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Integrating AI design tools into traditional design workflows: A study on collaborative tool usage willingness based
Hong Zou1, Mangting He2, Zichuan Liu1
1School of Fine Arts, Guangdong Polytechnic Normal University, Guangzhou, 510665, China.
Acta Psychologica
|January 13, 2026
Summary
Designers embrace AI tools collaboratively, not substitutively. Factors like complexity cause burnout, reducing willingness, while trust and adaptability foster AI-human collaboration in design workflows.
Area of Science:
- Human-Computer Interaction
- Design Studies
- Artificial Intelligence
Background:
- Rapid advancements in Artificial Intelligence (AI) are transforming professional design workflows.
- Designers are increasingly adopting a collaborative approach, integrating AI tools alongside traditional methods rather than replacing them.
- Understanding the factors influencing this collaborative tool usage is crucial for effective human-AI integration.
Purpose of the Study:
- To investigate the key determinants of designers' willingness to engage in collaborative tool usage with AI.
- To analyze the influence of push (complexity, limitations), pull (complementarity, humanization), and mooring (trust, social influence, habit, adaptability) factors on this willingness.
- To extend the Push-Pull-Mooring (PPM) framework to explain multi-tool co-use in design.
Main Methods:
- A quantitative study utilizing survey data from 404 professional designers.
- Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for data analysis.
- The study adopted the Push-Pull-Mooring (PPM) framework to model influencing factors.
Main Results:
- Operational complexity and tool limitations were found to increase tool burnout, negatively impacting collaborative willingness.
- Tool complementarity positively influenced willingness, while perceived humanization had a negative effect, potentially due to undermining creative autonomy.
- Mooring factors including tool trust, social influence, habit, and integration adaptability significantly promoted AI and traditional tool collaboration.
Conclusions:
- Human-AI cooperation in design faces psychological challenges, including emotional fatigue and concerns over creative autonomy.
- The PPM framework effectively explains multi-tool co-use, extending beyond simple tool replacement scenarios.
- Actionable insights are provided for designing AI tools that balance designer agency with optimized human-machine collaboration.
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