The rapid proliferation of Generative Artificial Intelligence (GenAI) technologies in K–12 educational environments has generated significant pedagogical, ethical, and governance challenges. While policy advisories from organizations like UNESCO and national education ministries have emerged globally, empirical evidence supporting scalable institutional governance frameworks remains limited, particularly for large-scale implementations. This study develops and validates a Responsible Generative AI Adoption Framework (RGAIF) implemented across 2,000 students (Grades VI–XII) and 120 educators over a full academic year (2024–2025) at Lancers Convent Senior Secondary School. Employing a quasi-experimental stratified sampling design with control (n=850) and intervention (n=1,150) groups, the study evaluates four key outcomes: AI dependency behavior, higher-order cognitive engagement, academic integrity compliance, and AI literacy competency. Instruments included the AI Dependency Index (ADI, a=0.86), Higher-Order Cognitive Engagement Score (HOCES, a=0.83), Academic Integrity Compliance Rate (AICR), and AI Literacy Competency Scale (AILCS). Statistical analyses-one-way ANOVA, ANCOVA with baseline covariates, hierarchical multiple regression, and Cohen’s d effect sizes-demonstrate statistically significant improvements: responsible AI behavior (F(1,1998)=28.64, p<0.001, ?²=0.21), cognitive engagement (F(1,1998)=24.11, p<0.001, ?²=0.19), and integrity compliance (F(1,1998)=31.07, p<0.001, ?²=0.23). The hierarchical regression model explains 52% of variance in responsible AI engagement (R²=0.52, ?R²=0.23 for RGAIF). Effect sizes range from moderate to large (Cohen’s d=0.72–0.89). Findings confirm that layered governance-integrating institutional policy, technical safeguards, and pedagogical AI literacy-significantly outperforms prohibition (e.g., temporary bans) or unregulated adoption strategies. Compared to smaller studies (n<500), this research provides robust external validity. The RGAIF offers a scalable blueprint for K–12 ecosystems worldwide, contributing empirically grounded insights to AI governance in education.
Generative AI, Responsible AI, K–12 Governance, AI Literacy, Human-in-the-Loop Systems, Educational Policy, Large-Scale Validation, Cognitive Offloading, Academic Integrity.
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