From Labor to Collaboration: A Methodological Experiment Using AI Agents to Augment Research Perspectives in Taiwan's Humanities and Social Sciences
#Generative AI#AI Agents#Humanities research#Social Sciences research#Taiwan research data#AEI Taiwan#Collaborative workflow#Task modularization#Human‑AI division of labor#Verifiability#Ethical reflection#Methodological experiment
📌 Key Takeaways
The article proposes a seven‑stage modular workflow grounded in task modularization, a human–AI division of labor, and verifiability.
It validates this methodology using real‑world AEI Taiwan data (7,729 conversations) collected in November 2025.
The workflow delineates distinct roles: researchers handle research judgment and ethics; AI agents conduct information retrieval and text generation.
The study identifies three operational modes—direct execution, iterative refinement, and human‑led—to classify human‑AI collaboration practices.
It concludes that human judgment remains irreplaceable in key research activities, even with advanced generative AI tools.
Limitations such as reliance on a single platform, cross‑sectional design, and AI reliability issues are acknowledged.
📖 Full Retelling
WHO: Yi-Chih Huang; WHAT: Introduces an AI agent–based collaborative research workflow—called the Agentic Workflow—to augment research methods in humanities and social sciences; WHERE: Taiwan, using AEI Taiwan data of 7,729 conversational records collected in November 2025; WHEN: The study was submitted to arXiv on 19 February 2026; WHY: To fill a methodological gap by demonstrating how generative AI can be systematically integrated into humanities and social science research, while highlighting the indispensable role of human judgment in question formulation, theoretical interpretation, contextual reasoning, and ethical reflection.
🏷️ Themes
AI‑assisted research methods, Human‑AI collaboration, Methodological design in humanities and social sciences, Verification and reproducibility
Entity Intersection Graph
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Deep Analysis
Why It Matters
This study shows how AI agents can be integrated into humanities research, moving beyond engineering and natural science applications. It offers a structured workflow that preserves human judgment while leveraging AI for data handling.
Context & Background
Generative AI is increasingly used in knowledge work
Existing research on AI methods mainly targets engineering and natural sciences
Taiwan's humanities scholars have limited methodological guidance for AI collaboration
What Happens Next
Future work may test the workflow across multiple platforms and longitudinal designs, and refine guidelines for ethical AI use in social science research.
Frequently Asked Questions
What is the Agentic Workflow?
A seven-stage modular process that divides tasks between human researchers and AI agents, emphasizing task modularization, human-AI division of labor, and verifiability.
How was the workflow validated?
Using 7,729 conversations from the Anthropic Economic Index in Taiwan as empirical data, demonstrating feasibility and output quality.
What are the operational modes of collaboration identified?
Direct execution, iterative refinement, and human-led modes, each reflecting different balances of human and AI responsibilities.
What limitations did the study acknowledge?
Single-platform data, cross-sectional design, and potential AI reliability risks.
Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.17221 [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 7,729 conversations, November 2025) from the Anthropic Economic Index 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 secondary level is the empirical analysis of AEI Taiwan data - serving as an operational demonstration of the workflow's application to secondary data research, showcasing both the process and output quality (see Appendix A). This study contributes by proposing a replicable AI collaboration framework for humanities and social science researchers, and identifying three operational modes of human-AI collaboration - direct execution, iterative refinement, and human-led - through reflexive documentation of the operatio...
