Teaching effectiveness in science classrooms has remained inconsistent due to limitations in traditional evaluation methods, thereby necessitating the integration of AI-based evaluation tools to enhance instructional quality. This study examined the effectiveness of AI-based evaluation tools in improving teaching effectiveness in science classrooms in Lagos State, Nigeria. The study adopted a quantitative explanatory design using the AI-Based Evaluation Tools and Teaching Effectiveness Questionnaire (AIETTEQ). The instrument was validated through expert review and pilot testing, while reliability was established using Cronbach’s alpha coefficient of 0.87. A sample size of 300 science teachers was selected through multistage sampling techniques comprising stratified and simple random sampling. Findings revealed that AI-based evaluation tools significantly improved feedback quality (β = 0.71, p < 0.05), enhanced instructional adaptability (β = 0.71, p < 0.05), and increased overall teaching effectiveness (β = 0.70, p < 0.05), leading to the rejection of all null hypotheses. These results indicate that AI tools facilitate timely feedback, support adaptive teaching, and improve learning outcomes. The study concluded that AI-based evaluation tools are critical drivers of effective teaching in science classrooms. It is recommended that educational institutions integrate AI tools into instructional systems and provide continuous teacher training to maximise their potential.