by Wilson Alejandro Flores Ortiz, Isabel Cecilia Llerena Rangel, Luis Eduardo Muñoz Guerrero, Flor Quispe Román, David Adán Zegarra Hidalgo
ABSTRACT
The emergence of artificial intelligence (AI)—especially generative AI—is transforming university learning. This article synthesizes recent evidence (2021–2025) on how cognitive variables (cognitive load, metacognition, and learning self-regulation) mediate the effects of AI-based systems (intelligent tutors, chatbots, and learning analytics) on student performance and experience. A narrative review of systematic reviews, meta-analyses and experimental studies in higher education was conducted. The findings indicate moderate to large positive effects of generative AI on performance and higher-order thinking when metacognitive scaffolding is provided and instructional design is taken care of to align cognitive load (Wang & Fan, 2025; Deng et al., 2024). The use of AIsupported tutors and learning analytics improves self-regulation in planning, execution, and reflection phases, although gaps persist in the fine measurement of cognitive processes and in adaptation to diverse contexts (Heikkinen et al., 2022; Rodríguez-Ortiz et al., 2025). Cognitive instructional design guidelines (extrinsic overload reduction, metacognitive prompts, and adaptive feedback) are proposed to maximize benefits and mitigate risks.
