This study examines group differences in AI dependence, self-efficacy, and design fixation based on learners’ AI utilization experience, and further explores the relationships between AI dependence and perception-related variables within an AI-based design education context. To this end, I surveyed 42 learners who participated in an AI-based design class. I then performed data analysis in IBM SPSS Statistics 26, using one-way analysis of variance (ANOVA) and Pearson correlation. The ANOVA revealed statistically significant group differences in AI dependence by AI usage level, but no significant differences in self-efficacy or design fixation. Furthermore, age and professional experience showed no significant influence on most variables. Meanwhile, the correlation analysis revealed that AI dependence was significantly positively associated with design fixation, but not with self-efficacy. These results suggest that, in AI-based design education, learners’ perceptions and attitudes are not strongly differentiated by personal background factors such as age or professional experience, but are instead associated with their experience with AI use and perceived dependence on AI. By distinguishing and analyzing AI utilization and AI dependence, this study provides empirical evidence that contributes to a more nuanced understanding of learners’ cognitive perceptions in AI-based design education in practice.

The purpose of this study is to develop and implement a customized AI-based speaking diagnosis, learning, and assessment system, SpeakMaster, in order to overcome the lack of systematic evaluation and practice opportunities in school English speaking class. This system integrates automated speaking scoring to provide students with feedback on their speaking abilities across pronunciation, conversation, and presentation. This study adopts a design-based research methodology, demonstrating the development and implementation process. 1,451 students and eight teachers in elementary, middle, and high schools participated in the experiment. Data were collected through learning logs, teacher journals, interviews, and post-surveys. The findings indicate that the system design is appropriate for English class, promoting students’ flow in engaging speaking practice. Students showed motivation and satisfaction while teachers found the system valuable for monitoring student progress and facilitating speaking assessments. Despite the challenges of improving chatbot performance and enhancing scoring reliability, the results suggest that SpeakMaster shows potential to enhance English speaking education.