A Dual-Loop, Four-Core, Three-Stage Pedagogical Model for AIGC-Enhanced Advertising Education: A Conceptual Framework

Abstract

The rapid evolution of Artificial Intelligence Generated Content (AIGC) is reshaping advertising workflows and altering professional competency requirements. Traditional advertising education often lags behind these changes, characterized by outdated course content, insufficient AIGC expertise among instructors, and assessment methods that rely excessively on final examinations. In response, this study develops a "Dual-Loop, Four-Core Competencies, Three-Stage Progression" teaching model tailored for Advertising Appreciation and Design courses. Drawing on constructivist learning theory and human–AI collaboration principles, the model features an inner "Generation–Reflection" loop to cultivate critical judgment in AI-assisted creation and an outer "Teaching–Practice–Evaluation" loop to facilitate iterative curriculum refinement. It embeds four core competencies-data literacy, design thinking, digital tooling, and domain ethics-into three progressive learning stages: cognitive foundation, applied practice, and critical innovation. The model’s evaluation framework combines formative (40%) and summative (60%) assessments, with the summative assessment's weighting system structured by the Analytic Hierarchy Process (AHP). This approach offers a theoretically grounded and practice-oriented framework for aligning advertising education with the evolving demands of the AIGC era.

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