arXiv cs.CL· Hunter McNichols, Fareya Ikram, Kai Du, Andrew Lan·· 12 小时前AI 评分26
Principal Trait Analysis：从人机协作对话中识别用户行为特征
Principal Trait Analysis: Identifying User Behavioral Traits in Human-AI Collaboration
AI 导读
研究者提出 Principal Trait Analysis（PTA），一种数据驱动方法，可从人机协作对话中提取可解释的行为特征，并依据这些特征为用户打分、筛选出覆盖用户间差异最大的特征集。该方法在两个人机协作编程数据集上评估，考察这些特征如何预测后续任务成功与学习效果，以及随时间如何演变。PTA 虽为无监督方法，但支持在特征识别阶段注入领域专业知识。
正文 · 原文
Abstract:Human-artificial intelligence (AI) collaboration takes many forms and occur in both professional and educational scenarios. It is important to identify how human users interact with AI and how their behavior affects task success and learning outcomes. Existing approaches often rely on predefined behavioral categories or derive patterns without establishing their relationship to outcomes. In this work, we propose Principal Trait Analysis (PTA), a data-driven method for identifying interpretable behavioral traits from human-AI collaboration dialogues. Our method analyzes large corpora of human-AI collaborative session traces, derive common traits across the dataset, scores each human collaborator on how well their interaction aligns with these traits, and selects a set that covers the most variance across users. While unsupervised and data-driven, our method also enables domain expertise to be injected during trait identification. We evaluate PTA on two human-AI collaborative coding datasets and investigate how the traits predict future task success, learning outcomes, and evolve over time.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.11460 [cs.CL] |
| (or arXiv:2608.11460v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.11460 arXiv-issued DOI via DataCite |
Submission history
From: Fareya Ikram [view email]
[v1]
Tue, 11 Aug 2026 21:49:35 UTC (2,903 KB)
[v2]
Wed, 4 Nov 2026 01:29:50 UTC (5,592 KB)
来源：arXiv cs.CL · arxiv.org