Teacher Monitoring Strategies for AI-Aided Student Writing Outputs Toward Policy Guidelines

This study examined the extent to which senior high school teachers monitor AI-generated written outputs, focusing on the use of AI detectors, manual review, and cross-checking results. The study respondents were teachers from the Humanities and Social Sciences (HUMSS) strand of Philippine Christian University-Dasmariñas. The researchers collected data through a structured questionnaire and analyzed it using descriptive statistics, including mean and standard deviation. Results showed that teachers used AI detectors and cross-checking to a moderate extent, while they used manual review to a high extent and found it significant. Teachers faced challenges such as limitations in AI detection tools and time constraints in manual evaluation and cross-checking. These findings highlight the importance of a layered monitoring approach, where teacher judgment remains central, and AI tools serve as supplementary aids. The study provides evidence for developing clear guidelines and practices that balance ethical assessment, professional discretion, and responsible integration of AI in student writing evaluation. Keywords: Artificial intelligence, AI-generated text, teacher monitoring strategies, manual review, cross-checking