AI in design education: Factors affecting students' professional learning adaptability

Area of Science:

• Educational Technology
• Artificial Intelligence in Education
• Design Education

Background:

• The design education ecosystem is rapidly evolving with AI integration.
• A critical challenge is the insufficient adaptability of design students to AI-enhanced learning.

Purpose of the Study:

• To examine factors influencing design students' adaptability to AI-supported learning.
• To identify mechanisms that impact this adaptability.
• To propose strategies for enhancing student readiness in AI-driven design education.

Main Methods:

• A survey questionnaire was administered to 784 design students.
• Structural equation modeling was employed for empirical analysis.

Main Results:

• Overall professional learning adaptability was found to be moderately high.
• Five key factors positively influence adaptability: motivation/goals, self-efficacy, teacher support, resources/environment, and intelligent literacy.
• Motivation, goals, and self-efficacy had the most significant direct impact.
• Teacher support indirectly enhances adaptability by boosting motivation and self-efficacy.

Conclusions:

• A novel five-factor model for AI-supported design education adaptability was validated.
• Findings provide empirical support for universities to improve student adaptability through curriculum and intervention strategies.
• Recommendations include synergistic stimulation of motivation/efficacy, customized interventions, and enhancing the resource-literacy continuum.