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Generative artificial intelligence in education: a systematic analysis of opportunities, challenges, and responses
(Taylor & Francis, 2025-08-04) García López, Iván Miguel; Ramírez Montoya, María Soledad; Molina Espinosa, José Martín; Tecnológico de Monterrey
Generative artificial intelligence (GenAI), exemplified by tools such as ChatGPT, is transforming the educational field by offering innovative solutions for the personalization of learning, student motivation, and the optimization of pedagogical processes. The core research question is: How is GenAI transforming educational processes, and what are the challenges and opportunities associated with its implementation? To answer it, a systematic literature review (SLR) was conducted, analyzing recent studies that address applications, policies, and limitations of AI in education. The main findings highlight three key constructs: personalization of learning, which improves academic performance through adaptive interventions; educational innovation, which boosts motivation through interactive and gamified environments; and the need for ethical and inclusive regulation, which guarantees transparency and equity in the use of these technologies. In addition, gaps were identified in institutional policies and existing legal regulation, evidencing the urgency of updating them to respond to emerging challenges. The added value of this study lies in its holistic and comprehensive approach, which connects pedagogical applications, ethical challenges, and regulatory policies, proposing concrete recommendations for the responsible and effective implementation of AI in educational contexts.
Psychometric assessment of a tool to evaluate motivation and knowledge of an energy-related topic MOOC
(Taylor & Francis, 2022-01-31) Valdivia Vázquez, Juan Antonio; Valenzuela González, Jaime Ricardo; Ramírez Montoya, María Soledad; Instituto Tecnológico y de Estudios Superiores de Monterrey
Online education has increased over the last two decades in response to an increasing learning demand. Massive open online courses (MOOCs) seem to be an adequate tool to facilitate current educational dynamics. However, evaluating students’ motivation and learning to predict their engagement when attending a MOOC represents a challenge. This study combines characteristics of data from MOOC environments (e.g., large sample size) with well-established psychometric methodology (validity and reliability estimations) to attain a reliable resource to evaluate MOOC participants. Four independent datasets from MOOCs on energy-related topics were used to psychometrically examine the new “Initial survey of interests, motivations, and knowledge regarding MOOCs” (EIIMC-MOOC). The results indicate that the EIIMCMOOC is a valid, reliable and stable tool to evaluate initial motivation and prior knowledge of participants attending energy-related topics.

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