Supporting students’ self-regulated learning in online learning using artificial intelligence applications

Abstract

Abstract Self-regulated learning (SRL) is crucial for helping students attain high academic performance and achieve their learning objectives in the online learning context. However, learners often face challenges in properly applying SRL in online learning environments. Recent developments in artificial intelligence (AI) applications have shown promise in supporting learners’ self-regulation in online learning by measuring and augmenting SRL, but research in this area is still in its early stages. The purpose of this study is to explore students’ perceptions of the use of AI applications to support SRL and to identify the pedagogical and psychological aspects that they perceive as necessary for effective utilization of those AI applications. To explore this, a speed dating method using storyboards was employed as an exploratory design method. The study involved the development of 10 AI application storyboards to identify the phases and areas of SRL, and semi-structured interviews were conducted with 16 university students from various majors. The results indicated that learners perceived AI applications as useful for supporting metacognitive, cognitive, and behavioral regulation across different SRL areas, but not for regulating motivation. Next, regarding the use of AI applications to support SRL, learners requested consideration of three pedagogical and psychological aspects: learner identity, learner activeness, and learner position. The findings of this study offer practical implications for the design of AI applications in online learning, with the aim of supporting students’ SRL.

Keywords

• Self-regulated learning
• Artificial intelligence
• Online learning
• Student perception