Work Control, Artificial Intelligence Integration and Teachers' Job Autonomy in Public Senior Secondary Schools in Yenagoa Local Government Area of Bayelsa State

Abstract The study examined work control, artificial intelligence integration and teachers' job autonomy in public senior secondary schools in Bayelsa State. Two research questions and corresponding hypotheses guided the study. The study employed a correlational design and focused on 1,208 teachers from 44 public senior secondary schools in Yenagoa Local Government Area of Bayelsa State. A sample size of 242 was used, representing 20% of the population, determined through proportionate sampling. The study used three questionnaires, the Work Control Questionnaire (WCQ), Artificial Intelligence Integration Questionnaire (AIIQ) and the Teachers' Job Autonomy Questionnaire (TJQ), to collect data. All three questionnaires utilised a five-point Likert scale ranging from "Very Satisfied" (5) to "Very Dissatisfied" (1). The questionnaires were validated, and their reliability was tested using Cronbach's Alpha, yielding a result of .782, indicating the instrument's reliability. The data were analysed using inferential statistics of Pearson Product-Moment Correlation Coefficient to answer the research question and test the hypotheses at a 0.05 level of significance, utilising the Statistical Package for Social Sciences (SPSS) version 25.0. Meanwhile, a total of 242 were distributed and 204 were retrieved, valid and used for the analysis. The study revealed that work control and artificial intelligence integration significantly influence teachers' autonomy in instructional design, delivery, and assessment practices in public senior secondary schools in Bayelsa State. Given the strong relationship between work control, AI integration, and teacher autonomy, it is recommended that educational policymakers promote teacher autonomy through supportive structures while integrating AI-powered tools with adequate training, enabling responsive instruction, efficient assessment, and data-driven decisions to enhance teaching quality and student outcomes.