MACHINE LEARNING IN NIGERIAN TERTIARY VOCATIONAL AND TECHNICAL EDUCATION: IMPERATIVES FOR INCLUSIVITY

The Fourth Industrial Revolution is redefining skill demands globally, yet Nigerian tertiary vocational and technical education (VTE) remains largely analogous, underfunded, and exclusionary. This paper argues that ML presents transformative potential for personalizing skill acquisition, predicting learner risk, and aligning VTE with labor market needs. However, adoption without deliberate inclusivity frameworks risks amplifying existing inequities across gender, disability, location, and socioeconomic status. Drawing on human capital theory and evidence from AI in education, this study posits that ML in Nigerian VTE must be anchored on four imperatives: equitable infrastructure, bias-audited algorithms, inclusive data, and teacher capacity. Unless these issues are addressed, ML will contradict national and global commitments to “education and work for all.” Policy recommendations and a framework for inclusive ML deployment are proposed.