AI Insight
This 14-day study of 260 Chinese design students found reciprocal relationships between daily generative AI use and design-learning self-efficacy, with each factor predicting increases in the other on subsequent instructional days. The research revealed that students experiencing greater baseline hassles showed stronger benefits to self-efficacy from AI use, suggesting contextual stressors influence how AI integration affects learning confidence. The effects were modest but statistically significant, indicating AI use and self-efficacy may gradually reinforce each other over time in design education contexts.
Why it matters
The findings suggest that integrating generative AI tools in design education may create positive feedback loops that enhance student confidence, particularly for those facing higher stress or challenges. This has implications for educators seeking to support struggling students and for understanding how AI tools become embedded in learning processes over time.
Understand the Science
Generative AI is increasingly embedded in design education, yet little is known about how students’ daily engagement with generative AI is dynamically associated with their learning-related capability beliefs across successive instructional days. Drawing on self-efficacy theory, this study examined the reciprocal next-instructional-day associations between daily generative AI use in design learning and daily design-learning self-efficacy, and further investigated whether baseline hassles were associated with individual differences in these dynamics. A 14-instructional-day diary study was conducted with 260 Chinese undergraduate design students, yielding 3,558 observed person-day assessments out of 3,640 possible assessments. Daily generative AI use and design-learning self-efficacy were assessed throughout the diary period, whereas hassles were measured at baseline. The data were analyzed using bivariate dynamic structural equation modeling within a Bayesian estimation framework. The results showed significant autoregressive effects for daily generative AI use (β = 0.198, 95% CrI [0.154, 0.237]) and daily design-learning self-efficacy (β = 0.138, 95% CrI [0.100, 0.175]). At the within-person level, higher-than-usual generative AI use on one instructional day positively predicted design-learning self-efficacy on the subsequent instructional day (β = 0.043, 95% CrI [0.006, 0.077]), while higher-than-usual design-learning self-efficacy also positively predicted generative AI use on the subsequent instructional day (β = 0.059, 95% CrI [0.023, 0.095]). At the between-person level, greater baseline hassles were associated with higher average generative AI use (β = 0.135), lower average design-learning self-efficacy (β = −0.254), and a stronger cross-lagged association from generative AI use to subsequent self-efficacy (β= 0.458). Gender did not significantly predict the main random effects. These findings indicate modest reciprocal next-instructional-day associations between generative AI use and design-learning self-efficacy, suggesting that the two may reinforce one another across instructional days. The stronger association from generative AI use to subsequent self-efficacy among students reporting greater baseline hassles further suggests that contextual demands may shape how generative AI becomes integrated into students’ day-to-day design learning.