Teacher Readiness for Artificial Intelligence Integration in Higher Education

Abstract

Background: Artificial intelligence (AI) is entering school teaching through lesson planning, resource creation, differentiated support, feedback, and assessment. Responsible use depends on teachers who can check outputs, protect data, maintain student thinking, and follow clear school rules. Purpose: This study develops a school-focused model of teacher readiness and tests whether a clear 28-item questionnaire and a simple analysis plan work together. Methodology: A reproducible synthetic dataset of 500 fictional primary- and secondary-school teacher profiles was generated with a fixed random seed. No human participants or real schools were involved. Six five-point Likert scales measured AI-related digital competence, perceived usefulness, school support, ethical concern, teacher readiness, and responsible classroom AI integration. Reliability, basic construct validity, correlations, and two multiple regression models were examined. Key findings: All six scales showed satisfactory reliability (Cronbach's alpha = .872-.907) and met the stated basic validity checks. Digital competence (standardized beta = .302), perceived usefulness (.276), and school support (.241) were positively related to readiness, while ethical concern was negatively related (-.155). Readiness was the strongest predictor of responsible classroom integration (.431). Usefulness and school support also had positive direct relationships, while ethical concern had a negative relationship. The models explained 41.9% of readiness and 49.8% of integration in the generated dataset. Practical implications: Schools should combine subject-based AI training, approved tools, clear policy, teaching support, and practical safeguards. The numerical results are a reproducible benchmark, not estimates of real teachers.