大模型
arxiv.org ·
评估 LLM 生成的学生写作反馈中的反馈焦点与教学适应性
该 arXiv 预印本研究 LLM 生成的写作反馈是否体现专家教师的教学实践，重点考察反馈焦点与适应性。作者将 Narciss 分类法细化为七种反馈焦点类型，对三门大学写作课程中教师与 LLM 生成的反馈进行标注，并发布 FeedType 基准，涵盖 6 个 LLM 在 3 种提示策略下的反馈。结果显示，多数 LLM 能覆盖大部分反馈焦点类型，但其分布与教师反馈不一致，适应性水平参差不齐，且没有一个模型能匹配教师的适应行为。
AI推荐理由 arXiv 论文，作者与摘要明确，提出将 Narciss 分类法细化为七类反馈焦点，并发布 FeedType 基准（含 6 个 LLM、3 种提示策略下的教师与 LLM 反馈标注）。新信息在于对反馈焦点分布与教学适应性的系统评估，结论是多数 LLM 虽覆盖多数焦点类型，但分布与教师不一致，且无一能匹配教师的适应性行为。局限是仅覆盖三门大学写作课程，属预印本，未提供具体模型名称与量化细节。
72
正文
Computer Science > Computation and Language
arXiv:2609.28026 (cs)
[Submitted on 23 Sep 2026]
Title:Evaluating Feedback Focus and Pedagogical Adaptivity in LLM-Generated Feedback on Student Writing
Authors:Norah Almousa, Shayan Peyghambari Oskoui, Raquel Coelho, Gayle Rogers, Xiang Lorraine Li, Diane Litman
View a PDF of the paper titled Evaluating Feedback Focus and Pedagogical Adaptivity in LLM-Generated Feedback on Student Writing, by Norah Almousa and 5 other authors
View PDF
HTML (experimental)
Abstract:We investigate whether state-of-the-art large language models (LLMs) generate feedback that reflects the pedagogical practices of expert teachers in terms of feedback focus and adaptivity. Previous evaluation efforts have examined feedback characteristics, its impact on learning, and its target, yet the focus of feedback and its adaptivity remains largely overlooked. To bridge this gap, we adopt and refine Narciss's taxonomy into seven feedback focus types to annotate teacher and LLM-generated feedback across three university writing courses. We release FeedType, a benchmark containing annotated teacher and LLM feedback from six LLMs under three prompting strategies. We assess the coverage and distribution of feedback focus types, and examine whether LLMs adapt their feedback across draft stages and student performance levels as an expert instructor does. Our findings show that while most LLMs cover most feedback focus types, they fail to reflect teacher feedback distributions and show varying levels of adaptivity, with none matching the teachers' adaptive behavior. We believe FeedType will support future research on pedagogical alignment in LLM feedback generation.
Comments:
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as:
arXiv:2609.28026 [cs.CL]
(or
arXiv:2609.28026v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.28026
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Norah Almousa [view email]
[v1]
Wed, 23 Sep 2026 12:51:08 UTC (616 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Evaluating Feedback Focus and Pedagogical Adaptivity in LLM-Generated Feedback on Student Writing, by Norah Almousa and 5 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CL
new
|
recent
| 2026-09
Change to browse by:
cs
cs.AI
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
export BibTeX citation
Loading...
BibTeX formatted citation
×
loading...
Data provided by:
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? |
Disable MathJax (What is MathJax?)