The article "Generative Artificial Intelligence Agent in Scientific Research: An Explanatory Analysis of Classroom Learning", by Roberto Berrios Zepeda and Lorgia Márquez Mora, examines the potential of generative artificial intelligence agents to enhance the learning of scientific research among university students.
Building on the growing interest in integrating AI into educational processes, the authors designed and evaluated a pedagogical strategy based on the use of generative AI tools to support the development of research projects. The study employed a longitudinal quasi-experimental design involving 111 students divided into one control group and two intervention groups, comparing learning outcomes achieved through traditional instruction with those obtained using an AI-assisted methodology.
The findings show that students who participated in the AI-supported methodology achieved significantly greater improvements across every stage of the research process. The most notable gains were observed in formulating research ideas, identifying knowledge gaps, developing the research problem, designing the methodology, and interpreting data.
The use of AI agents also enhanced information retrieval, reference management, project organization, and the understanding of methodological concepts. However, the authors emphasize that critical thinking, scientific writing, and methodological reflection continue to require strong guidance and support from instructors.
The study’s main contribution lies in demonstrating that generative artificial intelligence can play a valuable role in supporting the learning of scientific research when it is integrated into a carefully designed pedagogical strategy. Rather than replacing instructors, AI functions as a resource that facilitates knowledge construction and streamlines specific tasks within the research process.
At the same time, the authors acknowledge several limitations, including the lack of random assignment of participants, the reliance on self-reported data, and the potential biases of both participants and AI algorithms. They therefore recommend that future research incorporate more comprehensive evaluations of students’ final research outputs and further explore the potential of AI to foster critical understanding and more adaptive learning.
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How to Cite: Berrios Zepeda, R., & Márquez Mora, L. (2025). Generative Artificial Intelligence agent in scientific research. An explanatory analysis of classroom learning. RIED-Revista Iberoamericana de Educación a Distancia, 28(2), 39–55. https://doi.org/10.5944/ried.28.2.43545
