Desafíos y oportunidades de la inteligencia artificial en el aprendizaje colaborativo: implicancias para la innovación educativa en contextos institucionales

Miguel Sagredo-Gallardo; José González Campos; Carmen Alfaro Contreras; Marina Elias

doi:10.31637/epsir-2026-2211

Authors

Miguel Sagredo-Gallardo Universidad de Playa Ancha de Ciencias de la Educación https://orcid.org/0009-0008-1784-8557
José González Campos Catholic University of the Maule https://orcid.org/0000-0003-4610-6874
Carmen Alfaro Contreras Catholic University of the Maule https://orcid.org/0000-0002-9084-8624
Marina Elias Universitat de Barcelona https://orcid.org/0000-0002-8268-4965

DOI:

https://doi.org/10.31637/epsir-2026-2211

Keywords:

artificial intelligence, collaborative learning, educational innovation, narrative review, institutional transformation, educational policies, higher education, personalization of learning

Abstract

Introduction: Artificial intelligence (AI) is transforming collaborative learning, opening up unprecedented opportunities and tensions in educational contexts. This article presents a narrative review focusing on the impact of AI on collaborative practices and its implications for institutional educational innovation. Methodology: 24 studies published between 2018 and 2024, selected for their thematic relevance and critical contribution, were analyzed. The review integrates pedagogical, technological and ethical dimensions from an approach oriented to educational transformation. Results: Benefits such as personalization of learning and improvement of AI-mediated teamwork are identified. However, relevant challenges persist: loss of autonomy, digital inequality, ethical dilemmas and poor teacher preparation. Discussion: The evidence reveals that the adoption of AI without a critical pedagogical orientation can deepen gaps and reduce student protagonism. It is proposed to frame its use in institutional strategies of social and educational innovation. Conclusion: This review provides an integrative view for the design of educational and institutional policies that ensure an ethical, contextualized and transformative implementation of AI in collaborative environments.

Downloads

Download data is not yet available.

Author Biographies

Miguel Sagredo-Gallardo, Universidad de Playa Ancha de Ciencias de la Educación

Miguel Sagredo-Gallardo. Primary School Teacher, Bachelor of Education from the University of Playa Ancha, Chile. Master's Degree in Educational Assessment and Doctorate in Educational Policy and Management from the same university. Current areas of research: educational policy, collaborative learning and artificial intelligence applied to education. Currently, academic secretary at the Technological Institute of the University of Playa Ancha and professor in the Master's Degree in Inclusive Education at the University of Playa Ancha.

José González Campos, Catholic University of the Maule

Professor of mathematics and computing, graduate in education from the University of Playa Ancha, master's degree in statistics with a focus on psychometrics from the Pontifical Catholic University of Valparaíso, PhD in statistics from the State University of Campinas, Brazil. Postdoctoral degree in quality in higher education from IESED-Chile. Academic at the Catholic University of Maule, director of mathematics, physics and statistics at the Faculty of Basic Sciences, member of the doctoral faculty and researcher responsible for the Fondecyt Regular-Chile project.

Carmen Alfaro Contreras, Catholic University of the Maule

Bachelor of Science with a major in Biology and Master of Biological Sciences from the University of Chile and Doctor of Education from Bernardo O'Higgins University. Academic and researcher at the Catholic University of Maule. She has nearly a decade of teaching experience in the Chilean school system and in higher education institutions, focusing on science teacher training. She is a young researcher at the Millennium Nucleus for Research on Chilean Anti-Racist Research and a member of the Technical Advisory Committee of the UCM Gender InES.

Marina Elias, Universitat de Barcelona

Associate professor in the Department of Sociology at the University of Barcelona, specialising in the sociology of education. Her research focuses on educational inequalities in the field of education. This includes analysing students' transitions and trajectories to post-compulsory education, student profiles in terms of motivations, study strategies, and commitment across inequalities such as social background, ethnic differentiation, gender, and other living conditions.

She coordinates an Innovative Teaching Group (GIDASRES), and her teaching activities include Sociology of Education in various university degrees, master's programmes and other educational institutions. She is currently very focused on improving teaching to accommodate all types of students and thus improve their results and retention. She coordinates various subjects and teaching teams and teaching improvement groups, particularly in the master's programme for secondary school teachers.

