Artificial Intelligence in Informal Digital Learning of English: A Systematic Review of Engagement Mechanisms and Learning Processes in Intelligent Learning Systems

Abstract

Artificial intelligence (AI) creates personalized, interactive, and low-risk learning opportunities for informal digital learning of English (IDLE). However, effective AI tools do not necessarily lead learners to engage autonomously or productively. Guided by the person–behavior–environment framework of social cognitive theory, this systematic review synthesized 58 empirical studies published in SSCI-indexed journals between 2020 and 2025 to examine how AI affordances are translated into learning behaviors and intelligence-related processes in AI-mediated IDLE. Speech recognition, natural language processing, and personalized feedback were the most frequently investigated affordances, providing opportunities to observe, imitate, and adapt authentic language use. Engagement was mediated primarily by emotional, motivational, and self-belief mechanisms. These mechanisms could facilitate or hinder engagement depending on technological accuracy, perceived human responsiveness, learner goals, and proficiency-task alignment. From an intelligence perspective, the findings illustrate how learners evaluate AI-generated information, regulate affect and motivation, calibrate self-perceptions, and adapt learning behaviors. Thus, AI-mediated IDLE may serve as a site for activating adaptive intelligence and learner self-regulation through context-sensitive evaluation, regulation, and behavioral adaptation.