Zhibin Zhou
Biographic Data
| ID | 7594162 |
|---|---|
| NAME | Zhibin Zhou |
| GIVEN NAMES | Zhibin |
| FAMILY NAME | Zhou |
| SIGNATURE | ZHOU Z |
| AFFILIATIONS | International Design Institute, Zhejiang University, Hangzhou, China |
| ORCID | 0000-0001-9545-3763 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Assessment of artificial intelligence-generatedFrom manual to autonomous: A literature review of GenAI integration levels for design education
Generative AI (GenAI) is increasingly integrated into design education to boost creativity and productivity. However, the integration of GenAI into design workflows occurs at varying levels, ranging from manual to autonomous use. This variability remains insufficiently understood, raising concerns that some learners may over-rely on GenAI while others may fail to take full advantage of GenAI. To bridge this gap, this study reviews 116 peer-review…
Exploring the application of LLM-based AI in UX design: An empirical case study of ChatGPT
Study on the Changes in Immobilized Petroleum–Degrading Bacteria Beads in a Continuous Bioreactor Related to Physicochemical Performance, Degradation Ability, and Microbial Community
Continuous bioreactors for petroleum degradation and the effect factors of these bioreactors have rarely been mentioned in studies. In addition, indigenous bacteria living in seawater could influence the performance of continuous bioreactors with respect to petroleum degradation in practice. In this paper, a bioreactor fitted with immobilized petroleum-degrading bacteria beads was designed for further research. The results indicated that the dies…
ML Lifecycle Canvas: Designing Machine Learning-Empowered UX with Material Lifecycle Thinking
As a particular type of artificial intelligence technology, machine learning (ML) is widely used to empower user experience (UX). However, designers, especially the novice designers, struggle to integrate ML into familiar design activities because of its ever-changing and growable nature. This paper proposes a design method called Material Lifecycle Thinking (MLT) that considers ML as a design material with its own lifecycle. MLT encourages desig…
No prominent works on this page.
ML Lifecycle Canvas: Designing Machine Learning-Empowered UX with Material Lifecycle Thinking
As a particular type of artificial intelligence technology, machine learning (ML) is widely used to empower user experience (UX). However, designers, especially the novice designers, struggle to integrate ML into familiar design activities because of its ever-changing and growable nature. This paper proposes a design method called Material Lifecycle Thinking (MLT) that considers ML as a design material with its own lifecycle. MLT encourages desig…
Study on the Changes in Immobilized Petroleum–Degrading Bacteria Beads in a Continuous Bioreactor Related to Physicochemical Performance, Degradation Ability, and Microbial Community
Continuous bioreactors for petroleum degradation and the effect factors of these bioreactors have rarely been mentioned in studies. In addition, indigenous bacteria living in seawater could influence the performance of continuous bioreactors with respect to petroleum degradation in practice. In this paper, a bioreactor fitted with immobilized petroleum-degrading bacteria beads was designed for further research. The results indicated that the dies…
Exploring the application of LLM-based AI in UX design: An empirical case study of ChatGPT
Assessment of artificial intelligence-generatedFrom manual to autonomous: A literature review of GenAI integration levels for design education
Generative AI (GenAI) is increasingly integrated into design education to boost creativity and productivity. However, the integration of GenAI into design workflows occurs at varying levels, ranging from manual to autonomous use. This variability remains insufficiently understood, raising concerns that some learners may over-rely on GenAI while others may fail to take full advantage of GenAI. To bridge this gap, this study reviews 116 peer-review…
Computer Science (3 works) · Design Education and Practice (2 works) · Engineering (2 works) · Application lifecycle management (1 works) · Artificial Intelligence in Healthcare and Education (1 works) · Augmented Reality Applications (1 works) · Bacteria (1 works) · Biochemical engineering (1 works) · Biodegradation (1 works) · Biology (1 works)