Harnessing Artificial Intelligence for Capacity Building in Technical Teacher-Education: Empirical Insights from Anambra State, Nigeria

Abstract

This study examined the potential of artificial intelligence for capacity building in technical teacher education and the challenges affecting its integration in tertiary institutions in Anambra State, Nigeria. Two research questions guided this descriptive survey. The population comprised 105 technical educators from Nnamdi Azikiwe University, Awka, and the Federal College of Education (Technical), Umunze; no sampling was undertaken. Data were collected using a questionnaire containing 20 items. Three experts established content validity, while Cronbach alpha produced a reliability coefficient of 0.87. Mean and standard deviation answered the research questions, and Welch tests for independent samples compared institutional responses at the 0.05 significance level. Findings showed agreement on listed potentials and challenges. The grand means for AI potential were 3.41 at Nnamdi Azikiwe University and 3.32 at the Federal College, while the corresponding challenge means were 3.43 and 3.39. All item means exceeded the 2.50 criterion, but none of the institutional differences was statistically significant. Respondents particularly recognised personalised learning, current industry knowledge, funding constraints, inadequate infrastructure, pedagogical change, and data security concerns. The study concluded that AI can strengthen instruction, assessment, professional development, and practical trade training, but implementation depends on infrastructure, institutional support, secure policies, and sustained lecturer training. It recommended investment in AI resources, curriculum integration, digital literacy, and phased, supervised adoption of adaptive learning, automated grading, and virtual simulation tools. Keywords: Artificial Intelligence, Capacity Building, Technical, Teacher-Education, harnessing