RETHINKING LEARNING IN THE AI ERA: THE AIAS FRAMEWORK FOR REFLECTIVE HIGHER EDUCATION - Dong Shen (PUPIL: International Journal of Teaching, Education and Learning, 2026)
The rapid growth of generative Artificial Intelligence (AI) is reshaping how higher education conceptualizes learning, assessment, and pedagogy. Many institutions respond by relying on restrictive policies. Unfortunately, this approach often fails to support meaningful and sustainable education. The objective of this study is to reinterpret the Artificial Intelligence Assessment Scale (AIAS) (Perkins et al., 2024) as a developmental pedagogical framework that enables transparent, ethical, and reflective integration of AI into teaching and learning. Methodologically, the study applies the five-level AIAS model, ranging from AI prohibition to full AI collaboration, in FASH 137: Clothing, Society, and Culture, a General Education course examining the sociocultural meanings of dress. AI integration is scaffolded across multiple assignments, each explicitly aligned with a designated AIAS level. Data are drawn from assignment design, faculty observations, and structured student reflections documenting AI use and learning outcomes. Findings reveal three interrelated pedagogical themes. First, transparency as pedagogical integrity emerges through required “AI Use Notes,” which normalize disclosure and foster academic trust. Second, critical evaluation as human distinction is strengthened as students compare AI-generated insights with their own analyses, reinforcing judgment, creativity, and cultural interpretation. Third, AI as a structured learning partner supports exploration, critique, writing development, and identity reflection without replacing human authorship. The research outcomes demonstrate that AIAS functions effectively as a learning architecture, aligning academic integrity with instructional design. The framework offers a replicable model for fashion programs and other disciplines seeking responsible AI integration. Future research will expand empirical assessment across courses, disciplines, and institutions, examine longitudinal learning impacts, and refine discipline-specific AIAS applications to guide higher education in the AI-driven future.