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Beyond usability: how AI tools shape design innovation ability among Chinese industrial design students
Xin Wu1,2,3, Youngcheng Xie4, Yunyi Hu2
1School of Architecture and Art, Central South University, Changsha, China.
Frontiers in Psychology
|April 29, 2026
Summary
Artificial intelligence (AI) quality dimensions significantly impact design innovation ability (DIA) through user satisfaction and intention to use. Information quality has a stronger effect than interaction quality, highlighting functional pathways in AI-assisted education.
Area of Science:
- Design Education
- Human-Computer Interaction
- Artificial Intelligence
Background:
- Widespread AI adoption in design education necessitates understanding its impact on student innovation.
- Existing models inadequately explain how AI features influence innovation via psychological processes.
Purpose of the Study:
- Examine how AI tool quality dimensions (interaction and information quality) influence design innovation ability (DIA).
- Investigate the mediating roles of satisfaction (SAT) and intention to use (INU) in the AI quality-DIA relationship.
Main Methods:
- Cross-sectional quantitative design with 1,016 Chinese industrial design students.
- Online survey data analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).
Main Results:
- Both interaction quality (IQT) and information quality (INQ) positively influence satisfaction (SAT).
- SAT, IQT, and INQ jointly affect intention to use (INU), which significantly predicts DIA.
- Satisfaction (SAT) partially mediates the relationship between quality dimensions and intention to use (INU).
Conclusions:
- Information quality has a stronger impact than interaction quality on DIA.
- Direct functional evaluation of AI tools is more influential than affective mediation, challenging traditional acceptance models.
- AI-assisted creative education involves dual mechanisms: direct functional and indirect emotional pathways.
