A Multidimentional Approach to Digital Inclusion: What Is the Impact of a UDL-Oriented ITS on Global Affect and Self-Efficacy?

This study investigates the effectiveness of a Universal Design for Learning (UDL) - oriented Intelligent Tutoring System (ITS) in comparison to a generic chatbot within a digital learning context. The focus is on how these systems influence users’ global affect (emotional experience) and self-efficacy, with consideration given to users’ digital competencies. The study employed a case/control experimental design with independent samples, involving 99 participants divided into the ITS group (n=44) and chatbot group (n=55). Two digital learning tools were developed for the study: a UDL-oriented ITS and a generic chatbot. Both systems leveraged recent natural language processing tools but differed in sophistication and adaptivity [3]. The ITS featured a modular structure (Student, Domain, Instruction Modules), designed to operationalize UDL principles and offer adaptive support. It incorporated customizable interfaces, language models 252 for tailored explanations, and machine learning for dynamic content adaptation. The system ensured assistive technology compatibility and device-agnostic access [4-5]. In contrast, the chatbot provided standardized, non-adaptive interactions, acting as a control for evaluating the benefits of UDL-driven personalization. Its static architecture and lack of real-time adaptation highlighted any differential effects based on UDL implementation. Statistical analysis revealed that the ITS group experienced greater positive changes in emotional experience (global affect) compared to the control group, demonstrating the impact of adaptive, UDL-based design. Both systems received high usability ratings, but only the ITS showed differentiated effects in emotional response, particularly among those with lower digital competency. The results have significant implications for designing Intelligent Tutoring Systems (ITS) grounded in the Universal Design for Learning (UDL) framework. The observed interaction between users’ digital competencies and the effectiveness of the intervention underscores the importance of accounting for individual differences in technological proficiency.