![[2602.17221] From Labor to Collaboration: A Methodological Experiment Using AI Agents to Augment Research Perspectives in Taiwan's Humanities and Social Sciences](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.17221] From Labor to Collaboration: A Methodological Experiment Using AI Agents to Augment Research Perspectives in Taiwan's Humanities and Social Sciences
Summary
This article explores a methodological experiment using AI agents to enhance research in Taiwan's humanities and social sciences, proposing a collaborative framework for researchers.
Why It Matters
As generative AI transforms knowledge work, this study addresses the gap in methodological exploration within the humanities and social sciences. By proposing a structured AI collaboration framework, it opens new avenues for research methodologies and highlights the importance of human judgment in AI-assisted research.
Key Takeaways
- The study introduces an AI Agent-based collaborative research workflow for humanities and social sciences.
- It emphasizes the irreplaceability of human judgment in formulating research questions and ethical considerations.
- The proposed framework includes a seven-stage modular workflow that delineates roles for both AI and human researchers.
Computer Science > Artificial Intelligence arXiv:2602.17221 (cs) [Submitted on 19 Feb 2026] Title:From Labor to Collaboration: A Methodological Experiment Using AI Agents to Augment Research Perspectives in Taiwan's Humanities and Social Sciences Authors:Yi-Chih Huang View a PDF of the paper titled From Labor to Collaboration: A Methodological Experiment Using AI Agents to Augment Research Perspectives in Taiwan's Humanities and Social Sciences, by Yi-Chih Huang View PDF Abstract:Generative AI is reshaping knowledge work, yet existing research focuses predominantly on software engineering and the natural sciences, with limited methodological exploration for the humanities and social sciences. Positioned as a "methodological experiment," this study proposes an AI Agent-based collaborative research workflow (Agentic Workflow) for humanities and social science research. Taiwan's this http URL usage data (N = 7,729 conversations, November 2025) from the Anthropic Economic Index (AEI) serves as the empirical vehicle for validating the feasibility of this methodology. This study operates on two levels: the primary level is the design and validation of a methodological framework - a seven-stage modular workflow grounded in three principles: task modularization, human-AI division of labor, and verifiability, with each stage delineating clear roles for human researchers (research judgment and ethical decisions) and AI Agents (information retrieval and text generation); the secondar...