Enhancing Research Productivity with Next-Generation AI Tools

Enhancing Research Productivity with Next-Generation AI Tools

Information is being generated more rapidly and in larger volumes than ever before, challenging researchers to manage an overwhelming influx of data—from search results and journal articles to countless PDFs. Sifting through this information to find credible, relevant, and useful insights can be time-consuming and overwhelming. At the same time, generative AI tools are quickly transforming higher education, impacting various aspects of teaching, learning, and administration. For example, the University of South Florida has adopted Microsoft Copilot, which works with its Microsoft 365 system to enhance functions such as summarizing meetings in Microsoft Teams and supporting research projects (Microsoft, 2024). Similarly, Rice University is exploring the use of Google Gemini within its Google Workspace, potentially increasing access to educational resources through partnerships like OpenStax (Baraniuk, 2024). As the adoption of next-generation AI tools speeds up, individual researchers are increasingly examining how generative AI can support their research tasks. With many tools now available, each offering different capabilities, the question remains: which tools genuinely aid research, especially in managing data and organizing information for literature reviews? This poster explores a variety of generative AI tools, providing practical examples of how they can help users manage information overload, streamline repetitive tasks, and turn complex content into structured insights for literature reviews, research proposals, and grant applications. The aim is to give a snapshot of how these AI tools could assist researchers in responsibly incorporating AI into their workflows.

Sheu, Feng-Ru

Chinese American Librarians Association

2025-06-29

application/pdf

eng

Poster

Paper; Digital