Orchestrating GenAI for Interdisciplinary Research

2026-09-24

AI Summary

This study addresses the tension between knowledge acquisition and verification in interdisciplinary research, alongside the unclear mechanisms underlying generative AI (GenAI) adoption. Through a first-of-its-kind longitudinal investigation combining semi-structured interviews and qualitative analysis, it examines how researchers orchestrate GenAI to bridge knowledge gaps while preserving cognitive agency. The findings reveal an “expertise paradox” and elucidate the specific operational patterns through which GenAI facilitates interdisciplinary inquiry. Furthermore, this work proposes design strategies centered on calibrated verification, cross-domain synthesis, and disciplinary norm adaptation. Collectively, these insights provide empirical foundations for developing adaptive GenAI systems that prioritize and augment expert capabilities.

Abstract

As researchers tackle interdisciplinary problems, they face the need to deepen expertise in primary areas while rapidly acquiring knowledge in secondary domains. Generative AI (GenAI) is increasingly positioned to meet this need, from general-purpose chat assistants to Deep Research tools marketed as autonomous research agents. Prior work has examined how researchers use GenAI to support single-discipline or general research tasks. However, we know little about the goals and GenAI practices in interdisciplinary research. We conducted a longitudinal study and semi-structured interviews with 15 interdisciplinary researchers to examine how interdisciplinary researchers actually orchestrate GenAI. Findings show that researchers leaned on GenAI to fill knowledge gaps while maintaining epistemic agency for novelty discovery. We also uncovered an expertise paradox: GenAI outputs were hardest to verify when most needed. Our empirical insights motivate GenAI designs that calibrate verification to researchers' expertise, nudge toward cross-domain synthesis, and adapt prompting and outputs to disciplinary conventions.

Problem

• Generative AI
• Interdisciplinary Research
• Epistemic Agency
• Expertise Paradox
• Knowledge Gaps

Innovation

• Generative AI
• Interdisciplinary Research
• Epistemic Agency
• Expertise Paradox
• Human-AI Interaction