From Labor to Collaboration: A Methodological Experiment Using AI Agents to Augment Research Perspectives in Taiwan's Humanities and Social Sciences
arXiv:2602.17221v1 Announce Type: new 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 Claude.ai 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 Agent
arXiv:2602.17221v1 Announce Type: new 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 Claude.ai 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 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 operational process. This taxonomy reveals the irreplaceability of human judgment in research question formulation, theoretical interpretation, contextualized reasoning, and ethical reflection. Limitations including single-platform data, cross-sectional design, and AI reliability risks are acknowledged.
Executive Summary
The article 'From Labor to Collaboration: A Methodological Experiment Using AI Agents to Augment Research Perspectives in Taiwan's Humanities and Social Sciences' explores the integration of generative AI into humanities and social science research. The study introduces an AI Agent-based collaborative research workflow, validated through Taiwan's Claude.ai usage data. The proposed framework consists of a seven-stage modular workflow grounded in task modularization, human-AI division of labor, and verifiability. The study identifies three operational modes of human-AI collaboration and highlights the irreplaceable role of human judgment in research. While the study acknowledges limitations such as single-platform data and AI reliability risks, it offers a replicable framework for enhancing research methodologies in the humanities and social sciences.
Key Points
- ▸ Introduction of an AI Agent-based collaborative research workflow for humanities and social sciences.
- ▸ Validation of the methodology using Taiwan's Claude.ai usage data.
- ▸ Identification of three operational modes of human-AI collaboration.
- ▸ Highlighting the irreplaceable role of human judgment in research.
- ▸ Acknowledgment of limitations including single-platform data and AI reliability risks.
Merits
Innovative Methodology
The study proposes a novel AI Agent-based collaborative research workflow, which is a significant contribution to the field of humanities and social sciences.
Empirical Validation
The methodology is validated using real-world data from Taiwan's Claude.ai, providing empirical evidence for its feasibility and effectiveness.
Replicable Framework
The study offers a replicable framework that can be adopted by other researchers, enhancing the potential for broader application and impact.
Demerits
Single-Platform Data
The study relies on data from a single AI platform, which may limit the generalizability of the findings.
Cross-Sectional Design
The cross-sectional nature of the data may not capture the dynamic and evolving aspects of human-AI collaboration over time.
AI Reliability Risks
The study acknowledges the potential risks associated with AI reliability, which could affect the accuracy and validity of the research outcomes.
Expert Commentary
The study 'From Labor to Collaboration' presents a timely and innovative exploration of AI's role in humanities and social science research. The proposed AI Agent-based collaborative research workflow is a significant advancement, offering a structured approach to integrating AI into research processes. The empirical validation using Taiwan's Claude.ai data provides a robust foundation for the methodology, demonstrating its practical applicability. The identification of three operational modes of human-AI collaboration underscores the nuanced interplay between human judgment and AI capabilities, emphasizing the irreplaceable role of human researchers in critical aspects of research. However, the study's reliance on single-platform data and cross-sectional design limits the generalizability of the findings. Future research could address these limitations by incorporating data from multiple AI platforms and adopting a longitudinal study design. Additionally, the study's acknowledgment of AI reliability risks highlights the need for ongoing vigilance and ethical considerations in AI-driven research. Overall, this study contributes valuable insights to the evolving discourse on AI in research and sets a precedent for future methodological innovations.
Recommendations
- ✓ Future studies should incorporate data from multiple AI platforms to enhance the generalizability of findings.
- ✓ Longitudinal study designs should be considered to capture the dynamic aspects of human-AI collaboration over time.
