376 DOI: https://doi.org/10.3390/su12114376

Cheong, B. C. (2024). Transparency and accountability in ai systems: Safeguarding wellbeing in the age of algorithmic decision-making. Frontiers in Human Dynamics, 6, 1421273. https://doi.org/10.3389/fhumd.2024.1421273 DOI: https://doi.org/10.3389/fhumd.2024.1421273

Duran-Seguel, I., & Gallegos Rivera, M. (2020). Análisis de las decisiones estratégicas que facilitaron la implementación de un nuevo modelo de gestión universitaria. Universidad Santo Tomás. Universidad Santo Tomás. https://doi.org/10.15332/dt.inv.2020.01566 DOI: https://doi.org/10.15332/dt.inv.2020.01566

Evans, N., Miklosik, A., & Du, J. T. (2023). University-industry collaboration as a driver of digital transformation: Types, benefits and enablers. Heliyon, 9(10), e21017. https://doi.org/10.1016/j.heliyon.2023.e21017 DOI: https://doi.org/10.1016/j.heliyon.2023.e21017

Feng, J., Yu, B., Tan, W. H., Dai, Z., & Li, Z. (2025). Key factors influencing educational technology adoption in higher education: A systematic review. plos Digital Health, 4(4), e0000764. https://doi.org/10.1371/journal.pdig.0000764 DOI: https://doi.org/10.1371/journal.pdig.0000764

Ferk Savec, V., & Jedrinović, S. (2025). The role of ai implementation in higher education in achieving the Sustainable Development Goals: A case study from Slovenia. Sustainability, 17(1), 183. https://doi.org/10.3390/su17010183 DOI: https://doi.org/10.3390/su17010183

Floridi, L. (2023). The ethics of artificial intelligence: Principles, challenges, and opportunities. Oxford University Press. https://doi.org/10.1093/oso/9780198883098.001.0001 DOI: https://doi.org/10.1093/oso/9780198883098.001.0001

Jin, Y., Yan, L., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. (2025). Generative ai in higher education: A global perspective of institutional adoption policies and guidelines. Computers and Education: Artificial Intelligence, 8, 100348. https://doi.org/10.1016/j.caeai.2024.100348 DOI: https://doi.org/10.1016/j.caeai.2024.100348

Khairullah, S. A., Harris, S., Hadi, H. J., Sandhu, R. A., Ahmad, N., & Alshara, M. A. (2025). Implementing artificial intelligence in academic and administrative processes through responsible strategic leadership in higher education institutions. Frontiers in Education, 10, 1548104. https://doi.org/10.3389/feduc.2025.1548104 DOI: https://doi.org/10.3389/feduc.2025.1548104

Khan, A. A., Badshah, S., Liang, P., Khan, B., Waseem, M., Niazi, M., & Akbar, M. A. (2021). Ethics of ai: A systematic literature review of principles and challenges (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2109.07906 DOI: https://doi.org/10.1145/3530019.3531329

Khan, S., Mazhar, T., Shahzad, T., Khan, M. A., Rehman, A. U., Saeed, M. M., & Hamam, H. (2025). Harnessing ai for sustainable higher education: Ethical considerations, operational efficiency, and future directions. Discover Sustainability, 6(1), 23. https://doi.org/10.1007/s43621- 025-00809-6 DOI: https://doi.org/10.1007/s43621-025-00809-6

Kitchenham, B., Pretorius, R., Budgen, D., Pearl Brereton, O., Turner, M., Niazi, M., & Linkman, S. (2010). Systematic literature reviews in software engineering – A tertiary study. Information and Software Technology, 52(8), 792–805. https://doi.org/10.1016/j.infsof.2010.03.006 DOI: https://doi.org/10.1016/j.infsof.2010.03.006

Kovari, A. (2024). ai for decision support: Balancing accuracy, transparency, and trust across sectors. Information, 15(11), 725. https://doi.org/10.3390/info15110725 DOI: https://doi.org/10.3390/info15110725

Makarius, E. E., Mukherjee, D., Fox, J. D., & Fox, A. K. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research, 120, 262–273. https://doi.org/10.1016/j.jbusres.2020.07.045 DOI: https://doi.org/10.1016/j.jbusres.2020.07.045

Martínez-Silva, F.-C., & Cobos-Lozada, C.-A. (2024). Revisión sistemática de las herramientas de inteligencia artificial explicable usadas en métodos de ensamble. Revista Facultad de Ingeniería, 33(70), e18078. https://doi.org/10.19053/uptc.01211129.v33.n70.2024.18078 DOI: https://doi.org/10.19053/uptc.01211129.v33.n70.2024.18078

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022). A survey on bias and fairness in machine learning. acm Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607 DOI: https://doi.org/10.1145/3457607

