Exploring the usage demands of AIGC functions among Chinese researchers: A study based on the KANO model

Abstract

This study delves into the utilization demands of Artificial Intelligence-Generated Content (AIGC) tools among Chinese researchers, guided by the KANO model to understand their varying demands. By administering a comprehensive online survey (N = 1025), we collected data reflecting the researchers’ preferences for different AIGC functions. Our findings reveal a multifaceted perspective on user satisfaction: literature research emerged as a reverse quality, indicating a decline in satisfaction when provided, suggesting concerns over the authenticity of sources. Must-be qualities—data analysis and interpretation, statistical guidance, citation checks, and review response assistance—form the backbone of essential AIGC tools. Attractive qualities such as text writing, language services, charting assistance, and citation generation significantly boost user satisfaction, highlighting the AIGC's strength in content creation and formatting. Indifferent qualities, including concept clarification and viewpoint research, show a preference for personal research efforts, while diagram optimization and reference sorting are viewed as trivial tasks, comfortably managed with existing software tools. The study underscores the critical and discretionary AIGC functions from the perspective of Chinese academics, providing insights into tool development and indicating a need for future research on AIGC's evolving role in global research practices.