References

Alkhaldi, S. y AlZoubi, O. (2022). Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning. International Journal of Advanced Computer Science and Applications, 13(11), 654-661. https://doi.org/10.14569/IJACSA.2022.0131177 DOI: https://doi.org/10.14569/IJACSA.2022.0131177

Almulla, M. A. (2024). Investigating influencing factors of learning satisfaction in AI ChatGPT for research: University students perspective. Heliyon, 10, e32220. https://doi.org/10.1016/j.heliyon.2024.e32220 DOI: https://doi.org/10.1016/j.heliyon.2024.e32220

Andersen, R., Mørch, A. I. y Litherland, K. T. (2022). Collaborative learning with block-based programming: Investigating human-centered artificial intelligence in education. Behaviour & Information Technology, 41(9), 1830-1847. https://doi.org/10.1080/0144929X.2022.2083981 DOI: https://doi.org/10.1080/0144929X.2022.2083981

Arnold, O., Golchert, S., Rennert, M. y Jantke, K. P. (2023). Interactive collaborative learning with explainable artificial intelligence. En M. E. Auer, W. Pachatz y T. Rüütmann (Eds.), Learning in the Age of Digital and Green Transition. Proceedings of the 25th International Conference on Interactive Collaborative Learning (ICL 2022) (pp. 13-24). Springer Nature. https://doi.org/10.1007/978-3-031-26876-2_2 DOI: https://doi.org/10.1007/978-3-031-26876-2_2

Aromataris, E., Lockwood, C., Porritt, K., Pilla, B. y Jordan, Z. (Eds.). (2024). JBI manual for evidence synthesis. JBI. https://doi.org/10.46658/JBIMES-24-01 DOI: https://doi.org/10.46658/JBIMES-24-01

Boudia, C. y Bengueddach, A. (2024). Innovating Higher Education: The Rise of ChatGPT in Collaborative Learning – A Short Review. En M. Shelley y O. T. Ozturk (Eds.), Proceedings of ICRES 2024 – International Conference on Research in Education and Science (pp. 1347-1370). Antalya, Türkiye: ISTES.

Dempere, J., Modugu, K., Hesham, A. y Ramasamy, L. K. (2023). The impact of ChatGPT on higher education. Frontiers in Education, 8, 1206936. https://doi.org/10.3389/feduc.2023.1206936 DOI: https://doi.org/10.3389/feduc.2023.1206936

Farrelly, T. y Baker, N. (2023). Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice. Education Sciences, 13(11), 1109. https://doi.org/10.3390/educsci13111109 DOI: https://doi.org/10.3390/educsci13111109

Gil-Vera, V. D. (2024). Enhancing active learning through collaboration between human teachers and generative AI. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-024-12231-z

Haataja, E., Järvelä, S., Malmberg, J. y Sobocinski, M. (2023). The unspoken aspect of socially shared regulation in collaborative learning: AI-driven learning analytics unveiling ‘silent pauses’. British Journal of Educational Technology, 54(5), 1065-1081. https://doi.org/10.1111/bjet.13335 DOI: https://doi.org/10.1111/bjet.13335

Holstein, K., Aleven, V., Hong, Y. C. y Rummel, N. (2023). How teachers conceptualise shared control with an AI co-orchestration tool: A multiyear teacher-centred design process. British Journal of Educational Technology, 54(1), 56-79. https://doi.org/10.1111/bjet.13281 DOI: https://doi.org/10.1111/bjet.13281

Holstein, K., McLaren, B. M., Aleven, V. y Jermann, P. (2023). Pair-Up: Prototyping Human-AI Co-orchestration of Dynamic Transitions between Individual and Collaborative Learning in the Classroom. International Journal of Artificial Intelligence in Education, 33, 803-843. https://doi.org/10.1007/s40593-023-00353-8

Holstein, K., Yang, Y., Yu, Y., Aleven, V. y Rummel, N. (2023). Surveying Teachers’ Preferences and Boundaries Regarding Human-AI Control in Dynamic Pairing of Students for Collaborative Learning. International Journal of Artificial Intelligence in Education, 33,

803-843. https://doi.org/10.1007/s40593-022-00325-3

Kamaludin, K., Zakaria, S., Mohd Yunus, A. S. y Hassan, N. F. N. (2023). Evaluating Students Acceptance of AI Chatbot to Enhance Virtual Collaborative Learning in Malaysia. Education and Information Technologies, 28, 10701-10728.

https://doi.org/10.1007/s10639-023-12042-w

Lee, S., Kim, B., Kim, J. y Song, J. (2023). Collaborative learning with artificial intelligence speakers: Pre-service elementary science teachers’ responses to the prototype. International Journal of Science and Mathematics Education. https://doi.org/10.1007/s10763-023-10391-0

Lorente, S., Arnal-Palacián, M. y Paredes-Velasco, M. (2024a). Effectiveness of cooperative, collaborative, and interdisciplinary learning guided by software development in Spanish universities. European Journal of Psychology of Education, 39, 4467-4491. https://doi.org/10.1007/s10212-024-00881-y DOI: https://doi.org/10.1007/s10212-024-00881-y

Ma, T. (2025). Systematically visualizing ChatGPT used in higher education: Publication trend, disciplinary domains, research themes, adoption and acceptance. Computers and Education: Artificial Intelligence, 8, 100336. https://doi.org/10.1016/j.caeai.2024.100336 DOI: https://doi.org/10.1016/j.caeai.2024.100336

