AI-Assisted Self-Regulated Learning in English Language Education: A Conceptual Model for Human–Technology Scaffolding

Abstract

The integration of artificial intelligence (AI) into English language education has generated considerable scholarly interest, particularly in the context of self-regulated learning (SRL). Despite the rapid proliferation of AI-powered tools such as intelligent tutoring systems, automated writing evaluators, and speech recognition platforms, a coherent conceptual model that systematically articulates the interplay between human pedagogical agency and technological scaffolding remains underdeveloped. This paper addresses this gap by proposing a conceptual model of Human–Technology Scaffolding (HTS) in AI-assisted SRL for tertiary-level English-language education. Drawing upon Zimmerman’s cyclical model of SRL, Vygotsky’s zone of proximal development, and recent advances in human–AI co-regulation, the proposed framework delineates three interconnected layers: adaptive AI scaffolding, metacognitive mediation, and instructor-guided co-regulation. Through a synthesis of 70 peer-reviewed sources from Scopus-indexed journals published between 2018 and 2026, the paper critically examines how AI tools support the forethought, performance monitoring, and self-reflection phases of SRL across writing, speaking, listening, and reading. The model further accounts for ethical dimensions, including algorithmic bias, data privacy, digital equity, and the risk of over-reliance on automated systems. Implications for curriculum design, teacher professional development, institutional policy, and technology development are discussed. The paper concludes with a research agenda that emphasises the need for mixed-methods empirical validation, culturally responsive AI design, and longitudinal studies on affective and motivational outcomes in diverse educational contexts.