Navigating an Evolving Landscape: Exploring Researchers' Interactions with AI-Enhanced Academic Search Systems

In an era of exponential scholarly output, researchers face mounting challenges navigating vast academic literature. Artificial intelligence (AI) is reshaping academic search systems through features such as semantic search, generative summaries, and citation-context tools. While these innovations enhance research efficiency, empirical understanding on how researchers engage with and perceive AI-integrated academic search systems (AISS) remains limited. This study investigates how graduate students and early-career researchers interact with AISS, examining how disciplinary background, domain and search expertise, and attitudes toward AI influence trust and information-seeking strategies. Using a mixed methods design that combines survey with contextual inquiry, the study provides both quantitative trends and in-depth behavioral insights. Preliminary findings reveal substantial variation in transparency across AISS, highlighting the need for responsible and human-centered system design. This work advances understanding of AI’s impact on scholarly information behavior and inform the design of trustworthy academic search tools.