The AI–UDL nexus in teacher education: designing inclusive learning for student variability

Abstract

The modern higher education classroom is incredibly diverse, with students displaying various strengths, challenges, prior knowledge, and learning preferences. Traditional “one-size-fits-all” teaching methods often fail to meet different learners' needs and hinder student engagement and academic achievement. To address this, Universal Design for Learning (UDL), a research-based, learner-focused approach grounded in cognitive neuroscience, provides a proactive path to design more inclusive and accessible teaching practices. Employing a quasi-experimental design, this study examined written lesson-planning performance among 45 Moroccan pre-service English as a Foreign Language (EFL) teachers across three conditions: a control group (CG), a UDL-only training group (TG1), and a UDL training supplemented with AI tools group (TG2). Analysis of the 38 participants with matched pretest and posttest scores indicated an adjusted between-group difference in posttest lesson-plan scores. After controlling for pretest scores, TG2 had higher adjusted posttest scores than both CG and TG1, whereas TG1 did not differ significantly from CG. The TG2-versus-TG1 comparison was sensitive to the estimation method and is interpreted cautiously. The findings indicate an adjusted difference associated with the UDL + AI training package as delivered. Because the UDL + AI condition also included additional training time, demonstrations, guided prompts, practice, and feedback, the design cannot isolate the contribution of AI tools alone. The evidence is limited to rubric-scored lesson plans in a single Moroccan institution, and a larger, time-matched study is needed to examine which components of the training account for the observed difference.