A Pedagogical Framework for Generative AI Integration in Higher Education Studio Courses: Principles and Assessment

Abstract

Generative artificial intelligence is now entering higher education studio courses as a tool for visual research, ideation, prototyping, editing and reflective writing. This paper develops a pedagogical framework for integrating generative AI into studio learning without reducing artistic judgement or creative agency. It asks how AI tools can support, rather than replace, the interpretive, material and dialogic processes that define art and design education. The paper synthesises literature on studio pedagogy, AI in education, visual learning and assessment, and organises recurring pedagogical problems into four core findings: AI can extend ideation and critique when guided by teachers; AI makes visual literacy and cultural interpretation more important; assessment requires transparent process evidence; and authorship, originality, bias and representation must be addressed as classroom issues. The proposed framework is based on five principles: intention, transparency, transformation, critique and responsibility. It also introduces a process-evidence model that asks students to submit research notes, sketches, prompts, AI outputs, revisions and final reflection. The contribution of the paper is to translate general AI-education concerns into a studio-specific model for teaching, assessment and policy design.