"I Wrote, I Paused, I Rewrote" Teaching LLMs to Read Between the Lines of Student Writing
- URL: http://arxiv.org/abs/2506.08221v1
- Date: Mon, 09 Jun 2025 20:42:02 GMT
- Title: "I Wrote, I Paused, I Rewrote" Teaching LLMs to Read Between the Lines of Student Writing
- Authors: Samra Zafar, Shaheer Minhas, Syed Ali Hassan Zaidi, Arfa Naeem, Zahra Ali,
- Abstract summary: Large language models like Gemini are becoming common tools for supporting student writing.<n>Most of their feedback is based only on the final essay missing important context about how that text was written.<n>We built a digital writing tool that captures both what students type and how their essays evolve over time.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Help Me Write a Story: Evaluating LLMs' Ability to Generate Writing Feedback [57.200668979963694]
We present a novel test set of 1,300 stories that we corrupted to intentionally introduce writing issues.<n>We study the performance of commonly used LLMs in this task with both automatic and human evaluation metrics.
arXiv Detail & Related papers (2025-07-21T18:56:50Z) - LitLLMs, LLMs for Literature Review: Are we there yet? [15.785989492351684]
This paper explores the zero-shot abilities of recent Large Language Models in assisting with the writing of literature reviews based on an abstract.<n>For retrieval, we introduce a novel two-step search strategy that first uses an LLM to extract meaningful keywords from the abstract of a paper.<n>In the generation phase, we propose a two-step approach that first outlines a plan for the review and then executes steps in the plan to generate the actual review.
arXiv Detail & Related papers (2024-12-15T01:12:26Z) - Embracing AI in Education: Understanding the Surge in Large Language Model Use by Secondary Students [53.20318273452059]
Large language models (LLMs) like OpenAI's ChatGPT have opened up new avenues in education.<n>Despite school restrictions, our survey of over 300 middle and high school students revealed that a remarkable 70% of students have utilized LLMs.<n>We propose a few ideas to address such issues, including subject-specific models, personalized learning, and AI classrooms.
arXiv Detail & Related papers (2024-11-27T19:19:34Z) - Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs [49.18567856499736]
We investigate whether large language models (LLMs) can be supportive of open-ended dialogue tutoring.<n>We apply a range of knowledge tracing (KT) methods on the resulting labeled data to track student knowledge levels over an entire dialogue.<n>We conduct experiments on two tutoring dialogue datasets, and show that a novel yet simple LLM-based method, LLMKT, significantly outperforms existing KT methods in predicting student response correctness in dialogues.
arXiv Detail & Related papers (2024-09-24T22:31:39Z) - LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing [106.45895712717612]
Large language models (LLMs) have shown remarkable versatility in various generative tasks.
This study focuses on the topic of LLMs assist NLP Researchers.
To our knowledge, this is the first work to provide such a comprehensive analysis.
arXiv Detail & Related papers (2024-06-24T01:30:22Z) - Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation [13.854903594424876]
Large language models (LLMs) have demonstrated strong performance in generating coherent and contextually relevant text.
This work explores several prompting strategies for LLM-based zero-shot and few-shot generation of essay feedback.
Inspired by Chain-of-Thought prompting, we study how and to what extent automated essay scoring (AES) can benefit the quality of generated feedback.
arXiv Detail & Related papers (2024-04-24T12:48:06Z) - LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback [65.84061725174269]
Recent large language models (LLM) are leveraging human feedback to improve their generation quality.
We propose LLMRefine, an inference time optimization method to refine LLM's output.
We conduct experiments on three text generation tasks, including machine translation, long-form question answering (QA), and topical summarization.
LLMRefine consistently outperforms all baseline approaches, achieving improvements up to 1.7 MetricX points on translation tasks, 8.1 ROUGE-L on ASQA, 2.2 ROUGE-L on topical summarization.
arXiv Detail & Related papers (2023-11-15T19:52:11Z) - Creativity Support in the Age of Large Language Models: An Empirical
Study Involving Emerging Writers [33.3564201174124]
We investigate the utility of modern large language models in assisting professional writers via an empirical user study.
We find that while writers seek LLM's help across all three types of cognitive activities, they find LLMs more helpful in translation and reviewing.
arXiv Detail & Related papers (2023-09-22T01:49:36Z) - Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A
Preliminary Study on Writing Assistance [60.40541387785977]
Small foundational models can display remarkable proficiency in tackling diverse tasks when fine-tuned using instruction-driven data.
In this work, we investigate a practical problem setting where the primary focus is on one or a few particular tasks rather than general-purpose instruction following.
Experimental results show that fine-tuning LLaMA on writing instruction data significantly improves its ability on writing tasks.
arXiv Detail & Related papers (2023-05-22T16:56:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.