Self-Regulated Learning

This study examines how doctoral students collaborate with a GAI-assisted writing tool. Students engaging in iterative, interactive collaboration with the tool produced higher-quality writing, while those using it as a passive information source performed worse. Findings highlight the importance of active human-AI collaboration and suggest directions for designing educational strategies that enhance learning with AI.

The epistemic network analysis can detect learning and reasoning co-occurrences. High performers show more co-occurrences in reflection and higher-order reasoning. Task complexity has impact on students’ learning and reasoning co-occurrences. Intelligent tutoring systems should foster regulation and reasoning acquisition.

The nBrowser system is designed to support student teachers to regulate their learning while navigating the web and designing a lesson plan that implements technologies into the classroom. Through evaluating teachers' think-aloud protocols while using the computer-based learning environment, we identified emotions and self-regulated learning strategies as core elements in influencing teachers' instructional design performance.