An AI-Driven Approach in Visual Communication Design at Huaiyin Institute of Technology, China
DOI:
https://doi.org/10.14456/au-ejir.2025.13Keywords:
AI adoption, task-technology fit, perceived AI competency, perceived intelligence, Visual Communication DesignAbstract
Purpose: This study explores the impact of an AI-driven instructional approach in a Visual Communication Design course at Huaiyin Institute of Technology, China, aiming to enhance creative design skills. It identifies key factors influencing AI adoption in education, including technology characteristics, task characteristics, task-technology fit, learners' perceived AI competency, and perceived intelligence. Research design, data, and methodology: The research involved 450 students, using a multi-step sampling method to ensure diversity. Content validity was confirmed using an Item Objective Congruence (IOC) Index, and reliability was established through a pilot test (n=50) and Cronbach’s Alpha. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were applied to analyze the data. Results: Technology characteristics significantly influenced task-technology fit, and task characteristics had a stronger impact on task-technology fit. Task-technology fit positively affected intention to use AI. Perceived learners’ AI competency and perceived intelligence both significantly influenced intention to use AI. Finally, intention to use AI had a strong effect on actual usage of AI. Conclusions: The findings of this study provide valuable insights into how AI-driven instructional approaches can boost students' creativity and engagement, assisting educational institutions in effectively integrating AI technologies into design courses. This research contributes to the development of pedagogical strategies that harness AI to foster innovation and creativity in design education.
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