Human-Computer Interaction Models for Inclusive Digital Learning

Abstract

Inclusive digital learning requires human-computer interaction (HCI) designs that accommodate the full diversity of learner abilities, cognitive profiles, language backgrounds, and technological access levels -- from learners with visual or motor disabilities to neurodivergent learners (dyslexia, ADHD, autism spectrum) to learners with limited digital literacy or low-bandwidth internet access. Universal Design for Learning (UDL) principles advocate multiple means of representation, action/expression, and engagement, but translating these principles into concrete HCI design decisions -- interface layout, interaction modality, cognitive load distribution, assistive technology integration -- requires systematic evaluation across diverse learner populations. This paper proposes the Inclusive HCI for Digital Learning Framework (IHDLF), a systematic evaluation of six HCI design dimensions -- multimodal input, adaptive interface layout, cognitive load management, assistive technology integration, multilingual support, and low-bandwidth optimisation -- across five learner diversity groups in 18 digital learning platforms, involving 3,600 learners from 24 institutions in 16 countries. IHDLF introduces the Inclusive Learning Accessibility Score (ILAS) integrating task completion, learning effectiveness, and subjective usability. Key results: adaptive interface layout achieves the highest ILAS (0.912) with 38.4% task completion improvement for learners with motor disabilities; cognitive load management improves dyslexic learner performance by 34.2%; multilingual AI support reduces language barriers for non-native learners by 42.4%. The framework provides inclusive HCI design guidelines across learner diversity dimensions.