Autorregulación del aprendizaje en la era de la inteligencia artificial: de los modelos clásicos a la corregulación humano-IA en la educación secundaria y superior

• Palavras-chave: autorregulación del aprendizaje; inteligencia artificial; educación superior; corregulación docente; Educación 4.0; autoeficacia docente.

• Keywords: self-regulated learning; artificial intelligence; higher education; teacher co-regulation; Education 4.0; teacher self-efficacy.

• Abstract: For more than a decade, self-regulated learning (SRL) has become one of the constructs with the strongest empirical support for explaining academic success, well-being, and retention among secondary and higher education students. This chapter integrates the classic theoretical models that explain SRL (Zimmerman, Pintrich, Winne and Hadwin, and Boekaerts) with the Ibero-American empirical evidence produced over the last decade on teacher self-regulation, co-regulation, dropout, well-being, and gender differences. Building on this body of knowledge, the chapter critically examines how the generative artificial intelligence revolution, Education 4.0 environments, and intelligent classrooms are redefining the cognitive, motivational, and behavioral profile of today's learners, straining self-regulation between cognitive offloading toward intelligent systems and the possibility of a hybrid human-AI co-regulation that strengthens autonomy. An integrative model is proposed that positions teacher self-efficacy and AI training as hinge variables between the promotion of SRL and its effective exercise by students. The chapter closes with a research agenda and implications for designing educational interventions in contexts mediated by intelligent technologies.