Research Progress in Artificial Intelligence-Driven Self-Regulatory Learning from a Subjective Perspective

Self-regulation learning is crucial for academic success and lifelong learning. However, how to avoid the over replacement of learners’ agency by artificial intelligence, and how to foster the ability of self-regulation learning through human-machine interaction, remains a key challenge in current research. This paper proposes three collaborative models based on the concept of agency, and provides a systematic overview of the current state of research on artificial intelligence in the field of self-regulation learning. The analysis shows that while scaffolding-based regulation can significantly improve efficiency, it may also lead to dependency and metacognitive laziness due to excessive substitution. Joint decision-making regulation can enhance metacognitive processes, but it faces risks of unclear responsibilities and increased workload. Tool-assisted regulation...