Integrating ADDIE, Backward Design, and AI to Develop Curriculum Design Competencies in Preservice Special Education Teachers

다국어 초록 (Multilingual Abstract)

The rise of generative AI has intensified the theory-practice gap in the curriculum design for preservice teacher training. A mixed-methods case study was conducted with 25 undergraduate special education majors using an integrated approach of ADDIE, Backward Design, and AI tools. Over the course of a semester, participants produced digital textbooks for elementary school students with intellectual disabilities. Quantitative analysis using paired-samples t-tests revealed statistically significant increases in self-perceived competence across all measured domains: lesson planning construction (M=2.72, SD=0.843 to M=4.24, SD=0.663; d=1.401), systematic lesson design (M=2.64, SD=0.757 to M=4.24, SD=0.723; d=1.848), and perceived competence (M=2.88, SD=0.833 to M=3.80, SD=0.764; d=0.775), all with large to very large effect sizes. Qualitative analysis revealed a core pedagogical change. Participants shifted from planning lessons around activities to systematically designing lessons where objectives, assessments, and activities aligned with students' needs. Participants identified AI primarily as a tool for collaborative ideation and improving efficiency. While 84% utilized AI, critical limitations emerged regarding pedagogical control and contextual understanding. There were no significant differences between groups in terms of gender and prior experience (all p>.05). The integrated model effectively cultivated competencies regardless of starting point. When ADDIE guides the process and Backward Design ensures pedagogical alignment, AI enables rapid development. Through this integration, preservice teachers can expand their design skills and professional identity. Preservice teacher programs should integrate scaffolded project-based experiences in which AI is used to support pedagogical discernment rather than replacing design thinking.