Zehui Yu
Biographic Data
| ID | 7390193 |
|---|---|
| NAME | Zehui Yu |
| GIVEN NAMES | Zehui |
| FAMILY NAME | Yu |
| SIGNATURE | YU Z |
| AFFILIATIONS | Shandong Women’s University |
| ORCID | 0009-0003-1728-7829 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
How government AI employees shape public participation perception
This study explores how government AI employees shape public participation perception and clarifies the psychological mechanisms behind this influence. Based on survey data from 470 users of Shenzhen's "Shen Xiao-i," this study integrates technological, emotional, and cultural perspectives to explain how individuals form participation judgments. Emotional appraisal plays a central role, as higher emotional appraisal significantly enhances public …
Exploring the impact of learner- and peer- help seeking strategies on cognitive load reduction and self-regulated learning skills enhancement
In this mixed-methods investigation, the primary objective was to appraise the effectiveness of cultivating both learner- and peer-help seeking strategies in ameliorating cognitive load and fortifying self-regulated learning among Chinese EFL learners. A cohort of 122 students was randomly enlisted for participation in the study, with data acquisition conducted through pre- and post-assessments, observational records, and semi-structured intervie…
The Unseen Targets of Hate
Machine learning (ML)-based content moderation tools are essential to keep online spaces free from hateful communication. Yet ML tools can only be as capable as the quality of the data they are trained on allows them. While there is increasing evidence that they underperform in detecting hateful communications directed towards specific identities and may discriminate against them, we know surprisingly little about the provenance of such bias. To …
A Systematic Review of the Effects of AI-Assisted Moderation on Individuals and Groups
This review paper provides a conceptualization of AI-assisted content moderation with various degrees of autonomy and summarizes experimental evidence for how different levels of automation in content moderation and related losses of autonomy affect individuals and groups. Our results show that current research predominantly focuses on individual level effects, necessitating a shift toward understanding the impact on groups. The study highlights …
No prominent works on this page.
A Systematic Review of the Effects of AI-Assisted Moderation on Individuals and Groups
This review paper provides a conceptualization of AI-assisted content moderation with various degrees of autonomy and summarizes experimental evidence for how different levels of automation in content moderation and related losses of autonomy affect individuals and groups. Our results show that current research predominantly focuses on individual level effects, necessitating a shift toward understanding the impact on groups. The study highlights …
Exploring the impact of learner- and peer- help seeking strategies on cognitive load reduction and self-regulated learning skills enhancement
In this mixed-methods investigation, the primary objective was to appraise the effectiveness of cultivating both learner- and peer-help seeking strategies in ameliorating cognitive load and fortifying self-regulated learning among Chinese EFL learners. A cohort of 122 students was randomly enlisted for participation in the study, with data acquisition conducted through pre- and post-assessments, observational records, and semi-structured intervie…
The Unseen Targets of Hate
Machine learning (ML)-based content moderation tools are essential to keep online spaces free from hateful communication. Yet ML tools can only be as capable as the quality of the data they are trained on allows them. While there is increasing evidence that they underperform in detecting hateful communications directed towards specific identities and may discriminate against them, we know surprisingly little about the provenance of such bias. To …
How government AI employees shape public participation perception
This study explores how government AI employees shape public participation perception and clarifies the psychological mechanisms behind this influence. Based on survey data from 470 users of Shenzhen's "Shen Xiao-i," this study integrates technological, emotional, and cultural perspectives to explain how individuals form participation judgments. Emotional appraisal plays a central role, as higher emotional appraisal significantly enhances public …
Cognition (2 works) · Artificial Intelligence (1 work) · Artificial Intelligence Applications (1 work) · Cognitive Load (1 work) · Cognitive psychology (1 work) · Collectivism (1 work) · Computer Science (1 work) · Educational technology (1 work) · Ethics and Social Impacts of AI (1 work) · FinTech, Crowdfunding, Digital Finance (1 work)