Metcalf, J., Moss, E., Watkins, E. A., Singh, R., & Elish, M. C. (2021). Algorithmic impact assessments and accountability: The co-construction of impacts. In Proceedings of the 2021 acm Conference on Fairness, Accountability, and Transparency (pp. 735–746). Association for Computing Machinery. https://doi.org/10.1145/3442188.3445935 DOI: https://doi.org/10.1145/3442188.3445935

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., et al. (2021). Declaración prisma 2020: Una guía actualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología, 74(9), 790–799. https://doi.org/10.1016/j.recesp.2021.06.016 DOI: https://doi.org/10.1016/j.rec.2021.07.010

Pan, Z., Moore, O. A., Papadimitriou, A., & Zhu, J. (2025). ai literacy and trust: A multi-method study of Human-gai team collaboration. Computers in Human Behavior: Artificial Humans, 4, 100162. https://doi.org/10.1016/j.chbah.2025.100162 DOI: https://doi.org/10.1016/j.chbah.2025.100162

Phillips, B. (2021). uk further education sector journey to compliance with the General Data Protection Regulation and the Data Protection Act 2018. Computer Law & Security Review, 42, 105586. https://doi.org/10.1016/j.clsr.2021.105586 DOI: https://doi.org/10.1016/j.clsr.2021.105586

Raji, I. D., Xu, P., Honigsberg, C., & Ho, D. E. (2022). Outsider oversight: Designing a third-party audit ecosystem for ai governance (Version 1). arXiv. ht https://doi.org/10.1145/3514094.3534181 DOI: https://doi.org/10.1145/3514094.3534181

Rana, M. M., Siddiqee, M. S., Sakib, M. N., & Ahamed, M. R. (2024). Assessing ai adoption in developing country academia: A trust and privacy-augmented utaut framework. Heliyon, 10(18), e37569. https://doi.org/10.1016/j.heliyon.2024.e37569 DOI: https://doi.org/10.1016/j.heliyon.2024.e37569

Sánchez-Prieto, J. C., Izquierdo-Álvarez, V., Del Moral-Marcos, M. T., & Martínez-Abad, F. (2024). Inteligencia artificial generativa para autoaprendizaje en educación superior: Diseño y validación de una máquina de ejemplos. ried-Revista Iberoamericana de Educación a Distancia, 28(1). https://doi.org/10.5944/ried.28.1.41548 DOI: https://doi.org/10.5944/ried.28.1.41548

Sauer, P. C., & Seuring, S. (2023). How to conduct systematic literature reviews in management research: A guide in 6 steps and 14 decisions. Review of Managerial Science, 17(5), 1899–1933. https://doi.org/10.1007/s11846-023-00668-3 DOI: https://doi.org/10.1007/s11846-023-00668-3

Shamim, S., Yang, Y., Ul Zia, N., Khan, Z., & Shariq, S. M. (2023). Mechanisms of cognitive trust development in artificial intelligence among front line employees: An empirical examination from a developing economy. Journal of Business Research, 167, 114168. https://doi.org/10.1016/j.jbusres.2023.114168 DOI: https://doi.org/10.1016/j.jbusres.2023.114168

Stolze, A., & Sailer, K. (2022). Advancing heis’ third-mission through dynamic capabilities: The role of leadership and agreement on vision and goals. The Journal of Technology Transfer, 47, 580–604. https://doi.org/10.1007/s10961-021-09850-9 DOI: https://doi.org/10.1007/s10961-021-09850-9

United Nations Statistics Division. (2025). sdg indicators metadata repository. https://unstats. un.org/sdgs/metadata/

Viberg, O., Cukurova, M., Feldman-Maggor, Y., Alexandron, G., Shirai, S., Kanemune, S., Wasson, B., Tømte, C., Spikol, D., Milrad, M., Coelho, R., & Kizilcec, R. F. (2025). What explains teachers’ trust in ai in education across six countries? International Journal of Artificial Intelligence in Education, 35(3), 1288–1316. https://doi.org/10.1007/s40593-024-00433-x DOI: https://doi.org/10.1007/s40593-024-00433-x

Virkar, S., Alexopoulos, C., Tsekeridou, S., & Novak, A.-S. (2022). A user-centred analysis of decision support requirements in legal informatics. Government Information Quarterly, 39(3), 101713. https://doi.org/10.1016/j.giq.2022.101713 DOI: https://doi.org/10.1016/j.giq.2022.101713

Wirtz, B. W., Weyerer, J. C., & Kehl, I. (2022). Governance of artificial intelligence: A risk and guideline-based integrative framework. Government Information Quarterly, 39(4), 101685. https://doi.org/10.1016/j.giq.2022.101685 DOI: https://doi.org/10.1016/j.giq.2022.101685

Yu, L., Li, Y., & Fan, F. (2023). Employees’ appraisals and trust of artificial intelligences’ transparency and opacity. Behavioral Sciences, 13(4), 344. https://doi.org/10.3390/bs13040344 DOI: https://doi.org/10.3390/bs13040344

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