Research on the Construction and Optimization of Art and Design Curriculum System for AIGC Intelligent Context

ABSTRACT

Artificial Intelligence–Generated Content (AIGC) is transforming design education; however, existing curricula lack structured integration of human–AI collaboration within instructional processes. This study proposes a five‐stage AIGC‐integrated teaching model comprising cognition, thinking, collaboration, discrimination, and refinement to support student‐centered learning and systematic skill development. The model is implemented in undergraduate art and design programs in Henan Province and evaluated using a mixed‐methods research design. Quantitative data are collected through questionnaire surveys from 200 participants, while qualitative insights are obtained from semi‐structured interviews with 30 respondents. Descriptive statistics and thematic analysis are applied to assess learning outcomes and instructional effectiveness. Results indicate improved understanding of AI‐assisted design processes, enhanced human–AI collaboration, and strengthened evaluation and refinement skills. Higher positive responses are observed in collaboration (49.1%) and refinement (46.8%) stages. The proposed framework offers a scalable and structured approach for integrating AIGC into art and design curricula in intelligent learning contexts.