Collaborative strategies in the integration of artificial intelligence in educational processes
https://orcid.org/0000-0001-7538-1563
The integration of artificial intelligence into educational processes requires the collaboration of multiple actors with diverse interests, levels of influence, and objectives. In this context, the present study aimed to analyze the relations among the actors involved in the integration of artificial intelligence into educational processes and to identify collaborative strategies that promote a sustainable, ethical, and effective implementation of this technology. A non-experimental, cross-sectional, and descriptive research approach was developed, based on a literature review and strategic analysis using the MACTOR technique. The assessment of the influence matrices among actors and their positions regarding the objectives was carried out by a panel of eight participants representing profiles related to artificial intelligence, education, project management, and the student community. The results identified governments and regulatory agencies as linking actors within the system, while non-governmental organizations, educational associations, and researchers exhibited the highest levels of strategic power. Furthermore, significant convergences were evident among most of the actors, as well as a specific conflict between technology companies and governments regarding the development of technological solutions. Based on these findings, eight collaborative strategies were formulated to strengthen inter-institutional coordination, promote consensus-based regulatory frameworks, boost continuing education, and foster open research. These results provide a basis for guiding decision-making and strengthening the responsible integration of artificial intelligence into educational processes.
Abulibdeh, A., Zaidan, E., & Abulibdeh, R. (2024). Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: challenges, opportunities, and ethical dimensions. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2023.140527
Aguirre Castillo, D. A. (2026). Inteligencia artificial en procesos de selección de personal: Sesgos y oportunidades. Conocimiento Global, 11(1), 33–43. https://doi.org/10.70165/cglobal.v11i1.650
Alam, A. (2021). Possibilities and apprehensions in the landscape of artificial intelligence in education. International Conference on Computational Intelligence and Computing Applications (ICCICA)IEEE., 1-8. https://doi.org/10.1109/ICCICA52458.2021.9697272
Ansell, C., & Gash, A. (2008). Collaborative governance in theory and practice. Journal of Public Administration Research and Theory, 18(4), 543–571. https://doi.org/10.1093/jopart/mum032
Beins, B., & McCarthy, M. (2017). Research methods and statistics. Cambridge University Press. https://www.google.com.co/books/edition/Research_Methods_and_Statistics/3y42DwAAQBAJ?hl=es&gbpv=1&dq=Research+methods+and+statistics&printsec=frontcover
Castañeda, L., & Selwyn, N. (2018). More than tools? Making sense of the ongoing digitizations of higher education. International Journal of Educational Technology in Higher Education, 15, 1-10. https://doi.org/10.1186/s41239-018-0109-y
Celeita Murcia, O., Arango Pastrana, C., & Peña Montoya, C. (2026). Aceptación del e-learning en la educación superior: Un análisis bibliométrico de la producción científica en Scopus (2015–2024). Conocimiento Global, 11(1), 13–32. Conocimiento Global, 11(1), 13-32. https://doi.org/10.70165/cglobal.v11i1.633
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Cukurova, M., Miao, X., & Brooker, R. (2023). Adoption of artificial intelligence in schools: unveiling factors influencing teachers’ engagement. In International conference on artificial intelligence in education. Springer Nature, 151-163. https://doi.org/10.1007/978-3-031-36272-9_13
Dempere, J., Flores, P., & Allam, H. (2023). The challenges posed by national artificial intelligence strategies and policies on higher education institutions. Proceedings of the HCT International general education conference, 185-200. https://doi.org/10.2991/978-94-6463-286-6_15
Duan, Y., Edwards, J., & Dwivedi, Y. (2019). Artificial intelligence for decision making in the era of big data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Farzaneh, H., Malehmirchegini, L., Bejan, A., Afolabi, T., Mulumba, A., & Daka, P. (2021). Artificial intelligence evolution in smart buildings for energy efficiency. Applied Sciences, 11(2), 763. https://doi.org/10.3390/app11020763
Gali, Y., & Schechter, C. (2020). NGO involvement in education policy: Principals' voices. International Journal of Educational Management, 34(10), 1509-1525. https://doi.org/10.1108/IJEM-02-2020-0115
Garg, R. (2016). Methodology for research I. Indian Journal of Anaesthesia, 60(9), 640–645. https://doi.org/10.4103/0019-5049.190619
Gebel, M. (2023). Causal inference based on non-experimental data in health inequality research. Handbook of Health Inequalities Across the Life Course, 93-111. https://doi.org/10.4337/9781800888166.00014
Godet, M., & Durance, P. (2011). Strategic foresight for companies and territories. Dunod.
Hlongwane, J., Shava, G., Mangena, A., & Muzari, T. (2024). Towards the integration of artificial intelligence in higher education, challenges and opportunities: the African context, a case of Zimbabwe. International Journal of Research and Innovation in Social Science, 8(3S), 417-435. https://doi.org/10.47772/IJRISS.2024.803028S
Karan, B., & Angadi, G. (2025). Artificial intelligence integration into school education: A review of Indian and foreign perspectives. Millennial Asia, 16(1). https://doi.org/10.1177/09763996231158229
Liebig, L., Güttel, L., Jobin, A., & Katzenbach, C. (2024). Subnational AI policy: shaping AI in a multi-level governance system. AI & Society, 39(3), 1477-1490. https://doi.org/10.1007/s00146-022-01561-5
Macharis, C., & Bernardini, A. (2015). Reviewing the use of Multi-Criteria Decision Analysis for the evaluation of transport projects: Time for a multi-actor approach. Transport Policy, 37, 177-186. https://doi.org/10.1016/j.tranpol.2014.11.002
Macioszek, E., Cieśla, M., & Granà, A. (2023). Future development of an energy-efficient electric scooter sharing system based on a stakeholder analysis method. Energies, 16(1), 554. https://doi.org/10.3390/en16010554
Muhie, Y., & Woldie, A. (2020). Integration of artificial intelligence technologies in teaching and learning in higher education. Science and Technology, 10(1), 1-7. https://doi.org/10.5923/j.scit.202001001.01
Teegen, H., Doh, J., & Vachani, S. (2004). The importance of nongovernmental organizations (NGOs) in global governance and value creation: An international business research agenda. Journal of International Business Studies, 35, 463–483. https://doi.org/10.1057/palgrave.jibs.8400112
Wang, X., & Cheng, Z. (2020). Cross-sectional studies: strengths, weaknesses, and recommendations. Chest, 158(1), S65-S71. https://doi.org/10.1016/j.chest.2020.03.012
Wawak, S., Teixeira, J., & Sampaio, P. (2024). Quality 4.0 in higher education: reinventing academic-industry-government collaboration during disruptive times. The TQM Journal, 36(6), 1569-1590. https://doi.org/10.1108/TQM-07-2023-0219
Zakaria, N., & Hashim, H. (2024). Shaping the future of education: Conceptualising pre-service teachers’ perspectives on artificial intelligence integration. International Journal of Academic Research in Business & Social Sciences, 14(5). https://dx.doi.org/10.6007/IJARBSS/v14-i5/21584
Zawacki-Richter, O., Marín, V., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0