Integrating AI design tools into traditional design workflows
A study on collaborative tool usage willingness based on the Push-Pull-Mooring framework
Bibliographic Data
| ID | 9406765 |
|---|---|
| Authors | Hong Zou (0000-0002-0930-4444, Guangdong Polytechnic Normal University), Mangting He (Zhengzhou Business University), Zichuan Liu (0000-0002-5709-8497, Guangdong Polytechnic Normal University, corresponding author) |
| Year | 2026 |
| Volume | 263 |
| Pages | 106084-106084 |
| Publication date | 2026-01-12 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Acta Psychologica (JOURNAL) |
| Journal identifiers | ISSN: 0001-6918 • E-ISSN: 1873-6297 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.actpsy.2025.106084 |
| PMID | 41529355 |
| OpenAlex | W7122779959 |
| Language | EN |
| References cited | 13 |
In the context of rapid AI development, designers are increasingly integrating AI tools into traditional workflows, forming a collaborative rather than substitutive mode of tool usage. Grounded in the Push-Pull-Mooring (PPM) framework, this study investigates the key factors influencing designers' willingness for collaborative tool usage-defined as the intention to integrate AI tools while retaining traditional ones. Based on survey data from 404 professional designers analyzed through PLS-SEM, the results reveal that operational complexity and tool limitations intensify tool burnout, which in turn reduces collaborative willingness. Among pull factors, tool complementarity significantly enhances willingness, whereas perceived humanization-contrary to common assumptions-exerts a negative effect, indicating that excessive anthropomorphic features may undermine designers' sense of creative autonomy. Mooring factors such as tool trust, social influence, habit, and integration adaptability strongly promote collaboration between AI and traditional tools. These findings highlight the dual psychological burden of emotional fatigue and autonomy concerns in human-AI cooperation. The study extends the PPM framework beyond directional switching to explain multi-tool co-use behaviors and provides actionable insights for the design of AI-assisted tools that respect designers' creative agency while optimizing human-machine collaboration
Adaptability · Agency (philosophy · Autonomy · Complementarity (molecular biology · Context (archaeology · Key (lock · Design Education and Practice · Interactive and Immersive Displays · Usability and User Interface Design
| Highly cited | No |
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