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Qinyi Zhou

dblp:349/2159 · DBLP profile ↗
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3ranked-venue papers
1as first author
3since 2021 · last 2026
0009-0000-1878-3646ORCID · corroborated

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

Human-computer interaction and ubiquitous computing · 3 · 1 first-author · 3 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
2 papers
Human-AI interaction · 59% Design research and methods · 41%
Network and information security
1 paper
Web and mobile security · 100%
Software engineering, system software, and programming languages
1 paper
Empirical software engineering · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Design research and methods › design process
design ideation
0.912025
ProductMeta: An Interactive System for Metaphorical Product Design Ideation with Multimodal Large Language Models · CHI 2025
Web and mobile security › harmful content detection
misinformation detection
0.312026
Behavioral Indicators of Overreliance During Interaction with Conversational Language Models · CHI 2026
Empirical software engineering › software analytics
interaction log analysis
0.312026
Behavioral Indicators of Overreliance During Interaction with Conversational Language Models · CHI 2026
Human-AI interaction › AI-assisted creativity
AI-assisted design ideation
0.312025
ProductMeta: An Interactive System for Metaphorical Product Design Ideation with Multimodal Large Language Models · CHI 2025

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

semantic encoding · 3.0clustering · 3.0multimodal large language model · 0.9
YearPublicationVenuePosition
2026 Behavioral Indicators of Overreliance During Interaction with Conversational Language Models
abstract
LLMs are now embedded in a wide range of everyday scenarios. However, their inherent hallucinations risk hiding misinformation in fluent responses, raising concerns about overreliance on AI. Detecting overreliance is challenging, as it often arises in complex, dynamic contexts and cannot be easily captured by post-hoc task outcomes. In this work, we aim to investigate how users’ behavioral patterns correlate with overreliance. We collected interaction logs from 77 participants working with an LLM injected plausible misinformation across three real-world tasks and we assessed overreliance by whether participants detected and corrected these errors. By semantically encoding and clustering segments of user interactions, we identified five behavioral patterns linked to overreliance: users with low overreliance show careful task comprehension and fine-grained navigation; users with high overreliance show frequent copy-paste, skipping initial comprehension, repeated LLM references, coarse locating, and accepting misinformation despite hesitation. We discuss design implications for mitigation.
Chang Liu 0150, Qinyi Zhou, Xinjie Shen, Xingyu Liu 0002, Sherry Tongshuang Wu, Xiang 'Anthony' Chen
CHI2
2026 CoLyricist: Enhancing Lyric Writing with AI through Workflow-Aligned Support
abstract
We propose CoLyricist, an AI-assisted lyric writing tool designed to support the typical workflows of experienced lyricists and enhance their creative efficiency. While lyricists have unique processes, many follow common stages. Tools that fail to accommodate these stages challenge integration into creative practices. Existing research and tools lack sufficient understanding of these songwriting stages and their associated challenges, resulting in ineffective designs. Through a formative study involving semi-structured interviews with 10 experienced lyricists, we identified four key stages: Theme Setting, Ideation, Drafting Lyrics, and Melody Fitting. CoLyricist addresses these needs by incorporating tailored AI-driven support for each stage, optimizing the lyric writing process to be more seamless and efficient. To examine whether this workflow-aligned design also benefits those without prior experience, we conducted a user study with 16 participants, including both experienced and novice lyricists. Results showed that CoLyricist enhances the songwriting experience across skill levels. Novice users especially appreciated the Melody-Fitting feature, while experienced users valued the Ideation support.
Masahiro Yoshida, Songyan Zhao, Qinyi Zhou, Shiwei Hu, Xiang 'Anthony' Chen, Nanyun Peng 0001
IUI4
2025 ProductMeta: An Interactive System for Metaphorical Product Design Ideation with Multimodal Large Language Models
Qinyi Zhou, Jie Deng 0001, Yun Wang 0012, Zhicong Lu, Scarlett Li, Ying-Qing Xu
CHI1