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Laird Malamed

dblp:364/0529 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 50% Usability and user experience research · 50%
Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Usability and user experience research
user study
0.712023
Exploring the Impact of User and System Factors on Human-AI Interactions in Head-Worn Displays · ISMAR 2023
Virtual and augmented reality › augmented reality
augmented reality assistance
0.212023
Exploring the Impact of User and System Factors on Human-AI Interactions in Head-Worn Displays · ISMAR 2023

Methods — techniques the papers use, named apart from their topics

user study · 1.3
YearPublicationVenuePosition
2023 Exploring the Impact of User and System Factors on Human-AI Interactions in Head-Worn Displays
abstract
Empowered by the rich sensory capabilities and the advancements in artificial intelligence (AI), head-worn displays (HWD) could understand the user’s contexts and provide just-in-time assistance to users’ tasks to augment their everyday lives. However, there has been limited understanding of how users perceive interacting with AI services, and how different factors impact the user experience in HWD applications. In this research, we investigated broadly what user and system factors play important roles in human-AI experiences during an AI-assisted spatial task. We conducted a user study to simulate an everyday scenario where augmented reality (AR) glasses could provide suggestions/assistance. We researched three AI system factors (performance, initiation, transparency) with multiple user factors (personality traits, trust propensity, and prior trust with AI). We not only identified the impact of user traits such as the levels of conscientiousness and prior trust with the AI, but also found interesting interactions between them and system factors such as AI’s performance and initiation strategy. Based on the findings, we suggest that future AI assistance on HWD needs to take users’ individual characteristics into account and customize the system design accordingly.
Feiyu Lu 0001, Xuhai Xu, Brennan Jones, Laird Malamed
ISMAR5