Brent Winslow

Brent Winslow

Auto-generated profile, based on publicly available information

Brent Winslow

Google Research, Mountain View, USA

Research Focus & Metrics

Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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2 April 2014 in Nature
Verified
Journal Article
Closed access
PDF file
SOSeung Wook Oh
Seung Wook Oh
JHJulie A. Harris
Julie A. Harris
LNLydia Ng
Lydia Ng
BWBrent Winslow
Brent Winslow
NCNicholas Cain
Nicholas Cain
HZHongkui Zeng
Hongkui Zeng

2 January 2023 in International Journal of Human–Computer Interaction
Verified
Journal Article
Open access
OGOZLEM GARIBAY
OZLEM GARIBAY
BWBrent Winslow
Brent Winslow
SASalvatore Andolina
Salvatore Andolina
MAMargherita Antona
Margherita Antona
ABAnja Bodenschatz
Anja Bodenschatz
WXWei Xu
Wei Xu
Widespread adoption of artificial intelligence (AI) technologies is substantially affecting the human condition in ways that are not yet well understood. Negative unintended consequences abound including the perpetuation and exacerbation of societal inequalities and divisions via algorithmic decision making. We present six grand challenges for the scientific community to create AI technologies that are human-centered, that is, ethical, fair, and enhance the human condition. These grand challenges are the result of an international collaboration across academia, industry and government and represent the consensus views of a group of 26 experts in the field of human-centered artificial intelligence (HCAI). In essence, these challenges advocate for a human-centered approach to AI that (1) is centered in human well-being, (2) is designed responsibly, (3) respects privacy, (4) follows human-centered design principles, (5) is subject to appropriate governance and oversight, and (6) interacts with individuals while respecting human’s cognitive capacities. We hope that these challenges and their associated research directions serve as a call for action to conduct research and development in AI that serves as a force multiplier towards more fair, equitable and sustainable societies.

1 March 2010 in Biomaterials
Verified
Journal Article
Closed access
BWBrent D. Winslow
Brent D. Winslow
PTPatrick A. Tresco
Patrick A. Tresco

1 December 2010 in Biomaterials
Verified
Journal Article
Closed access
BWBrent D. Winslow
Brent D. Winslow
MCMichael B. Christensen
Michael B. Christensen
WYWen-Kuo Yang
Wen-Kuo Yang
FSFlorian Solzbacher
Florian Solzbacher
PTPatrick A. Tresco
Patrick A. Tresco

1 December 2010 in Biomaterials
Verified
Journal Article
Closed access
BWBrent D. Winslow
Brent D. Winslow
HSHui Shao
Hui Shao
RSRussell J. Stewart
Russell J. Stewart
PTPatrick A. Tresco
Patrick A. Tresco

2 July 2019 in Interactive Learning Environments
Verified
Journal Article
Closed access
MCMeredith Carroll
Meredith Carroll
SLSummer Lindsey
Summer Lindsey
MCMaria Chaparro
Maria Chaparro
BWBrent Winslow
Brent Winslow
Mobile technology’s ubiquity has caused a shift in how students learn and how pedagogy can take place. Educators have the potential to engage students more fully in the modern educational environment, by making learning content and interactions accessible via mobile devices, both within and outside of the classroom. There is a need to leverage the learning science literature on learner engagement to identify the most effective means of utilizing mobile and online technology to engage learners. Utilizing foundational engagement theory, this paper presents an Applied Model of Learner Engagement, including the individual, task and environmental factors that influence how likely a student is to become engaged in learning content. Based on this model, we present instructional interventions that educational practitioners can utilize to more effectively engage learners, as well as best practice guidance for achieving this, with mobile and online learning technology, in the modern educational environment.