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  4. Towards Effective AI-Driven Reading Assistants: A Design Science Exploration
 

Towards Effective AI-Driven Reading Assistants: A Design Science Exploration

URI
https://arbor.bfh.ch/handle/arbor/37197
Version
Published
Date Issued
2024-06
Author(s)
Martin, Hänel
Wambsganss, Thiemo  
Matthias, Söllner
Type
Conference Paper
Language
English
Abstract
Recent advancements in AI have led to the introduction of tools that support researchers in scientific reading. Tools such as SciSpace have come to the forefront to assist users in reading scientific texts. However, there is an insufficient theoretical foundation on how to design these reading assistants as well as no evidence of their effects, especially given the recent progress. Specifically, past literature lacks insights on evaluated user requirements and design principles for the design of computer-assisted reading systems. Addressing these challenges, we draw on Design Science Research (DSR) to derive and evaluate a set of five design principles for computer-assisted reading systems. Building on flow theory as our theoretical lens, we develop and perform a first proof-of-concept evaluation of a prototypical implementation of our principles as a computer-assisted reading artifact. Our design principles support researchers and practitioners on how to design, evaluate, and compare their AI-reading tools more effectively.
Subjects
T Technology (General)
DOI
10.24451/arbor.22096
https://doi.org/10.24451/arbor.22096
Publisher URL
https://aisel.aisnet.org/ecis2024/track23_designresearch/track23_designresearch/12/
Related URL
https://aisnet.org/page/ECISPage
Organization
Institut Digital Technology Management  
Wirtschaft  
Conference
ECIS 2024 - Design Research and Design Methods in Information Systems
Publisher
AIS Electronic Library
Citation apa
Martin, H., Wambsganss, T., & Matthias, S. (2024). Towards Effective AI-Driven Reading Assistants: A Design Science Exploration. ECIS 2024 - Design Research and Design Methods in Information Systems. AIS Electronic Library. https://doi.org/10.24451/arbor.22096
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