AI-Driven Teacher Performance Evaluation in Edo Secondary Schools: A Quantitative Assessment

Abstract

Since 2018, Edo State’s basic and secondary education system has operated one of Nigeria’s most technologically instrumented teacher-management infrastructures under the EdoBEST programme, including government-issued tablets used to record teacher attendance and support data-driven school management. This study surveyed 200 secondary school teachers across Edo State to assess readiness, perceptions, and anticipated challenges regarding AI-driven performance evaluation. Results indicate moderate overall readiness (grand mean = 3.21 on a 5-point scale). Perceived usefulness (M = 3.48) exceeded perceived ease of use (M = 3.12), while trust in AI-generated data relative to human observation remained low (M = 2.67). Teachers anticipated benefits of greater objectivity (M = 3.55) but expressed substantial concern about context-blindness (M = 4.02) and data privacy (M = 3.89). Principal anticipated challenges were lack of training (76%), inadequate ICT infrastructure (68%), concerns about punitive use of data (61%), and absence of clear institutional policy (54%). Chi-square analysis confirmed a significant association between perceived ease of use/usefulness and overall readiness (χ² = 24.31, p < .001), supporting Technology Acceptance Model predictions. An independent-samples t-test showed significantly higher readiness among teachers with high prior EdoBEST tablet usage (t = 3.87, p < .001). The findings suggest that AI-driven evaluation is feasible only after addressing existing EdoBEST underutilisation, infrastructure gaps, and trust and policy safeguards.