The effectiveness of Smart Compose: An artificial intelligent system

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Authors
Gnacek, Matt
Doran, Eric
Bommer, Sharon
Appiah-Kubi, Philip
Advisors
Issue Date
2020-06
Type
Article
Keywords
Machine learning , Workloads , Artificial intelligence , Manufacturing , Additive manufacturing , Smart Compose , Human performance , College students
Research Projects
Organizational Units
Journal Issue
Citation
Gnacek, M., Doran, E., Bommer, S., & Appiah-Kubi, P. (2020). The effectiveness of Smart Compose: An artificial intelligent system. Journal of Management & Engineering Integration, 13(1), 111-121. https://doi.org/10.62704/10057/24751
Abstract

The growth of artificial intelligence (AI) technologies in everyday life and manufacturing are expected to reduce the mental workload of a user or human operator and increase their efficiency. In industrial systems, such as additive manufacturing (AM), as AM transitions from a technology of manufacturing prototypes to rapid manufacturing, it is important that these added technologies reduce an operator's mental workload, have high user satisfaction, and are easily implemented and incorporated into the operator's tasks. One growing AI technology is Smart Compose, an artificially intelligent system that provides writing suggestions when composing an email through Gmail. Like other AI technologies, the goal of Smart Compose is to enhance the performance of a user; in this case, typing an email. It is hypothesized that Smart Compose increases a user's performance when typing an email. Thus, the objective of this study is to test the capability of Smart Compose and whether it increases human performance and decreases mental workload for college students. The study found that Smart Compose does not significantly increase human performance and does not significantly decrease mental workload for college students.

Table of Contents
Description
Published in SOAR: Shocker Open Access Repository by Wichita State University Libraries Technical Services, November 2022.
Publisher
Association for Industry, Engineering and Management Systems (AIEMS)
Journal
Book Title
Series
Journal of Management & Engineering Integration
v.13 no.1
PubMed ID
ISSN
1939-7984
EISSN