Artificial Intelligence and Instructional Practices of Technology Education Teachers in Nigerian Universities

This study focused on ways Artificial Intelligence (AI) could enhance the instructional practices of Technology Education teachers in Nigerian universities. Specifically, it determined ways AI influence: instructional planning; classroom instructional delivery; facilitation of practical; laboratory and workshop-based instruction; and assessment, feedback, and monitoring of students’ skill acquisition. It tested four null hypotheses. The Survey research design was adopted. The study was conducted in 13 federal universities in Nigeria offering Technology Education programmes. Population comprised of 456 Technology Education teachers (295 lecturers and 161 technologists). Data were collected using questionnaire and a semi-structured interview guide. Data were analysed using mean, standard deviation, and t-test, while qualitative data were summarized using thematic analysis. Findings
reveal 11 ways AI could enhance instructional planning (Cluster X̅g= 3.61), classroom instructional delivery (X̅g = 3.53), practical, laboratory and workshop-based instruction (X̅g = 3.30), and assessment, feedback, and monitoring of students’ skill
acquisition (x̄ = 3.72). Further, there are no significant differences between the mean ratings of lecturers and technologists across all instructional practice areas at 0.05 level of significance. Qualitative findings corroborated these results, indicating that AI could enhance lesson preparation, instructional delivery, practical demonstrations, assessment efficiency, and student monitoring.