Artificial Intelligence as a Scaffolding Tool for Self-Directed Learning in ODeL Environments: An Instrumental Case Study of Zimbabwe Open University

Abstract

Artificial Intelligence (AI) is increasingly integrated into Open and Distance eLearning (ODeL) environments, yet limited empirical research explains how AI functions as a scaffolding mechanism to support self-directed learning (SDL), particularly in developing country contexts. This qualitative instrumental case study examined how AI tools are perceived and utilised to scaffold self-directed learning at Zimbabwe Open University. Data were generated through semi-structured interviews with lecturers and a quality assurance coordinator and focus group discussions with students who had experience in using AI-supported learning tools. Data were analysed using Braun and Clarke's six-phase thematic analysis. Findings indicate that participants perceived AI as supporting self-directed learning through personalised learning support, instant feedback and data-driven reflection and goal setting. While the study relies on participant perceptions rather than direct observation of AI system behaviour, it provides context-sensitive insights into how AI may function as a pedagogical scaffold in resource-constrained ODeL environments. These mechanisms function as digital scaffolds within learners' Zone of Proximal Development, enabling autonomy, sustained engagement and self-regulation in ODeL contexts. The study contributes a context-sensitive AI-enabled scaffolding framework for SDL in resource-constrained ODeL environments and highlights implications for policy, practice, and future mixed methods research.