Inteligencia artificial agéntica y desarrollo de competencias digitales en estudiantes universitarios de Los Cabos, México
Abstract
Agentic artificial intelligence refers to systems that can plan and carry out multi-step tasks with some degree of autonomy: they search for information, use digital tools, review their own output, and adjust the plan without a person stepping in at every stage. Its arrival in university classrooms raises the question of which digital competencies students need in order to work with an agent and how institutions can develop them. This documentary and propositional article reviews the literature on AI agents, the DigComp 2.2 and DigCompEdu digital competence frameworks, UNESCO’s AI competency framework for students, and recent empirical evidence on AI tutors and digital competencies. The analysis indicates that agents can support information seeking, content creation, and problem solving, but that unrestricted use encourages dependence and reduces learning. On this basis, the article proposes the Agentic Integration Model for Digital Competence Development (MIA-CD), a five-phase cycle with a set of monitoring indicators designed for higher education institutions in Los Cabos, Baja California Sur, Mexico. It concludes with the model’s limitations and a path toward validating it with field data.
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References
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Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. En International Conference on Learning Representations (ICLR 2023). https://arxiv.org/abs/2210.03629
Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), e2422633122. https://doi.org/10.1073/pnas.2422633122
Bearman, M., Tai, J., Dawson, P., Boud, D., & Ajjawi, R. (2024). Developing evaluative judgement for a time of generative artificial intelligence. Assessment & Evaluation in Higher Education, 49(6), 893–905. https://doi.org/10.1080/02602938.2024.2335321
Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21, Artículo 4. [https://doi.org/10.1186/s41239-023-00436-z
Cabero-Almenara, J., Gutiérrez-Castillo, J. J., Palacios-Rodríguez, A., & Barroso-Osuna, J. (2020). Development of the teacher digital competence validation of DigCompEdu Check-In questionnaire in the university context of Andalusia (Spain). Sustainability, 12(15), 6094. [https://doi.org/10.3390/su12156094
Cabero-Almenara, J., & Palacios-Rodríguez, A. (2020). Marco Europeo de Competencia Digital Docente «DigCompEdu» y cuestionario «DigCompEdu Check-In». EDMETIC, Revista de Educación Mediática y TIC, 9(1), 213–234. https://doi.org/10.21071/edmetic.v9i1.12462
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20, Artículo 38. [https://doi.org/10.1186/s41239-023-00408-3
Dalgıç, A., Yaşar, E., & Demir, M. (2024). ChatGPT and learning outcomes in tourism education: The role of digital literacy and individualized learning. Journal of Hospitality, Leisure, Sport & Tourism Education, 34, 100481. https://doi.org/10.1016/j.jhlste.2024.100481
Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006
González García, C., & Pallarés, N. (2026). Impacto de la IA generativa en competencias digitales universitarias: Evidencia experimental basada en el marco DigComp. RIED-Revista Iberoamericana de Educación a Distancia, 29(1), 53–77. [https://doi.org/10.5944/ried.45533
Instituto Nacional de Estadística y Geografía. (2024). Encuesta Nacional sobre Disponibilidad y Uso de Tecnologías de la Información en los Hogares (ENDUTIH) 2023 [Comunicado de prensa núm. 372/24]. [https://inegi.org.mx/contenidos/saladeprensa/boletines/2024/ENDUTIH/ENDUTIH_23.pdf
Instituto Nacional de Estadística y Geografía. (2025). Encuesta Nacional sobre Disponibilidad y Uso de Tecnologías de la Información en los Hogares (ENDUTIH) 2024: Principales resultados. [https://inegi.org.mx/contenidos/programas/endutih/2024/doc/presentacion_endutih2024.pdf
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kestin, G., Miller, K., Klales, A., Milbourne, T., & Ponti, G. (2025). AI tutoring outperforms in-class active learning: An RCT introducing a novel research-based design in an authentic educational setting. Scientific Reports, 15, 17458. [https://doi.org/10.1038/s41598-025-97652-6
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. En Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. [https://doi.org/10.54675/EWZM9535
Miao, F., Shiohira, K., & Lao, N. (2024). AI competency framework for students. UNESCO. https://doi.org/10.54675/JKJB9835
Mollick, E. R., & Mollick, L. (2023). Assigning AI: Seven approaches for students, with prompts. SSRN. [https://doi.org/10.2139/ssrn.4475995
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4.ª ed.). Pearson.
Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2025). AI agents vs. agentic AI: A conceptual taxonomy, applications and challenges [Preprint]. arXiv. [https://arxiv.org/abs/2505.10468
Schluntz, E., & Zhang, B. (2024, 19 de diciembre). Building effective agents. Anthropic. [https://www.anthropic.com/engineering/building-effective-agents
Shavit, Y., Agarwal, S., Brundage, M., Adler, S., O’Keefe, C., Campbell, R., Lee, T., Mishkin, P., Eloundou, T., Hickey, A., Slama, K., Ahmad, L., McMillan, P., Beutel, A., Passos, A., & Robinson, D. G. (2023). Practices for governing agentic AI systems. OpenAI. https://cdn.openai.com/papers/practices-for-governing-agentic-ai-systems.pdf
Strzelecki, A. (2024). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 32(9), 5142–5155. [https://doi.org/10.1080/10494820.2023.2209881
UNESCO. (2022). Recomendación sobre la ética de la inteligencia artificial. UNESCO. [https://unesdoc.unesco.org/ark:/48223/pf0000381137_spa
Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The digital competence framework for citizens. With new examples of knowledge, skills and attitudes. Publications Office of the European Union. https://doi.org/10.2760/115376
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., Zhao, W. X., Wei, Z., & Wen, J. (2024). A survey on large language model based autonomous agents. Frontiers of Computer Science, 18(6), 186345. [https://doi.org/10.1007/s11704-024-40231-1
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., … Gui, T. (2023). The rise and potential of large language model based agents: A survey [Preprint]. arXiv. [https://arxiv.org/abs/2309.07864
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. En International Conference on Learning Representations (ICLR 2023). https://arxiv.org/abs/2210.03629
Received:
2026-09-30
Published:
2026-10-01
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How to Cite
Niebla Zataraín, V. B., Niebla Zataraín, J. M., Beltrán Lizárraga, M. G., & Ojeda Campaña, J. I. (2026). Inteligencia artificial agéntica y desarrollo de competencias digitales en estudiantes universitarios de Los Cabos, México. Iberoamerican Journal of Complexity and Economics Sciences, 4(3), 185-203. Retrieved from https://revistas.ulasalle.edu.pe/ricce/article/view/402
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