Evaluating human–AI collaborative pedagogical model for sustainable skill development in technical education workshops

The accelerating integration of artificial intelligence (AI) into educational environments has opened new possibilities for enhancing technical skill development, particularly in hands-on workshop-based settings. This study evaluated a Human–AI Collaborative Pedagogical Model designed to promote sustainable skill development among university-level building and woodwork technology education students in Nigeria. Employing a quasi-experimental design with a mixed-methods approach, the research recruited 120 participants drawn from four universities in South East Nigeria. Students were assigned to either a traditional instruction group or an artificial intelligence-supported instruction group over a six-week practical module. Mixed quantitative and qualitative data were collected through validated performance instruments and analyzed using inferential statistics and thematic analysis, respectively. Results demonstrated that students in the artificial intelligence-supported group achieved significantly higher task accuracy, reduced task completion time, lower error frequency, and superior skill transferability compared to their traditionally-instructed counterparts. Thematic analysis further identified the Scaffolded Human–AI Co-instruction Model, characterized by real-time artificial intelligence feedback, instructor mediation, and iterative experiential cycles, as the most effective pedagogical framework for workshop-based technical education. These findings inform sustainable digital transformation in TVET by highlighting how human-centered AI integration can improve skill outcomes while guiding instructor development, implementation governance, and equitable scaling in resource-constrained contexts.