Environmental, Social, and Governance (ESG) reporting has become a critical component of corporate sustainability, governing compliance, and stakeholder decision-making. However, ESG education and reporting practices face challenges such as fragmented standards, data complexity, inconsistent disclosure quality, and limited expertise across organizations. This paper proposes an AI-driven education system designed to enhance ESG reporting knowledge, skills, and consistency through intelligent learning and decision-support mechanisms. The system integrates natural language processing, machine learning, and knowledge-graph technologies to analyze ESG frameworks, regulatory guidelines, and corporate disclosures, providing adaptive educational content and real-time reporting assistance. By offering personalized learning pathways, automated gap analysis, and explainable AI-based feedback, the platform supports learners and practitioners in understanding ESG concepts, aligning reports with global standards, and improving transparency and accuracy. The proposed approach aims to reduce reporting inconsistencies, accelerate ESG competency development, and support sustainable decision-making while positioning AI as an assistive tool that complements human judgment and ethical oversight in ESG practices.
Deshpande et al. (2026) studied this question.