Bridging potential and achievement: the AI-enhanced Latin American model of talent development

The persistent gap between individual potential and realized achievement remains a central challenge in gifted education and talent development, particularly when strengths are unrecognized or opportunities are constrained. This Hypothesis and Theory article introduces the AI-Enhanced Latin American Model of Talent Development (AI-LAMTD), an ecological and equity-centered framework that examines underachievement through dynamic potential-opportunity mismatch. Potential is a provisional inference about further domain development under specified conditions; giftedness or high ability describes unusually advanced capacity relative to an explicit reference context; competence comprises acquired domain knowledge and skills; talent is outstanding developed domain competence; and achievement is an observable accomplishment at a particular time. These constructs serve different explanatory and measurement roles. The framework retains four interacting ecological levels-individual development, learning contexts, family and community, and policy and infrastructure-within the cross-cutting principles of equity and capability expansion and cultural relevance and inclusion. Five developmental functions organize possible AI support: recognition of emerging potential, opportunity design, engagement and feedback, developmental monitoring, and support for well-being and flourishing. Recognition, opportunity conversion, engagement, learning and practice, and independent transfer are recurrent conversion processes through which domain competence may develop into talent. Potential is reassessed throughout development, while achievement, agency, well-being, and contribution are evaluated over time; flourishing is not an automatic consequence of advanced competence. Ten falsifiable propositions specify relationships, boundary conditions, comparisons, and outcomes concerning recognition, opportunity conversion, independent development, and equity. Evidence from general AI in education informs mechanism plausibility and is distinguished from the emerging evidence specific to gifted and high-ability learners. AI is neither necessary nor sufficient for talent development. Its developmental value depends on accessible opportunity, learner agency, well-being, and competence that persists beyond the focal technological assistance.