Building the Global Teacher Workforce Using a Motivation-Based Approach

Objetivo

Teacher shortages now constitute a global crisis. UNESCO projects a need for 44 million new teachers in the next decade, and the OECD flags teacher recruitment as an urgent priority. The consequences of shortages are stark: reduced educational opportunity for children and deepening social inequality. Recruitment approaches dominated by extrinsic incentives and generic messaging have delivered limited gains, with interest in teaching at historic lows. TEACHFORCE treats teacher recruitment as a motivational, technological, and developmental challenge and integrates (a) a predictive motivation-matching framework that links motivational profiles to message responsiveness; (b) interactive, AI-personalised simulations that adapt messages in real time; and (c) a cross-national, developmental model mapping motivation from ages 16–24 to pinpoint the optimal timing for recruitment interventions. We will test these components through experimental vignette studies, large-scale field trials of simulation technologies in England, Germany, and Hong Kong, and multi-wave longitudinal cohort studies tracking motivation and career decision-making behaviours. By combining motivation science, digital innovation, and developmental research, TEACHFORCE will deliver a scalable, evidence-based model of when, how, and for whom recruitment strategies work, giving education systems a validated framework to increase high-fit applications to teacher education.

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