Pedagogy and Professional Development

Exploring Learning Experiences and Effectiveness in Generative AI-Integrated Fashion Design: A Connectivism-Based Learning Approach

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Abstract

The rapid integration of generative artificial intelligence (GenAI) into fashion design (FD) practice necessitates pedagogical approaches that support continuous, self-directed learning. Grounded in Connectivism learning theory, this study examines undergraduate FD students' learning experiences and perceived effectiveness within a GenAI-integrated learning environment. Using a case study approach, students participated in a GenAI tool exploration workshop followed by an individual design project applying AI-driven processes. Post-participation survey data (n=31) included open-ended questions and scaled measures assessing eight connectivity learning principles. Thematic analysis and descriptive statistical analysis revealed that students perceived connectivism as highly relevant to GenAI-integrated FD, particularly the principle of building connections for continual learning. Key drivers included enhanced ideation and visualization, while barriers centered on difficulty achieving designed outcomes. Findings demonstrated that connectivism provides a robust theoretical framework for understanding how students engage with GenAI and offers guidance for developing AI-integrated curricula that enhance learning effectiveness and career readiness.

Keywords: Generative Artificial Intelligence (GenAI), Fashion Design Education, Connectivism Learning Theory, Digital Pedagogy, AI-Integrated Design Processes, Student Learning Experiences, Learning Effectiveness, Higher Education Pedagogy

How to Cite:

Bernardoni, J. M., Zhang, Y. & Liu, C., (2025) “Exploring Learning Experiences and Effectiveness in Generative AI-Integrated Fashion Design: A Connectivism-Based Learning Approach”, International Textile and Apparel Association Annual Conference Proceedings 82(1). doi: https://doi.org/10.31274/itaa.21925