An Artificial Intelligence (AI)-Powered Voice-Based Intelligent Learning System for Visually Impaired Students

The rapid expansion of digital learning platforms has created unprecedented educational opportunities; however, the majority of these platforms remain inaccessible to the estimated 253 million visually impaired individuals worldwide, particularly students in inclusive K-12 settings. Existing assistive technologies such as screen readers and Braille displays function as access tools rather than pedagogically designed learning environments, leaving a critical gap in inclusive educational technology. This paper presents BlindLearn, an AI-powered, voice-based learning framework developed and evaluated using Design Science Research (DSR) methodology. Grounded in Universal Design for Learning (UDL) and Cognitive Load Theory (CLT), BlindLearn introduces the Voice-First Pedagogical Model (VFPM) — a novel five-stage learning cycle (Audio Activation, Narrative Input, Conversational Elaboration, Voice Practice, Adaptive Feedback) designed for auditory-primary learners. The framework was developed through systematic literature review (47 papers, 2015–2024), structured needs analysis (n = 23), multi-expert validation using Content Validity Ratio (CVR, n = 8), and usability evaluation using the System Usability Scale (SUS, n = 15). Expert validation yielded a mean CVR of 0.89 (p < .05), and usability evaluation produced a mean SUS score of 84.3 (Grade: Excellent). Three original artifacts are contributed: the VFPM theoretical model, a validated four-layer AI system architecture, and twelve evidence-based inclusive design guidelines, advancing the fields of educational technology and inclusive AI system design.