The Design Space of Recent AI-assisted Research Tools for Ideation, Sensemaking, and Scientific Creativity
Runlong Ye, Matthew Varona, Oliver Huang, Patrick Yung Kang Lee,, Michael Liut, Carolina Nobre (University of Toronto)

TL;DR
This paper analyzes recent AI-assisted research tools, highlighting their capabilities, design spaces, and providing recommendations to enhance user engagement and mitigate biases in scientific workflows.
Contribution
It offers a comprehensive survey of recent AI research tools, characterizes the emerging design space, and proposes four key design principles for future AI-assisted research systems.
Findings
Identifies two main tool categories: workflow mimicry and generative exploration.
Provides four design recommendations: user agency, thinking support, adaptability, transparency.
Highlights a shift towards AI as a co-creative partner in research.
Abstract
Generative AI (GenAI) tools are radically expanding the scope and capability of automation in knowledge work such as academic research. While promising for augmenting cognition and streamlining processes, AI-assisted research tools may also increase automation bias and hinder critical thinking. To examine recent developments, we surveyed publications from leading HCI venues over the past three years, closely analyzing thirteen tools to better understand the novel capabilities of these AI-assisted systems and the design spaces they enable: seven employing traditional AI or customized transformer-based approaches, and six integrating open-access large language models (LLMs). Our analysis characterizes the emerging design space, distinguishes between tools focused on workflow mimicry versus generative exploration, and yields four critical design recommendations to guide the development of…
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Taxonomy
TopicsBig Data and Business Intelligence
