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AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionals
IACAPAP ArXiv (Online)
19 Dec 2024
Abstract
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.
CCS CONCEPTS: Human-centered computing, Human-computer interaction, Empirical
studies in HCI
Additional Keywords and Phrases: whole genome sequencing, generative AI,
large language models, knowledge work, sensemaking, co-design, rare disease
Contact Author: Angela Mastrianni (This work was done during the author's
internship at Microsoft Research)
Ashley Mae Conard and Amanda K. Hall contributed equally
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Details
- Title
- AI-Enhanced Sensemaking: Exploring the Design of a Generative AI-Based Assistant to Support Genetic Professionals
- Creators
- Angela MastrianniHope TwedeAleksandra SarcevicJeremiah WanderChristina Austin-TseScott SaponasHeidi RehmAshley Mae ConardAmanda K Hall
- Publication Details
- IACAPAP ArXiv (Online)
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science
- Other Identifier
- 991022006297404721