Smart Pedagogical Strategies and Autonomous Learning in Higher Education in Peru

Abstract

Autonomy and self-regulation among university students constitute key factors for the development of metacognitive skills and the strengthening of meaningful learning. In this context, the present study analyzes the relationship between Artificial Intelligence (AI)–based methodological strategies and the development of autonomous learning in higher education students. The objective was to predict and explain the variance of the constructs AI methodological strategies, autonomous learning, and academic development. A quantitative approach was adopted and implemented in two phases: an exhaustive theoretical review and the administration of an online survey to a sample of 383 university students from different academic disciplines. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS v4 software, yielding acceptable reliability levels (α ≥ 0.70). The results revealed significant and positive relationships between AI strategies, metacognitive processes, self-regulation, and affective motivation with academic development. Specifically, the use of virtual assistants was found to be strongly associated with AI methodological strategies (β = 0.744) and with academic development (β = 0.212), as virtual assistants enhance motivation by providing adaptive and personalized resources that stimulate autonomous learning. Furthermore, the mediating role of autonomous learning in the relationship between AI and academic performance was confirmed. These findings provide empirical evidence of the pedagogical use of AI as a facilitator of autonomous learning in university contexts. It is concluded that the integration of AI strengthens student autonomy, improves learning personalization, and optimizes academic performance; however, ethical and technological accessibility challenges persist and must be addressed to ensure equitable implementation in higher education.