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AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionals
Journal article   Peer reviewed

AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionals

Angela Mastrianni, Hope Twede, Aleksandra Sarcevic, Jeremiah Wander, Christina Austin-Tse, Scott Saponas, Heidi Rehm, Ashley Mae Conard and Amanda K. Hall
ACM transactions on interactive intelligent systems, v 15(4), 22
06 Aug 2025
DOI: https://doi.org/10.1145/3756326
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Applied computing Applied computing / Life and medical sciences Human-centered computing Human-centered computing / Collaborative and social computing Human-centered computing / Collaborative and social computing / Collaborative and social computing theory, concepts and paradigms Human-centered computing / Collaborative and social computing / Collaborative and social computing theory, concepts and paradigms / Computer supported cooperative work Human-centered computing / Collaborative and social computing / Empirical studies in collaborative and social computing Human-centered computing / Human computer interaction (HCI) Human-centered computing / Human computer interaction (HCI) / Empirical studies in HCI Human-centered computing / Human computer interaction (HCI) / HCI design and evaluation methods Human-centered computing / Human computer interaction (HCI) / HCI design and evaluation methods / User studies Social and professional topics Social and professional topics / Professional topics Social and professional topics / Professional topics / Computing and business
Generative AI has the potential to transform knowledge work, but further research is needed to understand how knowledge workers envision using and interacting with generative AI. We investigate the development of generative AI tools to support domain experts in knowledge work, examining task delegation and the design of human-AI interactions. Our research focused on designing a generative AI assistant to aid genetic professionals in analyzing whole genome sequences (WGS) and other clinical data for rare disease diagnosis. Through interviews with 17 genetics professionals, we identified current challenges in WGS analysis. We then conducted co-design sessions with six genetics professionals to determine tasks that could be supported by an AI assistant and considerations for designing interactions with the AI assistant. From our findings, we identified sensemaking as both a current challenge in WGS analysis and a process that could be supported by AI. We contribute an understanding of how domain experts envision interacting with generative AI in their knowledge work, a detailed empirical study of WGS analysis, and three design considerations for using generative AI to support domain experts in sensemaking during knowledge work.

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

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Collaboration types
Industry collaboration
Domestic collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
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