From Prompting to Epistemic Proactivity: Temporal Trajectories of Student-AI Interaction in Mathematics Learning
In plain terms
This study explored how Grade-9 students interact with general-purpose AI tools, like ChatGPT, for learning mathematics in open-ended settings, aiming to understand what kind of interaction leads to better understanding. The researchers observed 112 students using an AI tutor for math practice, analyzing their conversations with the AI. They coded student messages for self-regulated learning behaviors, types of help requested, and engagement in mathematical problem-solving, specifically looking at how these patterns changed over time during a session. They found that simply summarizing a student's overall AI use, like the total number of questions, did not predict learning outcomes. However, students performed better when their interactions showed a temporal shift: moving towards more conceptual or procedural help-seeking and independent mathematical work in later stages, instead of primarily asking for answers or verification. This suggests that actively and strategically guiding the AI to advance one's understanding, termed "epistemic proactivity," is key for productive learning.