Beyond Formal Education: Building AI Capacity among Community Learning and Development Staff in LMICs

Abstract Book of the 11th International Conference on Research in Teaching and Education

Year: 2026

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Beyond Formal Education: Building AI Capacity among Community Learning and Development Staff in LMICs

Dr. Nazim Ali

ABSTRACT:

In Low- and Middle-Income Countries (LMICs), many underserved groups have limited access to formal learning opportunities. For these communities, community learning and development (CLD) programmes play a vital role in SDG progress, including widening access to education. AI can support the enhancement of education and training however, there is limited research on AI use within CLD settings. This creates an important gap, as CLD staff roles and programme priorities differ from those in formal education institutions. If this gap is not addressed, AI developments may widen digital inequalities by overlooking the staff and communities who rely on community-based learning programmes. This paper examines AI readiness and capacity-building needs following a TNE-supported training programme that introduced CLD staff to AI tools. Our findings show that, following this introductory AI training, staff are already beginning to use AI to support their roles. Furthermore, staff felt AI could help reduce workload, support communication and assist with the development of resources. However, they also recognised the need for further training, particularly in complying with responsible use, ensuring AI-generated outputs are accurate and adapting content appropriately to local contexts. These findings suggest that AI has potential value within CLD settings, but its effective use depends on staff training, access to appropriate tools, and clear guidance. The paper discusses the implications for transnational educator training and offers recommendations for integrating AI literacy into CLD professional development.

Keywords: AI; Education; Inequality; Readiness; Training