Applying Natural Language Processing Adaptive Dialogs to Promote Knowledge Integration During Instruction
Weiying Li · Education Sciences · 2025
We explored the value of adding NLP adaptive dialogs to a web-based, inquiry unit on photosynthesis and cellular respiration designed following the Knowledge Integration (KI) framework. The unit was taught by one science teacher in seventh grade middle school classrooms with 162 students. We measured students’ integrated understanding at three time points across instruction using KI scores. Students received significantly higher KI scores after the dialog and with instruction. We found that students who had complete engagement with the dialogs at three time points during instruction received higher KI scores than those who had inconsistent engagement with the dialog across instruction. By investigating the idea progression among students with full engagement with the dialogs, we found significant improvements in KI scores in revised explanations after the dialog at three instruction time points, with significant interaction with the dialog and instruction facilitating a shift toward more KI links. Two rounds of guidance in the dialog elicited more ideas. Students were more likely to add mechanistic ideas of photosynthesis reactants and cellular respiration after the dialog, especially during and after instruction. Case analyses highlight how adaptive dialogs helped one student refine and integrate scientific mechanisms at three time points. These findings demonstrate the potential of combining NLP adaptive dialogs with instruction to foster deeper scientific reasoning.