Integrating Universal Design for Learning, Challenge-Based Learning, and Generative AI in Teacher Education: A Participatory Action Research Study
TL;DR
The MIIA-R supported feasible proposals but not consistently deep inclusive, AI-mediated pedagogical integration, and Dunn tests showed that multimodal integrated use differed from generative-only and no or minimal use, but not from guided instrumental use.
Abstract
Teacher education incorporates generative artificial intelligence (GenAI) without developed inclusive pedagogical frameworks. This study contextually evaluates the Integrated Inclusive AI Challenge Model (MIIA-R). A concurrent mixed-methods design based on Participatory Action Research comprised three phases in a postgraduate programme and examined participation, engagement, and pedagogical creativity in designing inclusive GenAI-mediated proposals. The sample included 83 students, 80 proposals, and 178 learning artefacts. An independent rater blinded to the hypotheses re-evaluated a stratified subsample (ICC = 0.84; weighted κ = 0.74–0.78). Although the overall rubric score exceeded the functional threshold (M = 3.29), this reflected proposal feasibility (M = 4.18) and curricular coherence (M = 3.62), while UDL integration (M = 2.88) and pedagogical use of GenAI (M = 2.39) remained below it; therefore, Hypothesis 1 received partial and limited support. Pedagogical creativity followed a non-linear trajectory and correlated with GenAI tool diversity in the final phase (ρ = 0.527, p < 0.001). Profile differences were significant, H(3) = 18.34, p < 0.001, ε2 = 0.37; Dunn tests showed that multimodal integrated use differed from generative-only and no or minimal use, but not from guided instrumental use. The MIIA-R supported feasible proposals but not consistently deep inclusive, AI-mediated pedagogical integration.