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Jin Yi Li

dblp:344/9343 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0003-4100-8327ORCID · reported

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

Human-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 · 77% Interaction techniques and input · 23%

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

TopicWeightPapersLastEvidence papers
Human-AI interaction
intelligent assistant
0.712023
Identifying Multimodal Context Awareness Requirements for Supporting User Interaction with Procedural Videos · CHI 2023
Interaction techniques and input
hands-free interaction
0.212023
Identifying Multimodal Context Awareness Requirements for Supporting User Interaction with Procedural Videos · CHI 2023

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

wizard-of-oz study · 0.7user study · 0.7
YearPublicationVenuePosition
2023 Identifying Multimodal Context Awareness Requirements for Supporting User Interaction with Procedural Videos
abstract
Following along how-to videos requires alternating focus between understanding procedural video instructions and performing them. Examining how to support these continuous context switches for the user has been largely unexplored. In this paper, we describe a user study with thirty participants who performed an hour-long cooking task while interacting with a wizard-of-oz hands-free interactive system that is aware of both their cooking progress and environment contexts. Through analysis of the session scripts, we identify a dichotomy between participant query differences and workflow alignment similarities, under-studied interactions that require AI functionality beyond video navigation alone, and queries that call for multimodal sensing of a user’s environment. By understanding the assistant experience through the participants’ interactions, we identify design implications for a smart assistant that can discern a user’s task completion flow and personal characteristics, accommodate requests within and external to the task domain, and support nonvoice-based queries.
Georgianna Lin, Jin Yi Li, Afsaneh Fazly, Vladimir Pavlovic 0001, Khai N. Truong
CHI2