article · Education Sciences
Integrating artificial intelligence into teacher education within resource-constrained contexts requires comprehensive institutional preparedness rather than mere technology adoption. An investigation across six university campuses in Namibia examined educators' perspectives on embedding artificial intelligence into teacher training. The findings demonstrate that effective integration depends on coordinated curriculum transformation, pedagogical redesign, and institutional readiness, particularly regarding digital infrastructure, faculty capacity, and curriculum alignment. Key pathways identified include incorporating artificial intelligence competencies across entire curricula, embedding artificial intelligence into specific courses, adopting simulation-based learning, and supporting online teaching environments. While educators recognise benefits such as improved instructional efficiency, enhanced teaching capacity, and greater learner autonomy, they also note risks surrounding overreliance on digital tools. Successfully navigating these dynamics requires aligning institutional strategies with individual educator readiness to ensure sustainable adoption in developing higher education environments.
Frameworks for adopting artificial intelligence in education often assume advanced resources that developing regions lack. By identifying specific curriculum pathways and readiness challenges in resource-constrained teacher education, this work helps education leaders and policymakers design realistic implementation strategies. It ensures educators receive relevant training to prepare future teachers without creating unsustainable technological dependencies.
This work provides an early-stage institutional framework rather than a commercial product. The insights can guide educational technology developers, university administrators, and curriculum planners in designing tailored training programmes, simulation-based learning tools, and institutional deployment strategies for resource-constrained teacher education environments. While applied directly within university programmes, commercial adoption remains at an early conceptual stage focused on curriculum design and policy development.
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Despite growing global interest in Artificial Intelligence (AI) in education, limited empirical research has examined how teacher education institutions in developing-country and resource-constrained contexts can sustainably integrate AI into their curricula. This gap is particularly significant because many existing AI integration frameworks assume levels of technological infrastructure, institutional capacity, and digital readiness that may not reflect the realities of higher education institutions in the Global South. Against this backdrop, this study explored institutional readiness, curriculum integration pathways, and implementation strategies for Artificial Intelligence within teacher education programmes in Namibia. Guided by an interpretivist paradigm, a qualitative exploratory design was employed, using semi-structured interviews with 25 teacher educators across six university campuses. Data were analysed thematically using Braun and Clarke’s framework. The findings indicate that AI integration extends beyond technological adoption and requires coordinated curriculum transformation, institutional preparedness, and pedagogical redesign. Key integration pathways include embedding AI within faculty courses, curriculum-wide integration of AI competencies, simulation-based learning, and support for online teaching environments. Institutional readiness, particularly in terms of infrastructure, faculty capacity, and curriculum alignment, emerged as a critical determinant of implementation. While participants highlighted benefits such as improved instructional efficiency, enhanced teacher capacity, and increased learner autonomy, they also expressed concerns regarding overreliance on technology. By integrating Diffusion of Innovation (DOI) and the Technology Acceptance Model (TAM), this study advances a multi-level framework linking institutional and individual dimensions of AI adoption in teacher education. This study contributes context-specific insights to AI curriculum transformation in the Global South and provides practical implications for curriculum design, institutional strategy, and policy development.
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DOI: 10.3390/educsci16091417
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