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<p>The integration of generative artificial intelligence into creative education offers novel opportunities to enhance ideation, yet structured pedagogical frameworks for its application remain sparse. This study evaluates an innovative, theory-informed AI-integrated design thinking model that positions generative AI as a cognitive collaborator to facilitate structured ideation and technical translation in fashion design. Employing a Design-Based Research approach, a four-hour intensive workshop titled “<i>From Prompt to Pattern</i>” was implemented with undergraduate fashion design students (<i>N</i> = 30) at a vocational institution. Data collection utilised a mixed-method approach comprising pre- and post-intervention artefact evaluations, a post-workshop questionnaire based on the Technology Acceptance Model, and qualitative student reflections. Quantitative results confirmed high technology acceptance across Perceived Usefulness and Perceived Ease of Use, with emotional engagement acting as the strongest predictor of future behavioural intention. Empirical artefact analysis revealed a significant qualitative transformation from conventional, linear pre-workshop concepts to structurally complex, culturally integrated post-workshop outputs. The study demonstrates that embedding generative AI within a scaffolded pedagogical framework enhances divergent thinking and creative self-efficacy without undermining human authorship, offering a scalable model for modern digital design curricula.</p>

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An AI integrated design thinking model for fashion design ideation in vocational education using a design based research approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月15日

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  • Marzie Hatef Jalil,
  • Johari Abdullah

摘要

The integration of generative artificial intelligence into creative education offers novel opportunities to enhance ideation, yet structured pedagogical frameworks for its application remain sparse. This study evaluates an innovative, theory-informed AI-integrated design thinking model that positions generative AI as a cognitive collaborator to facilitate structured ideation and technical translation in fashion design. Employing a Design-Based Research approach, a four-hour intensive workshop titled “From Prompt to Pattern” was implemented with undergraduate fashion design students (N = 30) at a vocational institution. Data collection utilised a mixed-method approach comprising pre- and post-intervention artefact evaluations, a post-workshop questionnaire based on the Technology Acceptance Model, and qualitative student reflections. Quantitative results confirmed high technology acceptance across Perceived Usefulness and Perceived Ease of Use, with emotional engagement acting as the strongest predictor of future behavioural intention. Empirical artefact analysis revealed a significant qualitative transformation from conventional, linear pre-workshop concepts to structurally complex, culturally integrated post-workshop outputs. The study demonstrates that embedding generative AI within a scaffolded pedagogical framework enhances divergent thinking and creative self-efficacy without undermining human authorship, offering a scalable model for modern digital design curricula.