Naik, A., Yin, J. R., Kamath, A., Ma, Q., Wu, S. T., Murray, R. C., Bogart, C., Sakr, M. y Rose, C. P. (2025). Providing tailored reflection instructions in collaborative learning using large language models. British Journal of Educational Technology, 56(2), 531-550. https://doi.org/10.1111/bjet.13548 DOI: https://doi.org/10.1111/bjet.13548

Ouyang, F. y Zhang, L. (2024). AI-driven learning analytics applications and tools in computer-supported collaborative learning: A systematic review. Educational Research Review, 44, 100616. https://doi.org/10.1016/j.edurev.2024.100616 DOI: https://doi.org/10.1016/j.edurev.2024.100616

Ouyang, F., Xu, W. y Cukurova, M. (2023). An artificial intelligence-driven learning analytics method to examine the collaborative problem-solving process from the complex adaptive systems perspective. International Journal of Computer-Supported Collaborative Learning, 18, 39–66. https://doi.org/10.1007/s11412-023-09387-z DOI: https://doi.org/10.1007/s11412-023-09387-z

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D. y Alonso-Fernández, S. (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.recesp.2021.06.016

Ramadevi, J., Sushama, C., Balaji, K., Talasila, V., Sindhwani, N. y Mukti. (2023). AI enabled value-oriented collaborative learning: Centre for innovative education. Journal of High Technology Management Research, 34, 100478. https://doi.org/10.1016/j.hitech.2023.100478 DOI: https://doi.org/10.1016/j.hitech.2023.100478

Saúde, S., Barros, J. P. y Almeida, I. (2024). Impacts of Generative Artificial Intelligence in Higher Education: Research Trends and Students’ Perceptions. Social Sciences, 13(8), 410. https://doi.org/10.3390/socsci13080410 DOI: https://doi.org/10.3390/socsci13080410

Tan, S. C., Lee, A. V. Y. y Lee, M. (2022). A systematic review of artificial intelligence techniques for collaborative learning over the past two decades. Computers and Education: Artificial Intelligence, 3, 100097. https://doi.org/10.1016/j.caeai.2022.100097 DOI: https://doi.org/10.1016/j.caeai.2022.100097

Tchounikine, P. y Dillenbourg, P. (2022). Teacher Artificial Intelligence-supported pedagogical actions in collaborative learning coregulation: A Wizard-of-Oz study. Computers and Education, 182, 104470. https://doi.org/10.1016/j.compedu.2022.104470 DOI: https://doi.org/10.1016/j.compedu.2022.104470

Tzirides, A. O., Zapata, G., Kastania, N. P., Saini, A. K., Castro, V., Ismael, S. A., You, Y., Afonso dos Santos, T., Searsmith, D., O’Brien, C., Cope, B. y Kalantzis, M. (2024). Combining human and artificial intelligence for enhanced AI literacy in higher education. Computers and Education Open, 6, 100184. https://doi.org/10.1016/j.caeo.2024.100184 DOI: https://doi.org/10.1016/j.caeo.2024.100184

Wei-Kocsis, J., Sabounchi, M., Yang, B. y Zhang, T. (2022). Cybersecurity education in the age of artificial intelligence: A novel proactive and collaborative learning paradigm. In IEEE Frontiers in Education Conference (FIE), 2022, 1-7. https://doi.org/10.1109/FIE56618.2022.9962643 DOI: https://doi.org/10.1109/FIE56618.2022.9962643

Wu, W. C. V., Liao, Y. W., Lin, C. H. y Huang, Y.-M. (2023). The effects of different patterns of group collaborative learning on fourth-grade students’ creative thinking in a digital artificial intelligence course. Education and Information Technologies, 28, 12175-12201. https://doi.org/10.1007/s10639-023-11694-z

Wu, T. T., Mustika Sari, N. A. R. y Huang, Y. M. (2024). Integrating extended formative assessment in flipped jigsaw learning: Promoting learning engagement and higher-order thinking skills in international business education context. The International Journal of Management Education, 22, 100930. https://doi.org/10.1016/j.ijme.2024.100930 DOI: https://doi.org/10.1016/j.ijme.2024.100930

Xie, H., Yang, Y. y Hwang, G. J. (2023). Exploratory study of an AI-supported discussion representational tool for online collaborative learning in a Chinese university. Educational Technology Research and Development, 71, 1779-1805.

https://doi.org/10.1007/s11423-022-10144-7

Zhang, X., Liu, Q., Yang, S. J. H. y Kang, S. (2023). An AI-enabled feedback-feedforward approach to promoting online collaborative learning. Educational Technology Research and Development, 71, 2515–2542. https://doi.org/10.1007/s11423-023-10269-0