The advent of generative AI applications such as DALL·E, Midjourney, and Stable Diffusion is impacting the pedagogy, practice, and evaluation of drawing and painting. While these tools open new possibilities for ideation, prototype testing, and individualized feedback, they also pose challenges to pedagogical values that traditionally focused on observation, embodiment, and intentional craftsmanship. This study explores the gap created by the lack of a coherent, evidence-based pedagogical framework for incorporating AI in teaching drawing and painting. A hybrid methodology framework is utilized that combines systematic review, guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) (Page et al., 2021a), and Design Science Research (DSR). PRISMA informs a systematic review of recent literature, while DSR guides the development and evaluation of an artifact—a novel concept that emerges from this literature synthesis. The AI-Augmented Drawing and Painting Pedagogy (AADPP) model includes four intertwined components: foundational craft, AI-assisted ideation, critical and ethical reflection, and reflective studio practice. These concepts are built upon principles of constructivism, Technological Pedagogical Content Knowledge (TPACK), and AI literacy. The AADPP concept is evaluated through a six-criterion face validation, comparative analysis against similar studies, and a descriptive case study illustrating its implementation within a 12-week foundation drawing studio. The evaluation indicates that the proposed model is conceptually sound (complete, applicable, and current); this is illustrated by a detailed example of applying the model within a 12-week foundation drawing course. The current study does not include new empirical data; however, the case-study description serves as one means of evaluation. Limitations and directions for future empirical work are provided.
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