AI-Supported Learning in Low-Resource African Education Contexts: A Hybrid Ethical Leadership and Adaptive Learning Model for Improving Foundational Learning Outcomes

This working paper examines an AI-supported hybrid learning model designed to improve foundational learning outcomes in low-resource African education contexts. It is grounded in the AVELLS (Axton-Vent Ethical Leadership Learning System) framework, which integrates ethical leadership education, foundational literacy development, learner wellbeing support, and adaptive learning approaches.

The paper explores how responsible and context-sensitive AI tools can be used to enhance teaching and learning in environments with limited infrastructure, inconsistent educational resources, and high learner vulnerability. It proposes a hybrid model that combines teacher-led instruction, community-based learning support, and AI-assisted educational reinforcement to strengthen learner engagement and continuity.

The framework emphasizes ethical leadership development as a core pedagogical pillar, ensuring that technology use in education remains human-centered, inclusive, and aligned with child safeguarding and ethical AI principles. It further incorporates adaptive learning strategies aimed at improving literacy acquisition, communication skills, and learner participation.

The study is based on implementation-informed observations and programme design analysis from early-stage deployments in underserved educational settings. A mixed-methods approach is proposed for evaluating outcomes, including baseline and continuous assessment of literacy progression, learner engagement, ethical reasoning development, and wellbeing indicators.

Findings suggest that AI-supported hybrid learning, when properly contextualized and ethically governed, can improve access to learning support, enhance foundational literacy outcomes, and strengthen learner motivation in underserved African communities. The model is scalable and adaptable across diverse African education systems.

This work contributes to the fields of AI in education, African education development, and ethical learning system design by presenting a practical, low-resource adaptive model for inclusive and scalable learning innovation.