"Can You See Me Think?" Grounding LLM Feedback in Keystrokes and Revision Patterns
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
As large language models (LLMs) increasingly assist in evaluating student writing, researchers have begun questioning whether these models can be cognitively grounded, that is, whether they can attend not just to the final product, but to the process by which it was written. In this study, we explore how incorporating writing process data, specifically keylogs and time-stamped snapshots, affects the quality of LLM-generated feedback. We conduct an ablation study on 52 student essays comparing feedback generated with access to only the final essay (C1) and feedback that also incorporates keylogs and time-stamped snapshots (C2). While rubric scores changed minimally, C2 feedback demonstrated significantly improved structural evaluation and greater process-sensitive justification.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 0 of its 24 references that are in the map. First posted on arXiv 19 Aug 2025.
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