"I Wrote, I Paused, I Rewrote" Teaching LLMs to Read Between the Lines of Student Writing
This addresses the need for more personalized and supportive feedback in educational tools for students, though it is incremental as it builds on existing LLM applications with new data integration.
The paper tackled the problem of LLMs missing context in student writing feedback by using writing process data from keystroke logging and snapshots, finding that learners preferred process-aware feedback and that certain edits correlated with higher coherence and elaboration scores.
Large language models(LLMs) like Gemini are becoming common tools for supporting student writing. But most of their feedback is based only on the final essay missing important context about how that text was written. In this paper, we explore whether using writing process data, collected through keystroke logging and periodic snapshots, can help LLMs give feedback that better reflects how learners think and revise while writing. We built a digital writing tool that captures both what students type and how their essays evolve over time. Twenty students used this tool to write timed essays, which were then evaluated in two ways: (i) LLM generated feedback using both the final essay and the full writing trace, and (ii) After the task, students completed surveys about how useful and relatable they found the feedback. Early results show that learners preferred the process-aware LLM feedback, finding it more in tune with their own thinking. We also found that certain types of edits, like adding new content or reorganizing paragraphs, aligned closely with higher scores in areas like coherence and elaboration. Our findings suggest that making LLMs more aware of the writing process can lead to feedback that feels more meaningful, personal, and supportive.