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PURPOSE: This study examines the impact of AI-supported feedback on learning effectiveness and design quality in visual and illustration design education in China, and explores the moderating role of perceived usefulness of AI tools. METHOD: A quantitative, survey-based approach was used with data from 430 university students. Stratified random sampling and power analysis determined the sample size. Data were analysed using descriptive statistics and SEM via Smart PLS. FINDINGS: AI-supported feedback significantly enhances learning effectiveness and design quality. Perceived usefulness moderates these effects, with students’ recognition of AI’s value influencing outcomes. ORIGINALITY: The study provides empirical support for integrating AI feedback in design education, offering insights to improve pedagogy, engagement, and creative outcomes.

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Designing an AI-Supported Pedagogical Evaluation Model for Visual and Illustration Design in China

  • 会议论文

  • 发表日期：

    2026年

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  • Lin Yu,
  • Gening Liu,
  • Ziwei Yu

摘要

PURPOSE: This study examines the impact of AI-supported feedback on learning effectiveness and design quality in visual and illustration design education in China, and explores the moderating role of perceived usefulness of AI tools. METHOD: A quantitative, survey-based approach was used with data from 430 university students. Stratified random sampling and power analysis determined the sample size. Data were analysed using descriptive statistics and SEM via Smart PLS. FINDINGS: AI-supported feedback significantly enhances learning effectiveness and design quality. Perceived usefulness moderates these effects, with students’ recognition of AI’s value influencing outcomes. ORIGINALITY: The study provides empirical support for integrating AI feedback in design education, offering insights to improve pedagogy, engagement, and creative outcomes.