AI 기반 산학협력형 PBL을 통한 패션전공 학생의 지속가능성 학습 경험 연구

Abstract

This study aims to examine how fashion majors involved in an AI-based industry-academia collaborative problem-based learning (PBL) project develop competencies in sustainability and AI applications. The project, which utilized 8,700 inventory images from an online fashion platform, consisted of a joint industry-academia technology development initiative. In 2023, the focus was on classifying images based on TPO+Trend criteria, while 2024 involved attribute analysis using a Vision-Language Model (VLM), prompt design, and model validation. Seven fashion major students participated in the project over two consecutive years. Data collection methods included in-depth interviews, participant observation, and analysis of project deliverables, analyzed through Braun and Clarke's thematic analysis. This qualitative case study, centered on fashion majors from a single college, aimed to provide insights into the design and execution of AI-based industry-academia PBL projects. The findings highlighted several key points: First, learners recognized the potential for resource circulation and efficient reuse across industries through inventory data and AI, viewing sustainability as a tangible value. Second, the collaborative process with AI enhanced various skills, including data literacy, prompt design, model validation, self-directed learning, collaboration, and knowledge transfer. Third, learners began to see AI not just as a tool, but as a collaborative partner in developing classification criteria and interpretations. These findings offer valuable guidance and practical implications for educational design that integrates AI, sustainability, and industry-academia collaborative PBL in fashion education, while also providing foundational data for future research aimed at refining related instructional models.