AI-embedded robotic learning in inclusive classrooms: analysis of adaptive scaffolding effects on mathematical reasoning

Inclusive classrooms demand learning systems that accommodate diverse student cognitive profiles while supporting the development of high-level mathematical reasoning. However, empirical evidence on the integration of artificial intelligence (AI), educational robotics, and adaptive scaffolding in inclusive mathematics learning remains limited. This study aims to analyse the effects of an AI-based robotic learning environment with adaptive scaffolding on improving students’ mathematical reasoning abilities in inclusive classrooms. The study used a quasi-experimental design with a mixed-methods approach involving 47 slow learners in an inclusive junior high school. The intervention consisted of a hybrid physical-digital learning environment that combined a line-follower robot, contextual dioramas, an interactive tablet application, and an AI system for real-time assessment and tiered assistance, including problem interpretation, procedural guidance, and symbolic support. The results showed a significant increase in students’ mathematical reasoning abilities, with the average score increasing from 47.96 to 60.66 ( p < 0.001) and a very large effect size (Cohen's d = 1.58). Improvements were observed in most slow-learning students, demonstrating the system's potential to support adaptive differentiation in inclusive learning. These findings suggest that integrating tangible robotics with AI-based adaptive scaffolding can serve as an effective cognitive support system to improve students’ mathematical reasoning. This research contributes to the development of an AI-based robotic learning environment model that supports deep learning and pedagogical differentiation in inclusive mathematics education.