SMART-OU: Smart Model for AI-Enabled Remote Teaching in Open Universities

Abstract

Open Universities (OUs) are characterized by large-scale distance education, diverse learner profiles, and flexible temporal arrangements; however, remote teaching still suffers from low engagement, delayed feedback, limited personalization, heavy teaching loads, and other inequities. This concern has led to the design of an OU-specific smart model for AI-enabled Remote Teaching (SMART-OU) based on a progressive approach to teaching and learning, in which instruction, practice, feedback, and assessment are tailored to support small, incremental mastery. SMART-OU uses learning analytics, AI-enabled content scaffolding, formative assessment, and ethical design and implementation as its foundational pillars. The model prescribes and delineates a learner journey along the stages of the learning process: diagnose, scaffold, practice, assess, support, and an instructor’s journey through the design, monitoring, intervention, and improvement stages, all supported by AI tools: adaptive sequencing, automated feedback, risk prediction, and learning assistant chat. This paper outlines the model’s key elements, data flow, evaluation, resource allocation, and implementation for OUs and other institutions of higher education. The model aims to optimize retention and provide timely intervention for at-risk learners, achieve greater consistency across diverse cohorts, and significantly reduce administrative workload, all while safeguarding the core values of academic integrity, inclusiveness, and transparency.