Delegators or Self-regulators? Exploring University Students’ Self-Regulated Learning with AI

From an educational perspective, the findings suggest that educators should focus less on how much students use AI and when they turn to AI, and more on how they use it. The contrast between delegative and self-regulated use underscores the need for explicit guidance: students should be supported in using AI for feedback, exploration, and reflection rather than as a shortcut to bypass learning. The challenge for Higher Education lies in guiding students toward self-regulated use, where AI acts as a learning scaffold, rather than allowing patterns of delegative use that risk undermining students’ critical and reflective engagement. As Lan and Zhou (2025) emphasised, maintaining human-centred SRL is essential and should remain the predominant form of SRL, especially when contrasted with AI-centred SRL. Practical steps could include designing tasks that require students to compare their own reasoning with AI outputs, or embedding reflection prompts whenever AI is used as study support, integrating AI literacy with SRL training, and helping students shift from delegative to self-regulated AI use. This approach will be critical to ensuring that AI contributes to learners’ agency, critical thinking, and long-term educational goals.