AI-ENABLED PLATFORMS AND THE TRANSFORMATION OF HOME-SCHOOL COLLABORATION: GOVERNANCE MECHANISMS FOR TEACHER-PARENT WORKLOAD REDISTRIBUTION & PEDAGOGICAL EVOLUTION

The rapid digital transformation of education and widespread adoption of AI technologies have reshaped home-school collaboration, presenting both opportunities and challenges. While AI-enabled platforms promise to enhance educational equity and quality, they also exacerbate role conflicts and workload burdens for teachers and parents. Traditional collaboration models struggle to adapt to these technological shifts, necessitating systematic research on governance mechanisms and pedagogical innovations. This study aims to identify how AI platforms reconfigure home-school collaboration, optimize workload redistribution, and drive the evolution of teaching methodologies. The research employs a mixed-methods design, combining quantitative analysis of public datasets on adaptive learning (e.g., Kaggle’s Personalized Learning dataset) with qualitative interviews involving teachers and parents. Regression analysis and structural equation modeling (SEM) quantify the impact of AI platforms, while content analysis deciphers stakeholders’ lived experiences. Findings reveal three key pathways: AI mitigates administrative burdens through automated workflows, redefines parental engagement via data transparency, and fosters pedagogical shifts toward personalized learning. However, disparities in digital literacy and ethical risks emerge as critical barriers. The platformization of education further necessitates examining longitudinal effects on stakeholder wellbeing, particularly how continuous connectivity requirements reshape traditional boundaries between professional and domestic spheres. Emerging evidence suggests these boundary permeations differentially impact working parents versus single-income households, creating new dimensions of digital inequality that transcend access-based divides. The study contributes theoretically by integrating socio-technical systems theory with educational governance frameworks. Practically, it offers actionable insights for platform designers and policymakers to balance efficiency with equity. By bridging the gap between technology and pedagogy, this research underscores the need for holistic governance to harness AI’s transformative potential in education.